Learning Guides

Learn machine learning with Python: a free curated curriculum

How to learn data science and deep learning in Python

I recently wrote a 80-page guide to how to get a programming job without a degree, curated from my experience helping students do just that at Springboard. This excerpt is a part where I focus on how to learn machine learning in Python.

How to learn machine learning in Python is a very popular topic: with the rise of artificial intelligence, programmers have been able to do everything from beating human masters at Go to replicating human-like speech. At the foundation of this fantastic technological advance are programming and statistics principles you can learn.

Here’s how to learn machine learning in Python:

Python Basics

learn machine learning

Before you learn how to run, you have to learn how to walk. Most people who start learning machine learning and deep learning come from a programming background: if you do, you can skip this section. However, if you’re new to programming or you’re new to Python, you’ll want to take a look through this section.

Codecademy for Python

Codecademy is an online platform for learning programming, with free interactive courses that encourage you to fully type out your code to solve simple programming problems.

Introduction to Python for Data Science

This interactive Python tutorial is created by Datacamp, and is more suited to introducing how Python basics work in the context of data science.

11 Great Resources to Learn and Work in Python

This list of resources will point you to great ways to immerse yourself in Python learning. It’s a broad list filled with different resources that will help you, no matter your learning style.

Installing Jupyter Notebook

These are instructions for installing Jupyter Notebook, an intuitive interface for Python code. You’ll have all of the important Python libraries you need pre-installed and you’ll be easily able to export out and show all of your work in an easy-to-visualize fashion. I strongly suggest that you use Jupyter as your default tool for Python, and the rest of this learning path assumes that you are.

Statistics Basics

learn machine learning

In order to learn machine learning in Python, you not only have to learn the programming behind it — you’ll also have to learn statistics. Here are some resources that can help you gain that fundamental knowledge.

Khan Academy, Math, and Statistics

Khan Academy is the largest source of free online education with an array of free video and online courses. This section on Khan Academy will teach you the basic statistics concepts you need to know to understand machine learning, deep learning and more — from mode, median, mean to probability concepts.

Probabilistic Programming & Bayesian Methods for Hackers

This book will delve into Bayesian methods and how to program with probabilities. Combined with your budding knowledge of Python, you’ll be quickly able to reason with different statistical concepts. It’s a book the author gave out for free — and its deeply interactive nature promises to engage you into these new concepts.

Pandas

learn machine learning

The main workhorse of data science in Python is the Pandas data science library, an open-source tool that allows for a tabular organization of large datasets and which contains a whole array of functions and tools that can help you with both data organization, manipulation, and visualization. In this section, you’ll be given the resources needed to learn Pandas which will help you to learn machine learning in Python.

Cooking with Pandas

Julia Evans, a programmer based in Montreal, has created this simple step-by-step tutorial on how to analyze data in Pandas using noise complaint and bike data. It starts with how to read CSV data into Pandas and goes through how to group data, clean it, and how to parse data.

Official Pandas Cookbook

The official Pandas cookbook involves a number of simple functions that can help you with different datasets and hypothetical transformations you might want to do on your data. Take a look and play with it to extend your knowledge of Pandas.

Data Exploration and Wrangling

learn machine learning

Before you can do anything with the data, you’ll want to explore it, and do what is called exploratory data analysis (EDA) — summarize your dataset and get different insights from it so you know where to dig deeper. Fortunately, tools like Pandas are built to give you relevant and surprisingly deep summary insights into your data, allowing you to shape which questions you want to explore next.

By looking through your dataset from afar, you’ll already be able to understand what faults the dataset might have that will keep you from completing your analysis: missing values, wrongly formatted data etc. This is where you can start processing and transforming the data into a form that you want to answer your questions. This is called “data wrangling” — you are cleaning the data and making sure that it is able to answer all of your questions in this step.

Python Exploratory Data Analysis with Pandas

This article from Datacamp goes through all of the nuts and bolts functions you need in order to take a slightly deeper look at your data. It covers topics ranging from summarization of data to understanding how to select certain rows of data. It also goes into basic data wrangling steps such as filling in null values. There are interactive embedded code workspaces so you can play with the code in the article while you are digesting its concepts.

A Comprehensive Introduction to Data Wrangling

This blog article from Springboard is filled with code examples that describe how you can filter data, detect and drop invalid/null values from your dataset, how to group data such that you can perform aggregated analyses on different groups of data (ex: doing an analysis of survival rate on the Titanic by gender or passenger class) and how to handle time series data in Python. Finally, you’ll learn how to export out all of your work in Python so that you and others can play around with it in different file formats such as the Excel-friendly CSV.

Pandas Cheat Sheet

This Pandas cheat sheet, hosted on Github, can be an easy, visual way to remember the Pandas functions most essential to data exploration and wrangling. Keep it as a handy reference as you go out and practice some more.

Data Visualization

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Data exploration and data visualization work together hand-in-hand. Learning how to visualize data in different plots can be important is seeing underlying trends.

Beginner’s Guide to Matplotlib

This legend of resources on the official matplotlib library (the workhorse library for Python data visualization) will help you understand the theory behind data visualization and how to build basic plots from your data.

Seaborn Python Tutorial

The Seaborn library allows people to create intuitive plots that the standard matplotlib library doesn’t cover easily: things like violin plots and box plots. Seaborn comes with very compelling graphics right out of the box.

Introduction to Machine Learning

learn machine learning

Machine learning is a set of programming techniques that allow computers to do work that can simulate or augment human cognition without the need to have all parameters or logic explicitly defined.

The following section will delve into how to use machine learning models to create powerful models that can help you do everything from translating human speech to machine code, to beating human grandmasters at complex games such as Go.

It’s important before we get started implementing ideas in code that you understand the fundamentals of machine learning. This section will help you understand how to test your machine learning models, and what statistics you should use to measure your performance. It is an essential cornerstone to your drive to learn machine learning in Python. 

A Visual Introduction to Machine Learning

This handy visualization will allow you to understand what machine learning is and the basic mechanisms behind it through a visual display of how machines can classify whether a home is in New York or in San Francisco.

Train/Test Split and Cross-Validation in Python

This article explains why you need to split your dataset into training and test sets and why you need to perform cross-validation in order to avoid either underfitting or overfitting your data. Does that seem like a lot of jargon to you? The article will define all of these different concepts, and show you how to implement them in code.

Sci-kit Learn

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Sci-kit learn is the workhorse of machine learning and deep learning in Python, a library that contains standard functions that help you map machine learning algorithms to datasets.

It also has a bunch of functions that will allow you to easily transform your data and split it into training and test sets — a critical part of machine learning. Finally, the library has many tools that can evaluate the performance of your machine learning models and allow you to choose the best for your data.

You’ll want to make sure you know how to effectively use the library if you want to learn machine learning in Python.

A Gentle Introduction to Scikit-Learn

This post introduces a lot of the history and context of the Sci-Kit Learn library and it gives you a list of resources and documentation you can pursue to further your learning and practice with this library.

Scikit-Learn Documentation

The official scikit-learn documentation is filled with resources and quick start guides that will help you get started with Scikit-Learn and which will help you entrench your learning.

Regression

learn machine learning

Regression involves a breakdown of how much movement in a trend can be explained by certain variables. You can think about it as plotting a Y or dependent variable versus a slew of X or explanatory variables and determining how much of the movement in Y is dependent on individuals factors of X, and how much is due to statistical noise.

There are two main types of regression that we’re going to talk about here: linear regression and logistic regression.  Linear regression measures the amount of variability in a dependent factor based on an explanatory factor: you might, for example, find out that poverty levels explain 40% of the variability in the crime rate. Logistic regression mathematically transforms a level of variability into a binary outcome. In that way, you might classify if a name is most likely to be either male or female. Instead of percentages, logistic regression produces categories.

You’ll want to study both types of regression so you can get the results you need.

Simple and Multiple Linear Regression in Python

This informative Medium piece goes into the theory and statistics behind linear regression, and then describes how to implement it in Sci-Kit Learn.

Building a Logistic Regression in Python, Step-by-Step

This Medium tutorial uses the Sci-Kit Learn tools available to implement a logistic regression model. The amount of detail in each step will help you follow along.

Clustering

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Another type of machine learning model is called clustering. This is where datasets are grouped into different categories of data points based on the proximity between one point and other groups of points. Mastering clustering is an important part of learning machine learning in Python. 

An Introduction to Clustering and different methods of clustering

Analytics Vidhya has presented this comprehensive introduction to clustering methods: it’s good to get a handle on this theory before you try implementing it in code.

Customer Segmentation using Python

This article from Yhat demonstrates how to do simple K-means clustering across different wine customers. It’ll take your learning in Pandas and Scikit-Learn and combine them into a useful clustering example.

Deep Learning/Neural Networks

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Neural networks are an attempt to simulate how the human mind works (on a very simplified level) in computational code. They have been a great advance in artificial intelligence — and while in some ways they are a black box of complex algorithms working in tandem to learn how data generalizes, their practical applications have exponentially multiplied in the last few years. Deep learning encompasses neural networks as well as other approaches meant to simulate human intelligence. They are an important part to learn if you want to learn machine learning in Python. 

In a huge breakthrough, Google’s AI beats a top player at the game of Go

This short Wired article isn’t a technical tutorial: it’s the recounting of an epic match between a human grandmaster at Go, a game that was supposed to be so complex for computers to win that technology to do so wasn’t supposed to come until around the 2030s. By leveraging the power of neural networks, Google was able to bring AI victory forward some two decades or so. This article should give you a great glimpse at the potential and power of neural networks.

A Beginner’s Guide to Neural Networks in Python and SciKit Learn 0.18

This example-laden tutorial uses the neural networks module in the Scikit-Learn library to build a simple neural network that can classify different types of wine. Follow along and play with the code so you can get a feel for how to build neural networks.

Develop Your First Neural Network in Python With Keras Step-By-Step

This tutorial from Machine Learning Mastery uses the Python implementation of the Keras library to build slightly more powerful and intricate neural networks. Keras is a code library built to optimize for speed when it came to experimenting with different deep learning models.

Big Data

learn machine learning

Big data involves a lot of volume and velocity of data. It’s an amount of data, measured in petabytes, that can’t be processed easily with tools like Pandas, which are based on the processing power of one laptop or computer.

You’ll want to scale out to controlling many processors and servers and passing data through a network to process data at scale. Tools that allow you to map and reduce data between multiple servers and others such as Spark and Hadoop play an important role here. It’s time to take the learning you’ve had before this and apply it to massive data sets! You can’t learn machine learning in Python without dealing with big data. 

