Category Archives: Learning Lists

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 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!

 

Learning Lists

11 Great Resources to Learn and Work in Python

Python is one of our favorite languages at code(love). Versatile, and yet easy to grasp, it’s one of the best languages at expressing the logic behind code with a simplicity that is sometimes breathtaking in its elegance.

If you happen to be more practical, Python always ranks among one of the programming languages that draws the highest median annual salaries, hovering around the magic $100,000 USD mark.

Despite how simple it is, Python is also surprisingly powerful. It can help introduce you to the basics of machine learning, it can slice and dice relatively big datasets for you, and it can even help you build entire web platforms. Pinterest often uses Python to serve millions of images around the world.

The language itself grows ever more versatile with its community. If you want to join this healthy, vibrant network of builders and learn how to do awesome things with Python, you’ve come to the right place. Here are eleven places you should start.

Python is poetry

Python is poetry

1-Read about Python

Learn Python the Hard Way was my first introduction to Python and several programming concepts. Author Zed A. Shaw made the book accessible online for free, but he has a special place in his heart and inbox for people who pay the small sum of $29.95. The practical exercises within are well worth going through. Make sure you write out as much of the code as possible: it’s only through mastery of the basics that you can become an expert.

2-Watch Python Videos

If you’re more of a visual learner, you can learn about the fundamentals of using Python for the web with this excellent free Udacity course. Of course, there’s more where that came from, with a variety of courses from everything to data fundamentals in Python to machine learning. I went through the series myself, and though it’s a bit long (and there are a lot of exercises that I didn’t think added that much value), the end result was that I came out of the tutorial with a deeper understanding of how data moves across the web.

You can also catch plenty of Python videos on Coursera, Treehouse and Udemy.

Udacity with code(love)

Udacity with code(love)

3- Look through lists of Python Learning Resources

This might be a little bit meta, but I love lists of resources. One of the hidden secrets to finding those great resources are going through Github repositories. Github is the Google Docs of code, a great collection of “repositories” where coders can “commit” their code to a shared codebase. It’s also a place where people love compiling great collections of programming resources.

This particular link above is a favorite collection of mine. I hope you enjoy it as much as I do.

4- Anaconda and iPython Notebook

Anaconda and iPython Notebook are what I commonly refer to as the “Excel” of Python. It can be hard to work with the Python interpreter (the command line prompt where you enter Python code if you install it from Python.org) as is. You can’t really refer back to the work that you’ve done before very easily without saving a whole variety of Python files, and it can be pretty hard to share your code with the web at large in HTML form, especially with different charts and graphs and a structured flow you want to convey that goes beyond just one Python script.

iPython Notebooks allow you to write your code in Notebook form.

iPython Notebook with Python

This is what Notebook form looks like.

Python Interpreter with code(love)

This is what the Python interpreter looks like. Source: http://2.bp.blogspot.com/-Duisv8kz1l0/T9q30qpexeI/AAAAAAAAAAM/hxsQB-tLt7E/s1600/python-interpreter.png

Anaconda and iPython Notebook make it intuitive and visually appealing to organize different Python software modules, and bring them together so that you can work and show your results as easily as possible with nbviewer, which generates a HTML version of your Notebooks that you can share on Github. A lot of popular modules we talk about like Pandas are pre-installed, saving you some time. When you click on the next link, you’ll see exactly what it looks like using iPython Notebook.

5-Slice and dice data with Pandas

Built on the aforementioned iPython Notebook, Julia Evans has created a “cookbook” for the Pandas module, a collection of Python code that can help you handle relatively large data sets with ease.

Python can only help you process what you can fit in memory on your computer, but that’s more than enough for most of your data needs. Pandas will help you efficiently process that data: you’ll be able to read from very large CSVs and clean them up so you can find great data insights and visualize them (more on that in point #10!)

6-Build something small with Flask

Flask is what is termed as a micro-framework, a set of code that you can lean on to build small web projects. It has a bunch of reuseable components that help you build interactive websites that can both receive and transmit data. Give it a try: in a few lines of codes, you can get something interactive going on the web!