Get Started With Pyspark and Jupyter Notebook in 3 Minutes

This blog post will help you get set up with PySpark, a Python library that brings the full power of Spark to you in the Jupyter Notebook format you’ve been used to working in. PySpark can be used to process large datasets that can go all the way to petabytes of data!

PySpark Video Tutorial

This video tutorial will help you get more context about PySpark and will provide sample code for tasks such as doing word counts over a large collection of documents.

Using Jupyter on Apache Spark: Step-by-Step with a Terabyte of Reddit Data

This tutorial from Insight goes a little further than installation instructions and gets you working with Spark on a terabyte (that’s 1024 gigabytes!) of Reddit comment data.

Machine Learning Evaluation

learn machine learning

Now that you’ve learned a baseline for all of the theory and code you need to learn machine learning in practice, it’s time to learn what metrics and approaches you can use to evaluate your machine learning models.  

Metrics to Evaluate Machine Learning Algorithms in Python

In this tutorial, you’ll learn about the different metrics used to evaluate the performance of different machine learning approaches. You’ll be able to implement them in Scikit-Learn and Jupyter right away!

Model evaluation, model selection, and algorithm selection in machine learning

This long six-part series (check the end of this blog post for more posts after) goes deep into the theory and math behind machine learning evaluation metrics. You’ll come out of the whole thing with a deeper knowledge of how to measure machine learning models and compare them against one another.

Suggested daily routine

Learning isn’t often a static thing. You need ongoing practice to master a skill. Here’s a suggested learning routine you can implement in your day to make sure you practice and expand your knowledge and learn machine learning in Python.

Here’s my suggested daily routine:

  1. Continue working on something in machine learning at all times
  2. Go to StackOverflow, ask and answer questions
  3. Read the latest machine learning papers, try to understand them
  4. Practice your code whenever you can by looking through Github machine learning repositories
  5. Do Kaggle competitions so you can extend your learning and practice new machine learning concepts

At the end, you’ll have effectively mastered how to learn machine learning in Python!

Want more material like this? Check out my guide on how to get a programming job without a degree.

Blockchain Learning

Nine Free Resources to Learn Solidity

If you’re here, it’s likely because you’ve heard about Solidity and blockchain development. You’re probably looking to get involved and build your own DApps and you’re looking to learn solidity. This resource is the perfect place to get you started if that’s the case.

First let’s stop off with a primer about Solidity: the what of it, the why, and then what of what you can build with it. If you want to skip ahead to the resources, feel free to click here.

What is Solidity?

learn solidity

We have to start here with the concept of blockchains, immutable, distributed datastores that are verified by a network of actors rather than one centralized source. Bitcoin is the most famous example of this technology and its first widely adopted application.

The genesis of Solidity comes from the simple realization that while Bitcoin is a highly secure blockchain, it was not very scalable both from a technical sense, but also from a community development sense: people had to either create entire new blockchains or fork into the existing chain to create new innovations or iterations.

Solidity is the programming language associated with Ethereum, a blockchain that aimed to have developers iterate on top of it: the Ethereum blockchain aimed to provide a more complete platform where developers could dictate more of the logic behind what data and payments get recorded on the blockchain, and which do not. Solidity is the programming tool that makes that possible.

Ethereum rests on the principle of decentralization: as with other blockchains, there is no centralized data storage that declares a certain state for the entire system. Data and the state of the system flow through a series of decentralized nodes that can be run on a number of different servers. For example, you could run an Ethereum node on your PC, and it would form part of a collective amount of computing power dedicated to sending and verifying data on the Ethereum blockchain.

In technical terms, the Solidity language is Turing complete (meaning that it is a general-purpose programming language similar in functionality to JavaScript and that you can program for loops, if statements and more and benefit from object inheritance and function modifiers), and it’s a contract-based language oriented around Python, JavaScript and C++ concepts meant to be executed on the Ethereum Virtual Machine. It is a high-level language that abstracts away many fundamental memory and space problems so as to allow programmers to easily build their own DApps.  

Why Solidity?

learn solidity

Solidity is a language that compiles with the Ethereum Virtual Machine within each Ethereum node — you can think of the EVM as a Solidity compiler. It comes with a set of conventions and global variables (such as msg.sender, indicating an Ethereum address that triggers a function) that can make your life easier, and it’s easily plugged into web3.js, an API that allows you to interact with common JavaScript web frameworks such as React.js. It is, in short, the easiest way for you to create Ethereum wallets and embed them in your apps (allowing you the ability to transact monetary value in your functions).

While there are other Ethereum programming languages (such as the more Python-based Serpent), Solidity is the most popular one with the most documentation out there so far. You’ll be able to access complementary libraries such as those offered by OpenZeppelin and utility tools such as Truffle.  If you want to build something with Ethereum, or you want to monetize your functions or build your own token, you’re probably going to end up using Solidity.

What can you build with Solidity?

learn solidity

You might have heard of smart contracts — you’ll be able to build smart contracts that execute different functions with Solidity. This will allow you to write data to the Ethereum blockchain or to receive or send Ether when people trigger different functions. Think of it as integrating a wallet directly in your code.

An interesting limitation you’ll have to deal with is that function calls are correlated to gas price within the Ethereum blockchain, meaning people will pay to execute functions on your platform. This is something you’ll have to keep in mind as you scale out new applications — the monetization of your functions can be a double-edged sword.

Onto real-life examples, you can build your own token according to the ERC-20 standard and start distributing it for initial coin offerings. You can create your own game that might go viral (such as CryptoKitties). You could even create your own decentralized exchange for buying and selling other tokens such as the people behind Etherdelta have done. You can build any number of Ethereum Dapps. The possibilities are there for you to explore once you understand the technology.

Ok, what are the nine resources you promised?

Fair is fair. If you’ve made it this far or clicked through all the way down to the bottom, you’re probably looking for the resources I promised you to help you learn Solidity and different Solidity tutorials. 

Here they are, in the order for which I think it makes sense for you to consult them.

1. ConSensys Resources

You’ll want to start off with a general overview of Ethereum, different blockchain concepts, and a feel for how Solidity can fit into that framework. This compilation of resources can certainly be helpful in that space and help flesh out the context of the space you’re getting into when you start building smart contracts. You’ll be able to see and get inspired by the vast potential of what has been done with smart contracts — and what you can do with them in turn.

2. How to set up an Ethereum Node

Next up, you’ll want to set up an Ethereum node yourself. This is useful for local testing of the apps you build but also, in a greater sense, provides you with the bridge you need to be part of the Ethereum community. The guide in question links out to a section where you can install an Ethereum node under different operating systems — it also contains sections dedicated to other aspects of Ethereum you may find interesting, including the mining mechanism for it.

3. Ethereum for Web Developers

This guide on Ethereum concepts can help you really understand the promise of Solidity and how you can extrapolate your thinking about web concepts into development for blockchain.

It also allows you to separate out the differences between centralized and decentralized datastores and how you should conceptually think about the programming logic for either.

The guide then moves onto a free tutorial (though the rest of the site offers paid lessons) on how to build a ballot voting system in Solidity that is much better than any “Hello World” tutorial could be in terms of getting you started on your path to learning Solidity.

4. BlockGeeks Guide to Solidity

This guide offers you yet another text-based case study for creating something in Solidity — this time though, you’ll be able to get an overview of how to build a web app in conjunction with Ethereum functionality.

Think of this as a case study upon which you can layer the Solidity concepts you’re learning and put them into practice. It will also teach you how to build a development environment for Solidity apps so that you can build your own smart contract and iterate on it in real-time without the fear of breaking anything as you learn Solidity.

5. Smart Contracts Best Practices

This resource from ConsenSys, an accelerator based on powering different teams working on Ethereum-based projects helps you define more of the meta-thinking behind Ethereum smart-contracts.

It will help you design smart contracts and tailor your learning around best practices that will keep your contracts performant and secure as you learn Solidity — highly desirable factors in an emerging tech ecosystem that is often in flux.

6. CryptoZombies

This handy game helps explain Ethereum functions in more depth and will help you learn Solidity — you can learn interactively by building your own version of CryptoKitties (CryptoZombies) — as you go through, you’ll be able to explore, among other things, function creation, function calls and modification, how to implement standard programming language such as assert and if statements, the different data types within Solidity, how to make objects inherit from one another, how to return data and finally, how to determine who can securely access and trigger different functions within your code.

7. YouTube Intro to Solidity

If you’re more of a visual learner rather than textual, you’ll find this video series on YouTube more amenable to your desire to learn Solidity.

Use it to catch up on Solidity concepts or to refresh your knowledge — or try this learning perspective first if you know a video is how you learn best.

8. Remix for Ethereum

With this web browser based compiler for Solidity code, you can experiment with different contracts and different functions cheaply, seeing what compiles properly and what doesn’t right off the bat. It’s a great, experimental way for you to learn Solidity and practice with it.

Consider it a fun sandbox for you to test different functions, similar to what JSFiddle provides for JavaScript, and your first line of validation and defense against improperly built code. It can also serve as the easiest way for you to experiment and build things in a sandbox setting you might not want to deploy.

9. Ethereum StackExchange

Finally, the last resource is the Stack Exchange forum for Ethereum and Solidity questions — a solid resource for you to consult and to pose questions as you’re stumbling around as a beginner, and a community you can give back to once you’ve practiced and built different things with Solidity.

I hope this guide is helpful to getting you started in your Solidity programming journey. If you want to join a newsletter packed with cutting-edge resources for how to learn new technology skills and maximally leverage them for a meaningful and socially impactful life, look no further than mine.

Resources Lists

32 Free Tech Job Boards for Programming Job Seekers

If you’re here, it’s because you’re likely looking for a job in technology. This excerpt from our upcoming guide to how to get a programming job without a degree will help you do just that by giving you categories of tech job resources, tech job boards and tech job sites to consult. I’ve helped isolate some of the best job boards for you among the many tech job boards out there. Hopefully, this resource will help you land a new job! 

General

tech job boards

The following tech job boards often have a selection of general jobs, but they are also useful resources that can be used to find technical jobs — if you’re able to process the information correctly. Tech companies abound on these general resources. 

LinkedIn

Sometimes it’s good to start at the most obvious place: LinkedIn has a large number of technology jobs that you can find quite easily. You can sign up for a free trial of the premium version and quickly look through different jobs.

LinkedIn can also be a great way to research hiring managers and get a sense of what a company is like before you even apply there. You’ll be able to see what the organizational hierarchy looks like by scrolling from one profile to another — and you’ll be able to see what skills the company emphasizes, either by looking at the profiles of those who were hired or by using your trial Premium account and looking at job postings or company pages.

You’ll want to think about how to optimize your LinkedIn profile so you can get the most out of this career-oriented social network. Among tech job boards, it is easily one of the largest. 

Crunchboard

Crunchboard is the job board associated with TechCrunch, a publication that specializes in writing about emerging technologies and new companies. As you can imagine, their job board is filled with a lot of technology and web development positions due to their audience.

Another technique you can use related to this is to look for startups that have just raised a large fundraising round on either TechCrunch or CrunchBase and reach out to hiring managers or executives at those companies: immediately after raising a fundraising round, a company is in aggressive growth mode, and is most likely looking to hire many qualified people to fill different and interesting job roles.

Hacker News

Besides being a great repository of technical articles and a community that curates people who are interested in the cutting edge of technology, Hacker News also serves as a job portal of sorts for Y Combinator companies — technology companies that might be as young as a two-person startup and also those who have started full maturing (as an example, Dropbox, Airbnb, and Quora were all at one time or another incubated by Y Combinator). The jobs section of the site features different YC companies and their hiring needs. There are also monthly threads started by a bot called Ask HN: Who is hiring? –where discussion about urgent job opportunities is surfaced that may be hard to find elsewhere. Here’s an example of a“who’s hiring” thread in May 2017.

By commenting on different articles and reaching out to different members in the Hacker News community, many of whom are senior figures in the startup world, you might also find your way to different mentors — and somebody who can introduce you to the right hiring manager.

AngelList

AngelList is an online repository for different startups. The jobs on offer here tend to be with earlier stage companies working at the edge of technology. One great perk about this is that entrepreneurs may be more willing to accept people from non-traditional backgrounds to work with them — especially if you’re willing to accept and maybe even embrace the risk that comes with working in a startup.

GitHub

GitHub, the living repository of code collaboration, also offers a selection of curated jobs for developers around the world. You can even search by programming language here, ensuring the best match for your skills.

Stack Overflow Jobs

Stack Overflow, the popular Q&A site for programming questions, offers a selection of different programming jobs, many of them posted by hiring managers who are trying to find top talent within the Stack Overflow community.

Glassdoor

Glassdoor is an interesting job board since you’ll be able to see what employees think about the company and you can get some transparency on the salary range the company offers as well. All in all, Glassdoor is a great general place to find technology jobs — but its greatest value probably rests in the additional data on employee satisfaction and approximate salary ranges that can help guide your career decisions.

Mashable

Mashable, the popular content repository based out of New York City, has a job board as well with a lot of different technology job postings.

The Muse

The Muse is a unique jobs resource, with tons of personalized career coaching and resources related to career development. It can be well worth browsing the content on the site itself if you want to learn about salary negotiation, interviews and career progression from a somewhat general perspective. The jobs board section also boasts a selection of technical and developer jobs.  

Startupers

Another community oriented towards posting startup jobs, many of them programming-related.

Dice

One of the leading repositories of tech jobs in the world, Dice offers nearly 80,000 jobs in technology for you to consider.

Cybercoders

Run by a placement agency for engineers, Cybercoders offers an easy way to search across 10,000+ different technology jobs across different industries.

Front-End/Design

tech job boards

The following tech job boards focus on jobs that are oriented towards front-end work and user design. Check these out if you’re looking to work on how the user experience of digital products feels for different people.

Smashing Magazine

Smashing Magazine is one of the premier web development and design resources on the web. They offer a selection of jobs tailored to front-end web development. It’s a perfect selection among a number of tech job boards if you’re looking for more design and development-driven work. 

Codepen Jobs

Codepen is a great interactive sandbox for front-end code, where you can use HTML/CSS/JavaScript to generate awesome interactive graphics — or where you can copy those snippets of code for use on your own website. The site also offers a job board that tilts towards front-end web development and design jobs, as you might expect.

Web Development

tech job boards

The following job boards will help you hone your skills in web development if that’s the technical career path you want to choose.

Sensational Jobs

Sensational Jobs curates a selection of different positions for web professionals of all sorts and stripes.

WordPress Jobs

The official WordPress jobs board will help you curate a selection of jobs in web development specifically focused on building things with the WordPress platform — a popular, open-source content-management system that serves as the back-end framework for nearly one in six of all websites on the Internet.

WPHired

WPHired is another great selection among this list of tech job boards — that is if you’re looking for development jobs oriented around WordPress.

Data Science

tech job boards

Data science entails a mix of statistics, programming and communication skills that are quite specialized. Oftentimes, data science job postings will be found in these specialized communities that have grown to help support the data science community. These tech job boards are often the result of careful curation and community-building. 

Kaggle Data Science Jobs

Kaggle is an online community centered around machine learning competitions. Here, they’ve used their reach in the data science community to curate a selection of data science jobs for you.

Data Elixir Job Board

Data Elixir offers a newsletter filled with data science resources, and also curates this job board to help data science jobs seekers.

KDNuggets Jobs

KDNuggets is one of the leading data science content hubs, filled with useful tutorials and resources to help you understand different topics in data science. This static jobs page is updated quite frequently with different job postings in data science.

Mobile Development

tech job boards

The following tech job boards curate different opportunities for those looking to build mobile apps on a variety of platforms. The most common tend to be iOS or Android-oriented.

Android Jobs

Android Jobs curates a selection of jobs for developers interested in building Android applications. Come here if you want to make your mark in mobile development.

Core Intuition

Core Intuition features a selection of curated Mac Cocoa and iOS development jobs — if you want to develop apps for Apple products, there are few job boards as well-placed as Core Intuition to help you advance along that career path.

Language-Specific

tech job boards

The following tech job boards are specific to a type of programming language. It can be a handy place to look if you plan to specialize in one language and grow your career there.

AngularJobs

AngularJobs is a job board curated around the Google-backed front-end JavaScript framework. Come here if you want to work with Angular.js and develop your JavaScript skills.

We Work Meteor

We Work Meteor is a job board focused on meteor.js, a full-stack JavaScript framework that can handle every part of web development. If you’re interested in pursuing a career using Meteor as your tool of choice, or if you’re interested in developing your JavaScript skills — coming to this job board wouldn’t be a bad choice.

Ruby Now

Ruby Now is a job board focused on curating Ruby on Rails specialists. Given the extensive use of Ruby on Rails for web development, you’ll mostly be working with web development positions if you look through this job board — though there are some more senior positions in back-end development.

Python Jobs (official Python website)

Python.org (the official centerpiece of the Python programming community) hosts a small repository of curated and interesting jobs that involve the use of Python. It’s one of the best among these tech job boards for those looking to work with Python. 

Python Jobs

Python Jobs (unaffiliated with the official Python programming community) is a great free resource for looking up Python jobs and web development jobs associated with the Django web development framework.

R-Users

R-Users is the place to go if you’re proficient in R or if you’re a statistician looking to get some work developing their programming skills in R.

Remote

One of the luxuries of working in a technology-oriented career is the ability to be able to work remotely from anywhere in the world. The following job boards curate remote opportunities in technology.

We Work Remotely

We Work Remotely curates a selection of jobs that are online and remote, with a section dedicated to just programming jobs.

Remote OK

RemoteOk is another job board that curates different jobs where remote work is available. They have a large selection of technology jobs and they have a neat categorization of the highest paying remote jobs and the technologies involved with it.

AngelList Remote Jobs

AngelList curates a selection of startup jobs where it’s acceptable to work remote. Again, as with the rest of AngelList, most of the jobs revolve around earlier stage startups — so be aware of that as you browse through this selection.

Upwork Jobs

Upwork is a curated marketplace where freelancers can meet potential employers. The entire process of payment, job search, and work management can be completely managed on Upwork. As a result, it can be a great place to find remote work in different technical fields.


Want more content like this? Be one of the first to get our Guide to Getting a Programming Job without a Degree!

Learning Lists

The most popular deep learning libraries

You might have heard of artificial intelligence, deep learning and neural networks, and wanted to get a path into this exciting new technology. This article will help you get a comprehensive overview of the tools and frameworks you can use to accelerate your impact and learning in artificial intelligence. It will help you understand what deep learning library you should use to accelerate your learning. 

This article assumes your familiarity with basic deep learning concepts — if you need to catch up on those, the following Wikipedia article will help you get to scratch.

This is a walkthrough of the most popular deep learning libraries, examples of clever projects and implementations that have used the different libraries to create something awesome.

I’ve ranked the deep learning libraries in question by the number of Github stars their repositories have collected as of June 2017 — a great way to see how much traction these different frameworks have with programmers.

Deep learning library


1- TensorFlow

Overview: TensorFlow is the open-source machine learning library developed by Google (and still used in both research and production level applications at the company). The library allows you to bring machine intelligence capabilities to all sorts of devices, from those equipped with GPUs to mobile devices such as the Raspberry Pi. With over open-source 6,000 repositories using TensorFlow, it has quickly become one of the most popular frameworks out there for those looking to build something with deep learning. TensorFlow is very accessible, with APIs for Python, C++, Haskell, Java, Go and Rust and a 3rd party package built in R.

Introductory Tutorial: Get introduced to TensorFlow with this official tutorial by Google by using TensorFlow with the famous MNIST dataset.

Use Cases: TensorFlow has become of the most popularized deep learning frameworks, and as such, it has seen a wide array of uses from powering cutting-edge machine learning work at different Silicon Valley companies to classifying cucumbers for farmers.

Resources:

  1. This Github repository TensorFlow-World contains a bunch of introductory tutorials with code compiled together to give you a better sense of how to do deep learning on TensorFlow.
  2. TensorFlow-101 contains tutorials on how to get started with TensorFlow in Jupyter Notebook with Python.
  3. This Github repository contains example code that will help you work through different analyses in TensorFlow.  

How to get started: Get all of the documentation and installation instructions here, then start practicing and training deep learning models on different datasets! TensorFlow is one of the most popular and powerful deep learning frameworks out there: take advantage! 


2- Scikit-learn

Overview: Scikit-learn is the versatile machine learning knife in Python, used for simple experimentation and iteration with different templated machine learning models. With modules like the MLPClassifier, you can easily bring deep learning approaches to your datasets and use the rest of the trusty scikit-learn ensemble (such as train_test_split) to validate and evaluate your model. You can also combine the scikit-learn interface with different deep learning libraries if you want to do more powerful analyses.

Introductory Tutorial: This tutorial runs through how to use scikit-learn as a deep learning library and a multilevel perceptron model to classify different types of wine. You can see how with a few lines of code, you can create a very accurate model with many variables.  

Use Cases: scikit-learn is often the first go-to deep learning tool for people working in the Python data science ecosystem: it comes pre-installed with Jupyter Notebook and it comes with powerful functions that are already-optimized versions of essential deep learning functions. You’ll be able to quickly build together machine learning models, evaluate them, and split different use cases into either test or training sets.