7-Build something big with Django

If you’re tired of the word micro, and want to go with a full web framework, build something with Django! Django is used to this day to build very large websites including Pinterest, and Instagram.

django with code(love)

Take a bite out of the web with Django!

8-Play around with Python APIs and even more!

We had a list of learning resources before on Github, now we can explore a list of the things that make Python awesome! I especially love using Python to play with Application Programming Interfaces or APIs. APIs are a set of rules for servers to communicate data with one another: what this means is that with Python, you can scrape your personal fitness information from your Fitbit or work with Google Sheets automation easily. You can do anything that involves getting data from a server willing to give it to you.

You’ll find a list of really cool APIs above that will allow you to play with all sorts of cool data!

9-Do some machine learning with Python

Have you heard of machine learning? It’s all the rage today and the reason why is because it allows you to do more with less. By having machines learn patterns in your data and by being able to infer conclusions from smaller data sets to larger populations with their insights, machine learning lets you know more about the world around you with less data points.

This Github repository offers a fantastic dive into the fundamentals of machine learning, and gets you to practically embark on your machine learning adventure with sample code sets.

10-Tell data stories with Plotly

Data doesn’t mean anything unless you can storytell with it. You can throw all the numbers in the world at people but it won’t mean they’re any closer to understanding your point. You really have to break down your data into meaningful chunks for it to go anywhere.

Thankfully, Plot.ly can help with that. With a few lines of Python, you’ll be well on your way to doing bar graphs, charts, and figures of all kinds.

Plotly with code(love)

An example of what you can do with Plotly!

11-Do coding challenges in Python

Now that you’re done learning all of the fun stuff in Python, it’s time to put yourself up to the test! Use HackerRank challenges to test your skills: you could even get a job out of it!

HackerRank allows you to complete problems in the coding language of your choice and allows you to demonstrate your skill with clean code that solves problems in a short amount of time.

Python is a wonderful language for programming beginners, and powerful enough to explore multiple areas of data, machine learning, artificial intelligence and other advanced computer science concepts. It’s the perfect mix for anybody who is getting into programming or who wants to develop their skills further. With these resources you’ll be able to learn and work in Python!

Share this list of resources if it can help somebody–and let me know what else could be added to this list in the comments 🙂

Source for featured image: http://www.slideshare.net/audreyr/python-tricks-that-you-cant-live-without

Learning Lists

Nine free, brilliant resources to learn data mining

I’m a big fan of playing with data.

In my earlier corporate life, I often used Excel to look through thousands of lines of spreadsheet goodness. I assumed what I was doing was “big data”, and I prided myself on my association with a trendy buzzword.

I know better now. A lot better.

If you’ve ventured here, you’re probably looking into data science, the mysterious science that seems to verge on mysticism in the press. The virtues of data are constantly praised as innovative and disruptive. They seem like the domain of an exclusive few practitioners lifting numbers into actionable insight.

Harvard Business Review went as far as to saying that the data scientist was the sexiest job of the 21st century.

It seems that data scientists create many of the most exciting projects at the cutting-edge of technology. The people you may know on LinkedIn appear thanks to data mining. Amazon’s book recommendations rely on computers to mine your book preferences and select the one book that is most likely to appeal to you. Facebook finds what posts you like, and serves you more of the same. Google finds out who you are, and filters search results and ads for you.

If I like computers, the search term Python will return me the programming language. If I like snakes, it will return me a whole bunch of snakes.

This is all down to the magic of data mining. You’re here because you want to look behind the veil and learn how to do all this.

It’s hard, but not as hard as you think. Data science, at its’ core, is all about using computing power to parse through huge data sets.

Learn Data Mining with code(love)

Learn Data Mining with code(love)

Here are nine free, brilliant resources to do just that.

1- Coursera’s Specialization in Data Mining (level: beginner) 

https://www.coursera.org/specialization/datamining/20

Coursera brings the best from the University of Illinois at Urbana-Champaign, ranked in the top 5 for computer science schools in America. It’s a useful introduction to data mining–the application of data science and computing power to find patterns in large collections of data.