Resources:

  1. This cheatsheet by Datacamp will help you with many of the essential functions in scikit-learn.
  2. Springboard has a tutorial that will teach you how to build a simple neural network with scikit-learn and its MLPClassifier module.
  3. This gentle introduction into scikit-learn can help you ease into this machine learning package.

How to get started: Use jupyter notebook, which comes with scikit-learn installed by default. Start training machine learning models, then move into Scikit Flow, an interface that combines the code you’d use in scikit-learn augmented with functionality from Google’s TensorFlow library (we’ll dive into TensorFlow a little bit later in this article).


3- Caffe

Overview: Caffe is a deep learning framework built by Berkeley’s AI Research department (BAIR) and sustained through the use of community contributors. It features speedy application of deep learning approaches, with the ability to classify up to 60 million images a day on a single GPU. It powers a variety of deep learning projects in machine vision, speech and more — with projects ranging from fully fleshed out applications to academic papers.

Introductory Tutorial: This tutorial built by Berkeley AI researchers will help you get up to speed with this powerful deep learning framework.

Use Cases: Caffe is used for a variety of academic research — this web page has a ton of examples ranging from image classification to training the classic LeNet model.

Resources:

  1. This blog post will help you get up to scratch with using Caffe and Python with a relevant classification example from Kaggle on how to distinguish between dogs and cats.
  2. This tutorial will help you with loading Caffe onto iPython Notebook and also with C++ implementations of the library.
  3. This tutorial will get you to understand how you can build a layer of a neural network within Caffe.

How to get started: Use the command line and get started with different use cases of Caffe. You can use this tutorial to get installation instructions across a whole array of different platforms.


4- Keras

Overview: Keras is an open source deep learning library for neural networks written in Python. Authored by François Chollet, the library was meant to be a quick and easy way to experiment with different deep learning models — as a high-level API written entirely in Python, the library is easy to debug and navigate. It supports both convolutional and recurrent neural networks and it is designed to be as intuitive as possible for users to grasp.

Introductory Tutorial: This introductory tutorial will run over how to get started with Keras — all the way from installing the package to training a model with 99% predictive accuracy on the seminal MNIST dataset.  

Use Cases: Most of the time, Keras is used to build simple deep learning models as conceptual sketches: you would validate crude ideas using Keras, and get a first glance at whether or not you had a good idea for a deep learning architecture that can tackle a problem.

Resources:

  1. This course by DataCamp helps you dive deeper into Keras, even if you’re just a deep learning beginner!
  2. Here is a link to the official Keras documentation which allows you to get access to the inner working of the framework from the creators of it.
  3. If you’re more comfortable with the R programming language, you can use the R interface to Keras.

5- Torch

Overview: Torch is an open-source deep learning framework based on optimizing performance on GPUs based on the programming language Lua with an underlying C/CUDA implementation. It allows for the parallelization of neural networks across different CPUs and GPUs. Torch is used by a lot of organizations at the cutting edge of machine learning, from Google to Facebook. It has been extended for use in mobile settings, with the ability to perform on iOS and Android.

Introductory Tutorial: This 60 minute blitz into Torch will get you started and ready to use this powerful deep learning tool.

Use Cases: The deep learning library Torch is used for a lot of machine learning and deep learning research programs at leading companies such as Google, Twitter, and Facebook. Given its origins as Facebook Research’s default deep learning framework, you can be sure that it comes with all of the support it needs from one of the largest tech companies in the world.

Resources:

You’ll want to get started understanding Lua before you work with Torch. This handy guide will help you get up to scratch.

This Github repository contains a whole host of Torch tutorials.

The following blog article by Facebook Research contains a lot of related work done in Torch.

How to get started: Get the deep learning library Torch installed, then start running it on different deep learning problems. You might want to refer to the Facebook research blog for inspiration.


6- Theano

Overview: Theano is a deep learning framework built deeply into the Python data science ecosystem, with deep integration with the NumPy numerical computing library. You can use C to generate code as well to make it even speedier — and it is the default teaching tool used in deep learning founding father Yoshua Bengio’s lab.  

Introductory Tutorial: This Theano tutorial on Jupyter Notebook will help you understand the nuances of the deep learning library Theano, and will get you up to scratch with different conventions within the library.

Use Cases: This forum will allow you to understand different problems and use cases Theano users create with the deep learning library.

Resources:

  1. This documentation will help you understand more about the deep learning library Theano. 
  2. This blog post will help you understand the performance differences between Theano and Torch.
  3. Here is an introductory tutorial to Theano.

How to get started: Use the instructions here to get started with installing Theano.


7- Neon

Overview: Neon is an open-source deep learning platform built on Python that is committed to the most powerful implementation of deep learning possible, with a consideration towards simplicity.

Introductory Tutorial: Work with this Github repository which contains a Model Zoo that contains different scenarios of working with Neon.

Use Cases: This Youtube playlist walks through different use cases with Neon, including playing Pong, and speech recognition.

Resources:

  1. Learn how to do basic classification with the MNIST database and the deep learning library neon with this introductory-level tutorial.
  2. Use this video course to get you started in Neon.
  3. This compilation of Neon resources will help you learn the framework.

How to get started: Get the deep learning library Neon installed with the following instructions.

Learning Lists

101+ Resources to Learn Data Science

Many people are seeking to learn data science these days. It’s become a trendy topic associated with high salaries and some of the most interesting problems in the world. This demand has created many different resources in the data science space. People have curated their selection of favorite resources to learn data science, but I was seeking out something more comprehensive — so I built this list. Here’s my attempt at getting you my favorite resources in the data science space so you can understand what’s going on in the field — and how you can get your hands dirty and start learning right away.

Full disclosure: I work for Springboard (one of the data science education providers listed below). 

What is data science?

learn data science

First, let’s start with an overview of what seems to have become a popularized buzzword and defining exactly what you want to learn: data science. Data science is the combination of three kinds of skillsets: statistics, programming and business knowledge. It’s the interplay between these crafts where you’ll find a data scientist — somebody who will programmatically examine large data sets for precious business insights — somebody who can combine computer science knowledge with business insight.

You can use data science concepts and training to do data mining and get statistical inferences from large datasets. Using advanced techniques such as natural language processing and unsupervised learning, you can tame the power of computation and get precious data insights others simply cannot access. That will be attractive to all sorts of potential employers in the data science field, from Silicon Valley to Wall Street.

In order to get there though, you have to start with the basic techniques and basic concepts that underlie data science. Learning data science requires having an understanding of the process that goes behind it, and the various components that are required to bring everything together. Let’s get started on getting you know that knowledge. 

Overview

learn data science

You’ll want to get an overview of the field and the processes and concepts that make up data science so you can learn data science.

1- Data Scientist: The Sexiest Job of the 21st Century

In this seminal article, ex-Chief Data Scientist of the United States, DJ Patil, goes into exactly what makes a career in data science so compelling. It’s great fuel to the fire if you’re looking to learn data science. 

2What is data science?

This overview of data science by Berkeley delves into how data science came to be — and the average salary you can expect in the field.

3Data Science Salary Survey (2016) – O’Reilly

O’Reilly, a leading publication and media company on the cutting edge of technology, dives deeper into what tools and factors go into higher data science salaries. They’ve surveyed hundreds of data scientists in the field. Learn what pays and what doesn’t with data science careers through their research!

4Data Science (Wikipedia)

Wikipedia’s overview of data science goes over the history of the field and points to many different resources in the field. It can be a handy jumping-off point for further research.

5Building Data Science Teams

This piece by DJ Patil goes into the different roles inherent in a data scientist’s job — and exactly how best to build out a data science team.

6- Data Science Process

This piece by Springboard goes into what the day-to-day of data science looks like — tracing it all the way to a first principles view of exactly what steps effective data science requires.  

Interactive Tutorials

learn data science

Now that you’re done with an overview of the topic, it’s time to get your hands a bit dirty with interactive tutorials that will help you learn different parts of data science — whether that’s the statistical theories behind machine learning algorithms, or the programming skills you’ll need to implement those theories.

Statistics/Math

Understanding probability and the basics of statistics is essential to being able to understand machine learning methods and how to handle massive amounts of data. Linear algebra and the ability to manipulate different expressions of data (in matrix form or otherwise) will also be incredibly helpful in detailing what data scientists do. You’ll want to refresh your statistics knowledge and get a handle on the math you need to know to join their ranks.

7KhanAcademy (Statistics/Probability)

This free course from KhanAcademy serves as a great catch-up on the basics of probability and statistics.

8Introduction to Statistics in R (Datacamp)

Learn a bit of R (a programming language commonly used in data science) and statistics at the same time with this interactive walkthrough from Datacamp.

9Statistics 101 

This Youtube playlist from the Harvard Extension School covers everything from random variables to different statistical distributions. 

SQL

Knowing SQL and how to query from relational databases is a skill that is one of the building blocks of data science. You’ll often use SQL to source your data for further analysis — or even to transform your data on the spot.

10Mode Analytics SQL School

Mode Analytics teaches SQL through the use of case studies with real data. It’s an interactive experience that’ll teach you the basics of SQL by having you run through a dataset with some simple yet powerful commands.

11Learn SQL (Codecademy)

Codecademy, well known for its basic curated tutorials in different programming languages, has this simple interactive module that will help you learn SQL.

12SQLCourse

This is an older tutorial, but one that still holds up as an example of an organized approach to learning SQL.

Python

Python is one of the workhorse languages of data science — one of the most popular along with R. The large open-source community that powers Python enables it to be a powerful, versatile programming language that can help facilitate everything from data wrangling to training powerful machine learning models. It’s a powerful tool you’ll want to learn as you learn data science. 

13Pandas Cookbook

This interactive set of code examples walks you through how to get started with Pandas, the data processing library most commonly used in Python. It’s built by Julia Evans

14Intro to Python for Data Science (DataCamp)

This interactive course will walk you through the basics of the data science libraries for Python.

15Gentle introduction to scikit-learn

This gentle introductory tutorial will help you understand one of the most powerful machine learning and data science libraries out there: Python’s scikit-learn. You’ll be able to train simple, off-the-shelf data models in a matter of minutes.

16A dramatic tour through Python’s data visualization landscape

This somewhat witty and whimsical walkthrough will help you explore the difference between the major data visualization tools in the Python ecosystem — including some options that were ported from R!

17- Web scraping with BeautifulSoup

This short guide will teach you how to take information from different websites and render it into a format that is easy for machines to process — a handy skill for anybody looking to work with many different datasets. I often use the set of techniques described to scrape tables from Wikipedia so I can process that data in Python.

R

R is another popular programming language used for data science — in fact, it’s often pitted against Python as a comparable tool. The truth is that you can use both — and in fact, being conversant in both can only help you progress faster and further as a data scientist.

18– Introduction to R (Datacamp)

Here is the equivalent of the Datacamp introduction to Python — except this time for R, another common data science programming language.

19A complete tutorial to learn R from scratch

This tutorial, rendered as a blog post, offers a comprehensive A to Z guide to getting started in R. It covers everything from importing data into R to creating predictive models with it.

20Try R

Sponsored by O’Reilly Media, this interactive course will reward you with a badge for each fundamental building block of R you learn.

Hadoop

Hadoop is a big data framework meant to facilitate the treatment and storage of large data sets that have be processed in parallel by many different servers in order to yield actionable insights. 

21Hadoop Tutorial (Tutorialspoint)

This set of tutorials on Hadoop will help you understand how big data frameworks work — and how you can apply Hadoop to your data.

22Hortonworks Sandbox tutorial on Hadoop

This interactive Hadoop sandbox by Hortonworks lets you play with Hadoop code.

Spark

Spark helps solve some speed, flexibility and efficiency issues with Hadoop through the use of a new data structure: the RDD or resilient distributed dataset.

23Apache Spark Tutorial (TutorialsPoint)

TutorialsPoint offers a similar tutorial to Spark as it does for Hadoop.

24Hands-on introduction to Spark

Hortonworks has a sandbox that will let you play around with Spark code.

Courses/Workshops

learn data science

The following courses and online workshops will help you learn data science in an organized fashion. Use these resources to accelerate your learning of data science if you need to. A lot of these courses will help you find data science work, and you’ll likely be able to do data science projects after finishing them. 

25Fast.ai

This massive online course, built by a Kaggle champion in machine learning, will help you learn about neural networks and how to train machine learning models.

26Foundations of Data Science (Springboard)

This course offered by Springboard features a curated selection of resources in R, SQL and the basics of machine learning, as well as personalized mentoring from data science experts who work in the field.

27Data Science Intensive (Springboard)

Yet another course offered by Springboard, though this one is more advanced. Focused on Python and teaching the intricacies of machine learning methods, this course will help you use different machine learning techniques with ease.

28Data Science Career Track (Springboard)

Springboard’s Data Science Career Track is the first online bootcamp to offer a data science job or your tuition back. With personalized career coaching, mentorship from data scientists and exclusive employer partnerships, Springboard is putting it all on the line to help you get a job in data science.

29Data Science (Coursera)

Coursera partnered with Johns Hopkins University to deliver this nine-course series on data science, covering everything from tools to advanced machine learning methods.

30Machine Learning (Coursera)

This curated set of machine learning courses taught by Andrew Ng (the famous Stanford professor who founded Coursera in the first place) is one of the best resources to consult as you start understanding data science.

31Thinkful Data Science Bootcamp

Thinkful, an online education provider, provides a data science bootcamp that will curate your learning of data science and Python.

32Intro to Machine Learning (Udacity)

Udacity offers a free mini-course curated by Facebook and Tableau to help guide you through to doing analysis of the Enron email database.

33Data Science Certificate (Harvard Extension School)

This data science certificate offered by the Harvard Extension School can help you learn data science while getting credits and credibility from one of the leading universities in the world.

34Statistics with R (Coursera)

This selection of courses created in partnership with Duke University will help you understand basic probability and the use of Bayes’ Rule through the use of R.

35Data Science (EdX)

This set of curated learning paths in data science can help you get accreditation in the field — if you’re willing to pay for it.

36Insight Data Science Fellowship

The Insight Data Science Fellowship is a special type of data science education program — it takes talented PhD. students who have already demonstrated technical skills and aptitude, and helps them bridge the gap between academia and industry with a postgraduate fellowship that combines the best of academic rigor with industry knowledge.

37Data Science (General Assembly)

General Assembly, one of the largest online education providers in the world, offers courses in data science.

38Galvanize

If you’re looking for an in-person experience to learn data science instead of something online, Galvanize can help. This link leads to the San Francisco experience — however, Galvanize itself is present in many different other cities.

39Coursereport Data Science Reviews

Here are some reviews of different data science courses in Coursereport — this will allow you to pick and choose between many different options with fair reviews from previous students on display.

40Switchup Data Science Reviews

Here are some more reviews of different data science courses, this time from Switchup, another course review site.

Books

learn data science

Oftentimes it’s not a great course that helps you learn the most — it can be one single resource within that course — say a particularly well-written book. This selection of data science books can help you understand data science in detail.

41Bayesian Methods for Hackers

This book, delivered as an extended Github repository, can help you understand Bayesian inference and how to think about probabilities by working through them in code. 

42Think Stats

This O’Reilly book helps you conceptualize statistical concepts by having you work with them in Python.

43Think Bayes

This book combines Python programming with Bayesian inference, and can be a handy resource in case the books above aren’t enough.

44Deep Learning

This free technical book by some of the scions of deep learning and artificial intelligence (Ian Goodfellow, Yoshua Bengio and Aaron Courville) will help you understand exactly how to think about deep learning and neural networks.

45Learn Python the Hard Way

In case you need a refresher on Python, Learn Python the Hard Way will help you break down exactly what you need to do to master Python. While it focuses on an older version of Python, the first principles taught here can be useful to those looking to freshen up their knowledge of Python — though you shouldn’t become overly dependent on this book as it has quite a rigid philosophy on one particular version of Python. 

46The Data Science Handbook

This Data Science Handbook curates insights from 25 data science leaders and distills what it truly means to work in this exciting new field.

47Data Science from Scratch

This book from O’Reilly goes into the first principles of data science, looking beyond the programming tools and frameworks.

48Storytelling with Data

This book will help you visualize insights that you find within your data and teach you how to communicate them effectively so that you can drive impact with your data findings.

49Exploratory Data Analysis with R

Roger D. Peng, an expert in statistics, has written this book to teach how to look through datasets with the R programming language.

50Interactive Data Visualization for the Web

This online book will teach you how to use frameworks such as D3.js to make your visualizations fully interactive on the web.

51Machine Learning Yearning

This book by Andrew Ng, the famous artificial intelligence leader who founded Coursera, is going to be released soon — sign up to get drafts of new chapters as they come in!

Curated Collections

learn data science

I know you’re looking for curated resources to learn data science. There’s more than just this list right here — and each collection will help you expand your knowledge and collection of great data science resources even further.

52Awesome Machine Learning

This Github repository follows the “Awesome” method of curating the best resources in a particular space — in this case, all the different resources you’d need to learn machine learning.  

53Awesome Deep Learning Papers

In case you ever wanted to get a handle on the science behind the amazing technology being built out of artificial intelligence, this awesome curation of deep learning papers will help you continually be on top of exciting new developments.

54Awesome TensorFlow

TensorFlow is an awesome deep learning framework: this Github repository will have everything you need to learn more.

55Awesome Data Science

This repository is everything it promises: an awesome curation of different data science resources.

56Data Analysis Learning Path (Springboard)

This learning path curates different resources in an intuitive fashion so that you can learn the data analysis skills required for data science.

57The Open Source Data Science Masters

This is a curated curriculum of free, open-source resources to learn data science — consider it a masters’ degree for a fraction of the price.

General Resources

learn data science

58A visual intro to machine learning

This interactive, visual view of data science in action can help you conceptualize data science, especially if you prefer to learn visually. 

59Deep Learning Review (Nature)

This paper summarizes some of the latest findings in deep learning and artificial intelligence and it is written by one of the founding fathers of modern artificial intelligence research: Geoffrey Hinton.

60Build a deep learning machine

This fun little tutorial by O’Reilly will teach you how to build a computer that you can use specifically for data science purposes.

61- How can I become a data scientist (Quora)

This Quora thread contains different thoughtful replies on how to become a data scientist — and includes a bevy of free resources to boot!

62Becoming a Data Scientist

This blog charts the author, Renee, and her path from being a SQL analyst to becoming a full-fledged data scientist.

Career Advice

learn data science

Becoming a data scientist is now a career path that many envy — however, getting started and placing yourself in a position where you are paid to practice data science doesn’t start and end with technical skills. Here’s a set of resources that will help spell out exactly what you need to do to have a successful data science career.

632015 Data Science Salary Survey (O’Reilly)

This salary survey by O’Reilly was curated from about 600 respondents who divulged their salary and what they did at work. It’s an informative read on what the average salaries are like in data science and what factors or technical skills can either increase your data science salary — or set it on the path to stagnating.

We already highlighted the 2016 survey as part of our general overview of data science, but the 2015 survey will add even more context on how the data science industry works — and how much you should expect to be paid.

64Guide to Data Science Jobs (Springboard)

This guide to Data Science Jobs by Springboard curates a variety of job seeker and hiring manager stories and seeks to inform you on every element of what it takes to get a data science job: from how to get hiring managers to notice your profile, to advice on what technologies and skills you should practice before doing a data science interview. 

65Guide to Data Science Interviews (Springboard)

This companion guide to the Guide to Data Science Jobs by Springboard runs you through different interview questions and exactly what hiring managers are thinking when they are on the other side of the table. It’s a comprehensive overview of the data science interview process — and it provides you actionable tips on how to ace the data science interview.

66Getting your first job in data science

This blog post goes over different general tips on how to get that first job in data science.

67Data Science Career Paths

This blog post by Springboard breaks down the difference between data analysts, data scientists and data engineers.

Datasets

learn data science

In order to really get started and to learn data science, you have to have datasets to play with. The following resources will link you to different datasets you can experiment with as you’re learning data science techniques and putting them into practice.

6819 Free Public Datasets (Springboard)

This curated list of 19 free public datasets will help you get started on your path to learn data science!

69Kaggle Datasets

This list of datasets curated by Kaggle comes with upvote functionality as well as comments, so you can exactly which datasets are the most exciting — and what work has already been done with them.

70Reddit Datasets

This subreddit can be a handy way to pick out new datasets, and see some of the most popular ones.

71Data.world

This new social network has evolved around sharing great datasets and bringing data fans together!

72Google BigQuery Datasets

Google BigQuery has open-sourced some interesting big data sets–from Reddit comments to Github activity.

73Quandl

Quandl is a search engine mostly used for financial and economic data. Comb through if you’re looking in that space for data to play with. 

74Public Big Datasets

This curated list of big datasets can help you practice with Hadoop or Spark.

75Wikipedia dumps

Wikipedia dumps data from its database and makes it free to analyze every so often. Sift through here if you want to query the world’s largest collection of knowledge on your quest to learn data science.

76Open Street Map

This collection of open-source geographic data extends around the world in its reach!

Resources/Blogs to Follow

learn data science

You’ll want to keep an eye on different resources and blogs that update frequently as you learn data science. This ensures that you’re always on top of the latest developments — and it can be a stimulating way to keep your data science skills sharp.