2- A UCLA professor’s overview of data mining (level: beginner)

http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm

This blogpost delves deep into the specifics of data mining. It provides an overview and a set of definitions that will help bring you up to scratch.

3-Introduction to R (level: beginner)

https://www.codeschool.com/courses/try-r

The coding language R is the workhorse of scientific data analysis and visualization. Codeschool offers an interactive and gamified approach to learn it, similar to Codecademy. Working with R will give you insight into how to move and dance with digital data, a skill that is the foundation of data science.

4- Kaggle’s Wiki on Python (level: beginner) 

https://www.kaggle.com/wiki/GettingStartedWithPythonForDataScience

Kaggle is a platform for crowdsourced data challenges. The website has a ton of resources on how to get started with data science. This particular link leads to their guide on Python, one of the most versatile programming languages for data analysis.

5- Data Science 101 (level: beginner)

http://101.datascience.community/

This blog knows how to describe itself: “Data Science 101 is about learning to become a data scientist.” Simple, clear and to the point.

6- W3’s Tutorial on SQL (level: beginner) 

http://www.w3schools.com/sql/

W3 hosts a bunch of interactive tutorials on the basics of programming. This set of tutorials goes through SQL, a language that allows you to access data from most web databases. The tutorials will give you a glimpse into how data is structured for many websites and they will give you enough knowledge so that you would know how to play with data.

7-Horton’s Hadoop Sandbox (level: intermediate)

http://hortonworks.com/products/hortonworks-sandbox/

Have you ever wanted to play with big data? Learn the basics here and experiment with them. Hadoop helps distribute data across multiple servers, helping to process large amounts of data as seemlessly as possible.

8- Machine Learning on Coursera with Andrew Ng (level: intermediate)

https://www.coursera.org/course/ml

Learn about data mining and the algorithms you can create to make your data analysis job so much easier from a master in the field: the founder of Coursera Andrew Ng, a Stanford professor who has recently become Baidu’s chief scientist.

9- A Programmer’s Guide to Data Mining (level: advanced) 

http://guidetodatamining.com/

If you can work with Python at a proficient level, this book will help you implement different algorithms that will sort, filter, and manipulate your data for you. A must-read for people looking into the practical applications of data mining.

I hoped that helped get you set on the path to data mining. What resources do you think I’m missing? Comment below. 🙂

Learning Lists

Learning Artificial Intelligence

Last year, my partner and I designed Gump, a Voice-Commanded Bipedal Robot for our Engineering Graduation Project. Gump won first place in the Engineering Faculty for Best Project and is currently featured in Engineering News Magazine at Concordia.

The moment we saw our robot walk on two legs for the first time and respond to the sound of our voice was miraculous.

That moment is when we knew we wanted to work on artificial intelligence for the rest of our lives.

Learning Artificial Intelligence with code(love)

Learning Artificial Intelligence with code(love)

That moment is when we knew we wanted to work on artificial intelligence for the rest of our lives.

As technology grows, so does the information that is available to us.

We began working on artificial intelligence in the NLP (Natural Language Processing) domain, something that allowed machines to understand and process human language patterns. We worked on speech recognition and now we are doing text analysis using artificial intelligence.

We’ve learned that the possibilities of using artificial intelligence are endless. Learning artificial intelligence is crucial to understanding how the 21st century will unroll—Chris Dixon, a prominent venture capitalist, argues that the next 10,000 startup ideas are clear: take x and add artificial intelligence to it.

With artificial intelligence, you can get unlimited insights from Big Data. Imagine measuring the mood of iPhone Users on Twitter after Apple’s latest product launch, gauging the world’s opinion on a major event like Russia’s invasion of the Ukraine, or predicting a company’s performance based on previous financial data, without any need for a human to relay that information to us.

Imagine a world where machines can automatically process information, and present it to use in a relatable and relevant form. This is what artificial intelligence proposes.

Artificial intelligence can help us all be more informed in less time, allowing us to make quicker decisions by getting the insights and analytics we need from Big Data.