77Top data scientists to follow on Twitter

This is a list of data science influencers you’ll want to consider following to get to know more about the industry.

7850 of the best data science blogs

This curated list of data science blogs will help you find the best blogs to follow as you learn data science.

79Ultimate guide to data science blogs

This larger, extended guide to data science blogs has a lot more entries — feel free to take a look if you feel like you want something comprehensive to digest.

80KDNuggets

KDNuggets is one of the largest data science communities on the web, and their blog regularly posts interesting data science content.

81R-bloggers

R Bloggers is a data science blog focused on tutorials to learn R and different resources in the R ecosystem. 

82Dataconomy

Dataconomy focuses on larger trends in data science rather than many technical tutorials. It’s the data science blog with the largest focus on the European data science scene as well.

83Analytics Vidhya

Analytics Vidhya contains plenty of technical tutorials on many data science topics.

84Big Data Made Simple

Big Data Made Simple is a relatable blog that conveys different topics in data science in an approachable manner.

85Yhat blog

The Yhat blog is always filled with interesting tutorials and data science case studies.

86Machine Learning Mastery

Machine Learning Mastery focuses on the intricacies of machine learning.

87Learndatasci

Learndatasci is a blog that offers a broad overview of different data science topics.

88Mastersindatascience

Mastersindatascience is the resource to consult if you wanted to look at paid offerings to learn data science.

Newsletters

learn data science

If you want regular updates in your inbox on the latest news in data science, there’s no better way to do that than to subscribe to the following data science newsletters.

89Data Science Weekly

This weekly newsletter summarizes the latest tutorials and resources in data science. It’s a very useful resource if you’re looking to learn data science. 

90Data Elixir

Another data science newsletter that will keep you informed on the latest happenings in data science. 

91Python Weekly

This weekly Python newsletter curates a selection of the finest Python resources, many of them related to data science.

92Datafloq

This handy newsletter promises to be a one-stop shop for you when it comes to big data trends.

93- The Analytics Dispatch

Mode Analytics provides a dispatch to keep you informed on all things analytics and BI-related.

94- Postgres Weekly

This Postgres Weekly newsletter keeps you informed on the latest Postgres updates.

95- O’Reilly Data Newsletter

A premium data science newsletter, O’Reilly will often curate the best data science resources that have popped up.

Communities

learn data science

While newsletters and blogs are great, interactive communities where participants share articles and comment on them together can truly help you entrench your data science knowledge. Here are just a few of those communities where you can learn data science and interact with different data science practitioners.

96Datatau

Datatau is a sort of Hacker News for data science resources where data science practitioners discuss the latest news and upvote the best articles.

97Reddit Datascience

This subreddit deals with general data science topics.

98Reddit Machine Learning

This subreddit deals with more in-depth machine learning materials and discussions.

99Reddit Deep Learners

This subreddit deals with how to learn artificial intelligence and deep learning.

100Reddit Data is Beautiful

This subreddit contains impactful data visualizations that are visually appealing — and a true set of examples if you want to display your data in a beautiful manner.

101Data Science Stack Exchange

This subcomponent of the Stack Exchange network deals with technical questions and solutions in data science.

102Quora Data Science

This section of Quora is composed of many of the questions posed about data science — it is an awesome resource for those looking to learn data science. 

Hopefully the resources above have been helpful for you to learn data science: let me know in the comments below what you think about them or whether you think there are some I missed!

Learning Guides

How to do common Excel and SQL tasks in Python

How to do common Excel and SQL tasks in Python

The code and data for this tutorial can be found in this Github repository. For more information on how to use Github, check out this guide

Data practitioners have many tools that they use to slice and dice data. Some people use Excel, some people use SQL — and some people use Python. The advantages of using Python are obvious when it comes to certain tasks. You can process much bigger datasets at much faster speeds. You can use open source machine learning libraries built on top of Python. You can easily import and export data in different formats. 

Python can become an essential part of any data analyst’s toolbox due to its versatility. However, it can be hard to get started. Most data analysts are probably familiar with either SQL or Excel. This tutorial is structured to help you transfer over skills and techniques from those two programs to Python.

First, let’s get you set up on Python. The easiest way to get started is to use Jupyter Notebook and Anaconda. This visual interface will allow you to plug Python code in and immediately see the output of your results. It’ll make it easy for you to follow along with the rest of this tutorial as well.

I highly recommend using Anaconda, but this beginners guide will also help you with installing Python directly — though that’ll make following this tutorial harder. 

Let’s start with the basics: opening up a dataset.

IMPORTING DATA

You can import .sql databases and process them in SQL queries. On Excel, you could double-click a file and then start working with it in spreadsheet mode. In Python, there’s slightly more complexity that comes at the benefit of being able to work with many different types of file formats and data sources.

Using Pandas, a data processing library, you can import a variety of file formats using the read function. A full list of the file formats you can import using this function is in the Pandas documentation. You can import everything from CSV and Excel files to the whole content of HTML files!

One of the biggest advantages of using Python is the ability to be able to source data from the vast confines of the web instead of only being able to access files you’ve downloaded manually. The Python requests library can help you sort through different websites and take data from them while the BeautifulSoup library can help you process and filter the data so you get exactly what you need. Be careful of usage rights issues if you’re going to go down this route.

(Don’t worry if you want to skip this part, you can! The raw csv file is here, and you can download it at will if you’d rather start this exercise without taking data from the web. Or you can git clone the entire repository.)

In this example, we’re going to take a Wikipedia table of countries by their nominal GDP per capita (a technical term that means an amount of income a country earns divided over the number of its population), and use the Pandas library in Python to sort through the data.

First, let’s import the different libraries we need. For more information on how imports work in Python, click here.

import pandas as pd
import numpy as np
import requests
from bs4 import BeautifulSoup
import re

We’ll need the Pandas library to process our data. We’ll need the numpy library to perform manipulations and transformations of numeric data. We’ll need the requests library to get HTML data from a website. We’ll need BeautifulSoup to process that data. Finally, we’ll need the regular expression library of Python (re) to change certain strings that will come up as we process the data. 

It’s not necessary to know much about regular expressions in Python, but they are a powerful tool you can use to match and replace certain strings or substrings. Here’s a tutorial if you wanted to learn more.

r = requests.get('https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita')

gdptable = r.text
soup = BeautifulSoup(gdptable, 'lxml')
table = soup.find('table', attrs = {"class" :"wikitable sortable"})

theads=[]
for tx in table.findAll('th'):
    theads.append(tx.text)

data =[]
for rows in table.findAll('tr'):
        row={}
        i=0
        for cell in rows.findAll('td'):
            row[theads[i]]=re.sub('\xa0', '',cell.text)
            i+=1
        if len(row)!=0:
            data.append(row)
print(data)

Credit to this website for some of the code.

Here’s a more technical explanation of how to grab HTML tables with Python code with more step-by-step instructions.

You can copy + paste the code above into your own Anaconda setup, and iterate with it if you want to play with some Python code!

The output from the code below, if you don’t modify it, is what is known as a list of dictionaries.

You’ll notice commas separating bracketed lists of key-value pairs. Each bracketed list represents a row in our dataframe, and each column is represented by the keys within: we are working with a country’s rank, its GDP per capita (expressed as US$), and its name (in ‘Country’).

For some more information on how data structures such as lists and dictionaries work in Python, this tutorial will help as well as this course: Intermediate Data Science Course by Springboard.

Thankfully, we don’t need to understand much of that in order to move this data into a Pandas dataframe, a similar way of aggregating data to a SQL table or an Excel spreadsheet. With one line of code, we’ve assigned and saved this data into a Pandas dataframe — as it turns out to be the case, lists of dictionaries are the perfect data format to be converted to a dataframe.

gdp = pd.DataFrame(data)

With this simple Python assignment to the variable gdp, we now have a dataframe we can open up and explore anytime we write out the word gdp. We can add Python functions to that word to create curated views of the data within. For a bit more of an in-depth look at what we just did with the equal sign and assignment in Python, this tutorial is helpful.

TAKING A QUICK LOOK AT THE DATA

Now, if we want to take a quick look at what we’ve done, we can use the head() function, which works very similarly to selecting a few rows in Excel or the LIMIT function in SQL. Use it handily to take a quick look at datasets without loading the whole thing! You can also insert a number within the head function if you want to look at a particular number of rows.

gdp.head()

The output we get are the first five rows of the GDP per capita dataset (the default value of the head function), which we can see are neatly arranged into three columns as well as an index column. Be aware that Python starts indexes at 0 and not 1, such that if you wanted to call up the first value in a dataframe, you’d use 0 instead of 1! You can change the number of rows displayed by adding a number of your choice within the parentheses. Try it out!

RENAMING COLUMNS

One thing you’ll quickly realize in Python is that names with certain special characters (such as $) can become very annoying to handle. We’ll want to rename certain columns, something you can do easily in Excel by clicking on the column name and typing over the old name and something you can do in SQL either with the ALTER TABLE statement or sp_rename in SQL server.

In Pandas, the way to do it is with the rename function.

gdp = gdp.rename(columns = {'US$':'gdp_per_capita'}) 

In implementing the above function, we’ll be replacing the column header ‘US$’ with the column header ‘gdp_per_capita’. A quick .head() function call confirms that this change has been made.

DELETING COLUMNS

There’s been some data corruption! If you look at the Rank column, you’ll notice that there are random dashes scattered throughout it. That’s not good, and since the actual number order is disrupted, this makes the Rank column quite useless, especially with the numbered index column that Pandas gives you by default.

Fortunately, deleting a column is easy with a built-in Python function: del. By selecting columns through the use of square brackets appended to the dataframe name.

del gdp['Rank']

Now, with another call to the head function, we can confirm that the dataframe no longer contains a rank column.

CONVERTING DATA TYPES WITHIN COLUMNS

Sometimes, a given data type is hard to work with.This handy tutorial will break down the differences between the different data types in Python in case you need a refresher.

In Excel, you could right-click and find ways of converting columns of data to a different type of data quite easily. You could copy a set of cells rendered by formulas and paste special as values, and you can use formatting options to quickly switch between numbers, dates, and strings. 

It’s not as easy in Python to switch between one data type to the other sometimes, but it’s certainly possible.

Let’s first use the re library in Python. We will regular expressions to replace the commas within the gdp_per_capita column so we can more easily work with that column.

gdp['gdp_per_capita'] = gdp['gdp_per_capita'].apply(lambda x: re.sub(',','',x))

The re.sub function essentially takes every comma and replaces it with a blank space. This following tutorial goes into each function of the re library in detail.

Now that we’ve gotten rid of the commas, we can easily convert the column into a numeric one.

gdp['gdp_per_capita'] = gdp['gdp_per_capita'].apply(pd.to_numeric)

Now we can calculate a mean for the column.

We can see that the mean of the GDP per capita column is about $13037.27, something we couldn’t do if the column were classified as strings (which you can’t perform arithmetic operations on). We can now do all sorts of calculations on the GDP per capita column that we weren’t able to do before — including filtering the columns by different values and determining what percentile rank values are for the column.   

SELECTING/FILTERING DATA

The basic need of any data analyst is to slice and dice a large dataset into actionable insights. In order to do that, you have to go through a subset of the data you have: this is where selecting and filtering data is very helpful. In SQL, this is accomplished with a mix of SELECT and different other functions, while in Excel, this can be done by dragging and dropping through data and implementing filters.

Using the Pandas library, you can quickly filter down with different functions or queries.

Let’s, as a quick proxy, only show countries that have a GDP per capita above $50,000.

This is how to do it:

gdp50000 = gdp[gdp['gdp_per_capita'] > 50000]

We assign a new dataframe with a filter that takes a column and creates a boolean variable — this function above essentially says “create a new dataframe for which there is a GDP per capita above 50000”. Now we can display gdp50000.

And now we see that there are 12 countries with a GDP above 50000!

Now let’s select only rows that belong to a country that start with s.

We can now display a new dataframe containing only countries that start with s. A quick check with the len function (a life-saver for counting the number of rows in a dataframe!) indicates that we have 25 countries that fit the bill.

Now what if we want to chain those two filter conditions together?

Here’s where chained filtering comes in handy. You’ll want to understand how this works before filtering with multiple conditions. You’ll also want to understand the basic operators in Python. For the purposes of this exercise you just need to know that ‘&’ stands for AND — and that ‘ | ‘ stands for OR in Python. However, with a deeper understanding of all basic operators, you can easily manipulate data with all sorts of conditions. 

Let’s go ahead and work on filtering countries that both start with ‘S’ AND that have a GDP per capita above 50,000.

sand500gdp = gdp[(gdp.gdp_per_capita > 50000) & (gdp.Country.str.startswith('S'))]

Now let’s work on those that start with S OR have over 50000 GDP per capita.

sor500gdp = gdp[(gdp.gdp_per_capita > 50000) | (gdp.Country.str.startswith('S'))]

There we go! We’re well on our way to working with filtered views in Pandas.

MANIPULATE DATA WITH CALCULATIONS

What would Excel be without functions that help you calculate different results?

Pandas in this case leans heavily on the numpy library and general Python syntax to put calculations together. We’re going to go through a simple series of calculations on the GDP dataset we’ve been working on. Let’s for example, calculate the sum total of all GDP per capita countries that are over 50,000.

gdp50000.gdp_per_capita.sum()

That’ll give you the answer of 770046. Using that same logic we can calculate all sorts of things — the full list can be located at the Pandas documentation under the computation/descriptive statistics section located on the menu bar at the left.

DATA VISUALIZATION (CHARTS/GRAPHS)

Data visualization is a very powerful tool — it allows you to share insights you’ve gained with others in an accessible format. A picture, after all, is worth a thousand words. SQL and Excel both have the capability to translate queries into charts and graphs. With the seaborn and matplotlib libraries, you can do the same with Python.

There are far more comprehensive tutorials on data visualization options — a favorite of mine is this Github readme document (all in text) which explains how to build probability distributions and a wide variety of plots in Seaborn. That should give you an idea of how powerful data visualization can be in Python. If you’re ever feeling overwhelmed, you can use a solution such as Plot.ly which might be more intuitive to grasp.

We’re not going to go through each and every data visualization option — suffice it to say that with Python, you’re going to have a lot more power to visualize things than anything SQL can offer, and you’ll have to trade-off the additional flexibility you gain with Python for how easy it is in Excel for generating charts from templates.

In this case, we’re going to build a simple histogram to show the distribution of GDP per capita for those countries that have more than $50,000 in GDP per capita.

gdp50000.hist() 

With this powerful histogram function (hist()) we can now generate a histogram that shows that most of the countries with a high GDP per capita cluster around the $50000 to $70000 range!

GROUPING AND JOINING DATA TOGETHER

Within Excel and SQL, powerful tools such as the JOIN function and pivot tables allow for the rapid aggregation of data.

Pandas and Python share many of the same functions that have been ported over from both SQL and Excel. You’ll be able to group data within datasets and join different datasets together. You can take a look here at the documentation. You’ll find that the join functionality offered by the merge function in Pandas is very similar to the one offered by SQL through the join command, while Pandas also offers pivot table functionality for those who are used to it in Excel.

We’re going to do a simple join here between the table we’ve developed with GDP per capita, and a list of world development indices from the World Bank.

Let’s first import the csv of country-level indicators.

country = pd.read_csv("Country.csv")

Let’s do a quick .head() function to take a look at the different columns in this dataset.

Now that we’re done, we can take a quick look and see that we’ve added a few columns that we can play with, including different years where data was sourced.

Now let’s merge the data:

gdpfinal = pd.merge(gdp,country, how = 'inner', left_on='Country', right_on = 'TableName')

We can now see the table incorporates elements of both our GDP per capita column and our new country-wide table with different data columns. For those familiar with SQL joins, you can see that we’re doing an inner join on the Country column of our original dataframe. 

Now that we have a joined table, we may want to group countries and their GDP per capita by the region of the world they’re in.

We can now use the group by functions in Pandas to play around with the data grouped by region.

gdpregion = gdpfinal.groupby(['Region']).mean()

What if we want to see a permanent view of groupby summation? Groupby operations create a temporary object that can be manipulated, but they don’t create a permanent interface to aggregated results that can be built upon. For that, we’ll have to go through an old favorite of Excel users: the pivot table. Fortunately, pandas has a robust pivot table function.

gdppivot = gdpfinal.pivot_table(index=['Region'], margins=True, aggfunc=np.mean)

gdppivot

You’ll see we’ve picked up some extra columns we don’t need. Fortunately, with the drop function in Pandas, you can easily delete several columns.

gdppivot.drop(['LatestIndustrialData', 'LatestTradeData', 'LatestWaterWithdrawalData'], axis=1, inplace=True)

gdppivot

Now we can see that the GDP per capita differs depending on the regions in different parts of the world. We have a clean table with the data we want.

This is a very superficial analysis: you’d want to actually do a weighted mean since a GDP per capita for each nation is not representative of the GDP per capita of every nation in a group since populations differ across the nations within a group.

In fact, you’ll want to redo all of our calculations involving means to reflect a population column for each country! See if you can do that within the Python notebook you’ve just started. If you can figure it out, you’ll have been well on your way to transferring your SQL or Excel knowledge to Python. 

Got any comments or questions? Please leave them in the comments section on this blog post 🙂 

Learning Lists, Uncategorized

Learn Machine Learning With These Six Great Resources

Learn Machine Learning 

A friend of code(love), Matt Fogel is doing awesome things with machine learning at fuzzy.io. He’s shared this valuable list of resources to learn machine learning that he usually gives his friends who ask him for more information.

You’ll see his original post here: https://medium.com/@mattfogel/master-the-basics-of-machine-learning-with-these-6-resources-63fea5a21c1c#.ta2bhsq8y

Learn machine learning with code(love)

Learn machine learning with code(love)

Great blog posts, podcasts and online courses to help you get started

It seems like machine learning and artificial intelligence are topics at the top of everyone’s mind in tech. Be it autonomous cars, robots, or machine intelligence in general, everyone’s talking about machines getting smarter and being able to do more.

Yet for many developers, machine learning and artificial intelligence are dense terms representing complex problems they just don’t have time to learn.

I’ve spoken with lots of developers and CTOs about Fuzzy.io and our mission to make it easy for developers to start bringing intelligent decision-making to their software without needing huge amounts of data or AI expertise. A lot of them were curious to learn more about the greater landscape of machine learning.

You can describe machine learning as using techniques to help computers learn new ways of uncovering insights from data. This deep dive into the topic will explore many elements outside of this short guide if you’re interested in learning more.

What you need to understand before you learn machine learning is that it’s not a magic buzzword that will help solve every problem with you. Machine learning is a practical way to get more data insights with less work. Nothing more, nothing less. 

To quote a professor in the field, “Machine learning is not magic; it can’t get something from nothing. What it does is get more from less. Programming, like all engineering, is a lot of work: we have to build everything from scratch. Learning is more like farming, which lets nature do most of the work. Farmers combine seeds with nutrients to grow crops. Learners combine knowledge with data to grow programs.”

If that excites you, here are some of the links to articles, podcasts and courses about machine learning that I’ve shared with my friends who were eager to learn more. I hope you enjoy!

Learn machine learning with code(love)

Learn machine learning with code(love)

1A Gentle Guide to Machine Learning

This guide, written by the awesome Raul Garreta of MonkeyLearn, is perhaps one of the best I’ve read. In one easy-to-read article, he describes a number of applications of machine learning, the types of algorithms that exist, and how to choose which algorithm to use.

2A Visual Introduction to Machine Learning

This piece by Stephanie Yee and Tony Chu of the R2D3 project gives a great visual overview of the creation of a machine learning model that determines whether an apartment is located in San Francisco or New York based on the traits they hold. It’s a great look into how machine learning models are created and how they work in practice.

Podcasts

3Data Skeptic

A great starting point on some of the basics of data science and machine learning. Every other week, they release a 10–15 minute episode where the hosts (Kyle and Linhda Polich) give a short primer on topics like k-means clustering, natural language processing and decision tree learning. They often use analogies related to their pet parrot, Yoshi. This is the only place where you’ll learn about k-means clustering via placement of parrot droppings.

4Linear Digressions

This weekly podcast, hosted by Katie Malone and Ben Jaffe, covers diverse topics in data science and machine learning. They teach specific advanced concepts like Hidden Markov Models and how they apply to real-world problems and datasets. They make complex topics extremely accessible, and teach you new words like clbuttic.

Online Courses

5Intro to Artificial Intelligence

Plan for this online course to take several months, but you’d be hard-pressed to find better teachers than Peter Norvig and Sebastian Thrun. Norvig quite literally wrote the book on AI, having co-authored Artificial Intelligence: A Modern Approach, the most popular AI textbook in the world. Thrun’s no slouch either. He previously led the Google driverless car initiative.

6Machine Learning

This 11-week long Stanford course is available online via Coursera. Its instructor is Andrew Ng, Chief Scientist at Chinese internet giant Baidu and one of the pioneers of online education. 

This list is really only scratching some of the complex and multifaceted topic that is machine learning.  If you have your own favorite resource, please suggest it in the comments and start a discussion around it!

 

Open News

Shake: The Bitcoin Debit Card Perfect for Travel or Anything Else

If you’ve ever been hit by foreign transaction fees, you’ll probably have remembered your dream trip around the world less fondly.

We live in a global economy, but the infrastructure to deal with it doesn’t seem to have caught up. Financial companies still charge you for the crossing of borders and you’re largely restricted to a set of charge and bank cards you have to collect in your mailbox or go to a branch to get.

Shake: A new Bitcoin debit card

Shake aims to change all of that. You can issue as many cards as you’d like digitally for a variety of expenses by loading them with Bitcoin.

Importantly, you can choose to issue a card in different foreign currencies. Foreign currency charges still apply if you charge a card differently than the currency you issued it in, but since Shake seamlessly allows you to create as many cards as you want, you can simply prevent those charges by making sure that you issue a card for every situation.

You can also choose to receive SMS notifications every time a transaction is approved or denied on your Shake Bitcoin debit card.

This bypasses several financial constraints. You can travel around the world without worrying about foreign transaction fees. You can load your card with Bitcoin, and spend it wherever you want in whatever currency you issue, bypassing stores that don’t accept Bitcoin. Shake allows you to take advantage of NFC (near-field communications) payment technology, the same technology that powers Apple Pay.

Shake uses Visa’s financial infrastructure to back an innovative approach to democratizing the spend of bitcoin that comes with the security and ease of use required for anybody to start spending their money around the world.

I played around with it and figured out that you could issue a Bitcoin debit card with no daily purchase limit. The first tier of cards (dubbed the KYC Level 1) allowes you to issue cards up to a value of $2,500 USD. If you want an unlimited amount, you’ll have to upgrade to the KYC Level 2, though that’s free of charge. The interface was slick and easy to navigate: in other words, nothing like your typical experience with a bank.

While I was there, I thought I glimpsed a bit of the financial future, one where transactions were as seemless and as costless as possible, and one where banks cared about end users in every way. I don’t know if Shake will be a large part of that vision in the future, but I do know they are moving the needle on it, and that given the right moves, the company could help transform financial transactions.

For now though, they’re in Alpha, and Shake is merely your key to unlocking an ultramodern financial system, on-demand–which for most people, may be more than they’ll ever need.

Learning Guides

Python List Comprehension: An Intro and 5 Learning Tips

Python list comprehension: an introduction and 5 great tips to learn

Python list comprehension empowers you to do something productive with code. This applies even if you’re a total code newbie. At code(love), we’re all about teaching you how to code and embrace the future, but you should never use technology just for its own sake.

Python list comprehension allows you to do something useful with code by filtering out certain values you don’t need in your data and changing lists of data to other lists that fit specifications you design. Python list comprehension can be very useful and it has many real-world applications: it is technology that can add value to your work and your day-to-day.

To start off, let’s talk a bit more about Python lists. A Python list is an organized collection of data. It’s perhaps easiest to think of programming as, among other things, the manipulation of data with certain rules. Lists simply arrange your data so that you can access them in an ordered fashion.

Let’s create a simple list of numbers in Python.

numbers = [5,34,324,123,54,5,3,12,123,657,43,23]
print (numbers)
[5, 34, 324, 123, 54, 5, 3, 12, 123, 657, 43, 23]

You can see that we have all of the values we put into the variable numbers neatly arranged and accessible at any time. In fact, we can access say, the fifth variable in this list (54) at any time with Python list notation, or we can access the first 5 and last 5 values in the list.

print(numbers[:5]); print(numbers[-5:]); print(numbers[4])
[5, 34, 324, 123, 54]
[12, 123, 657, 43, 23]
54

If you want to learn more about how to work with Python lists, here is the official Python documentation and an interactive tutorial from Learn Python to help you play with Python lists.

Python list comprehensions are a way to condense Python for loops into lists so that you apply a formula to each value in the old list to create a new one. In other words, you loop a formula or a set of formulae to create a new list from an old one.

What can Python list comprehensions do for you?

Here’s a simple example where we filter out exactly which values in our numbers list are below 100. We start by applying the [ bracket, then add the formula we want to apply (x < 100) and the values we want to apply it to for (x in numbers -> numbers being the list we just defined). Then we close with a final ] bracket.

lessthan100 = [x < 100 for x in numbers]
print (lessthan100)
[True, True, False, False, True, True, True, True, False, False, True, True]
#added for comparision purposes
[5, 34, 324, 123, 54, 5, 3, 12, 123, 657, 43, 23]

See how everything above 100 now gives you the value FALSE?

Now we can only display which values are below 100 in our list and filter out the rest with an if filter implemented in the next, which is followed by the if trigger.

lessthan100values = [x for x in numbers if x < 100]
print(lessthan100values)
[5, 34, 54, 5, 3, 12, 43, 23]

We can do all sorts of things with a list of numbers with Python list comprehension.

We can add 2 to every value in the numbers list with Python list comprehension.

plus2 = [x + 2 for x in numbers]
print (plus2)
[7, 36, 326, 125, 56, 7, 5, 14, 125, 659, 45, 25]

We can multiply every value by 2 in the numbers list with Python list comprehension.

multiply2 = [x * 2 for x in numbers]
print(multiply2)
[10, 68, 648, 246, 108, 10, 6, 24, 246, 1314, 86, 46]

And this isn’t just restricted to numbers: we can play with all kinds of data types such as strings of words as well. Let’s say we wanted to create a list of capitalized words in a string for the sentence “I love programming.”

codelove = "i love programming".split()
codelovecaps = [x.upper() for x in codelove]
print(codelove); print(codelovecaps)
['i', 'love', 'programming']
['I', 'LOVE', 'PROGRAMMING']

Hopefully by now, you can grasp the power of Python list comprehension and how useful it can be. Here are 5 tips to get you started on learning and playing with data with Python list comprehensions. 

1) Have the right Python environment set up for quick iteration

When you’re playing with Python data and building a Python list comprehension, it can be hard to see what’s going on with the standard Python interpreter. I recommend checking out iPython Notebook: all of the examples in this post are written in it. This allows you to quickly print out and change list comprehensions on the fly. You can check out more tips on how to get the right Python setup with my list of 11 great resources to learn and work in Python.

2) Understand how Python data structures work

In order for you to really work with Python list comprehensions, you should understand how data structures work in Python. In other words, you should know how to play with your data before you do anything with it. The official documentation on the Python website for how you can work with data in Python is here. You can also refer again to our resources on Python.

3) Have real-world data to play with

I cannot stress enough that while a Python list comprehension is useful even with pretend examples, you’ll never really understand how to work with them and get things done until you have a real-world problem that requires list comprehensions to solve.

Many of you came to this post with something you thought list comprehensions could solve: that doesn’t apply to you. If you’re one of those people who are looking to get ahead and learn without a pressing problem, do look at public datasets filled with interesting data. There’s even a subreddit filled with them!

Python list comprehension with code(love)

Real-world data with code(love)

4) Understand how to use conditionals in list comprehensions

One of the most powerful applications of Python list comprehensions is the ability to be able to selectively apply different treatments to different values in a list of values. We saw some of that power in some of our first examples.

If you can use conditionals properly, you can filter out values from a list of data and selectively apply formulas of any kind to different values.

The logic for this real-life example comes to us from this blog post and Springboard’s Data Science Career Track.

Imagine you wanted to find every even power of 2 from 1 to 20.

In mathematical notation, this would look like the following:

A = {x² : x in {0 … 20}}

B = {x | x in A and x even}

square20 = [x ** 2 for x in range(21)]
print(square20)
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400]
evensquare20 = [x for x in square20 if x % 2 == 0]
print (evensquare20)
[0, 4, 16, 36, 64, 100, 144, 196, 256, 324, 400]

In this example, we first find every square power of the range of numbers from 1 to 20 with a list comprehension.

Then we can filter which ones are even by adding in a conditional that only returns TRUE for values that when divided by 2 return a remainder of 0 (even numbers, in other words).

We can then combine the two into one list comprehension.

square20combined = [x ** 2 for x in range(21) if x % 2 == 0]
print(square20combined)
[0, 4, 16, 36, 64, 100, 144, 196, 256, 324, 400]

Sometimes, it’s better not to do this if you want things to be more readable for your future self and any audience you’d like to share your code with, but it can be more efficient.

5) Understand how to nest list comprehensions in list comprehensions and manipulate lists with different chained expressions

The power of list comprehensions doesn’t stop at one level. You can nest list comprehensions within list comprehensions to make sure you chain multiple treatments and formulae to data easily.

At this point, it’s important to understand just what list comprehensions do again. Because they’re condensed for loops for lists, you can think about how combining outer and inner for loops together. If you’re not familiar with Python for loops, please read the following tutorial.

This real-life example is inspired from the following Python blog.

list = [(x,y) for x in range(1,10) for y in range(0,x)]
print(list)
[(1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2), (4, 0), (4, 1), (4, 2), (4, 3), (5, 0), (5, 1), (5, 2), (5, 3), (5, 4), (6, 0), (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (7, 0), (7, 1), (7, 2), (7, 3), (7, 4), (7, 5), (7, 6), (8, 0), (8, 1), (8, 2), (8, 3), (8, 4), (8, 5), (8, 6), (8, 7), (9, 0), (9, 1), (9, 2), (9, 3), (9, 4), (9, 5), (9, 6), (9, 7), (9, 8)]

If we were to represent this as a series of Python for loops instead, it might be easier to grasp the logic of a Python list comprehension. As we move from the outer loop to the inner loop, what happens is that for each x value from 1 to 9 (for x in range(1,10)), we print out a range of values from 0 to x.

for x in range(1,10):
    for y in range(0,x):
        print(x,y)
1 0
2 0
2 1
3 0
3 1
3 2
4 0
4 1
4 2
4 3
5 0
5 1
5 2
5 3
5 4
6 0
6 1
6 2
6 3
6 4
6 5
7 0
7 1
7 2
7 3
7 4
7 5
7 6
8 0
8 1
8 2
8 3
8 4
8 5
8 6
8 7
9 0
9 1
9 2
9 3
9 4
9 5
9 6
9 7
9 8

The chain of for loops we just went over has the exact same logic as our initial list comprehension. You’ll notice though that in a for loop, you will print seperate values while in a list comprehension it will produce a new list, which allows us to use Python list notation to play with the data.

With this in mind, you can make your code more efficient and easily manipulable with a Python list comprehension.

I hope you enjoyed my introduction to Python List Comprehensions. If you want to check out more content on learning code, check out the rest of my content at code-love.com! Please comment if you want to join the discussion, and share if this created value for you 🙂

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