Learning Artificial Intelligence

Learning Artificial Intelligence

Because of all this, I’m excited about artificial intelligence. I want you to be as excited as I am, so I’ve decided to highlight some ways to get started with artificial intelligence using resources available online

1-NLTK with Python

NLTK stands for Natural Language Tool Kit, an open source library with lot’s of functions to help you use NLP and artificial intelligence. to solve a problem or work on a project. You must know Python to get started with NLTK. To begin your path, I recommend the Coursera class offered by Stanford University. You might want to brush up on your Python skills with this great book for Python and NLTK beginners and then level up and learn more about Natural Language Processing by looking at this book.

2- IBM Watson Sandbox

Watson is IBM’s artificial intelligence., famous for defeating two human contestants on Jeopardy by having only the Wikipedia database to answer questions. It is considered the gold standard for artificial intelligence nowadays. IBM released a sandbox version for using Watson’s APIs for 30 days, which allows you to hack away and create a cognitive application.

If you want to use Watson for your next big thing, you won’t be able to unless you’re a Series B Funded startup, but that will change in the future. The access IBM has given to Watson now bodes well for the future. The ecosystem is still evolving, but it is the gold standard of artificial intelligence, and IBM is working to make it more accessible.

3- Udacity Courses

Sign up for the free courses on Udacity for Introduction to Artificial Intelligence, and the three Machine Learning Courses offered there. There are tons of resources and a community there to answer all your questions. We personally believe that Machine Learning should be a mandatory course for every Software Engineer and Computer Science Major.

Check out the forums as well, they’re very insightful and they will help guide you step by step.

4- MATLAB’s Neural Network Toolbox

This one is costly, but if you can get a MATLAB license with Neural Network Toolbox license as an add on, you have a very powerful system to play with. NNT is an Artificial Neural Network that has amazing capabilities for your project like speech recognition, image recognition, and object detection. We loved playing with NNT for our robot, Gump.

With a few lines of code, you can create artificial “neurons” in your program. The neurons can be assigned to do several things. We created eight neurons with MATLAB, each one assigned to a specific voice command. After that, we trained the neurons by recording over 5,000 voice samples from every person we could find on campus. The more voice recordings each neuron had per voice command, the more it grew in knowledge. When we tested it, the response was just perfect! MATLAB is immensely powerful.

Those resources will help you get started with learning artificial intelligence.

Leave comments below if you found this helpful, or know of other resources!

This post was written by Yaz Khoury, the founder of an A.I startup called Summarit that uses artificial intelligence to summarize articles for students.

Learning Lists

Ten curated resources for you to learn code and entrepreneurship.

Imagine a world where you could access information as easily as you could breathe.

You can stop imagining: this is the world we live in.

With Google, almost everything can be a finger tap away. With the right keywords, you can access the right information.

The challenge now isn’t a lack of information—it’s how to access that information in a curated fashion.

In that sense, Github, the hub for open source software has become a good way to organize information. By modifying the README files typically used to document how software is used into a list or a resource itself, the open source movement is applying yet another twist to how it can leverage existing resources in new ways to solve old problems.

It is innovation in action. The best part of it is that you can contribute even if you’re non-technical by getting an account, and making pull requests that change the text: you update the text how you will, and then you can push the changes to moderators who will look over your proposed changes, or reject them.

Here’s a guide on how to go about doing that:

https://help.github.com/articles/creating-a-pull-request

Now to take a look at the resources that have been assembled for you to learn code and entrepreneurship.

Entrepreneurship

https://github.com/athivvat/Startup-Resources: A list of startup resources that’ll help you get your feet set to build something.

https://gist.github.com/ndarville/4295324: A list of digital business models, along with a comparision to a company or startup known to be using that strategy.

Code

https://github.com/bayandin/awesome-awesomeness: An overarching framework of most of the coding resources on Github, including a bunch of resources on technical topics.

https://github.com/gloparco/Master-List-of-HTML5-JS-CSS-Resources: A special list for HTML/CSS/JS resources.

https://github.com/sorrycc/awesome-javascript: A comprehensive overview of all things Javascript.

https://github.com/vinta/awesome-python: A list of the Python frameworks you can use.

https://github.com/akullpp/awesome-java: A similar list as awesome-python, this time for Java frameworks.

https://github.com/vhf/free-programming-books: An awesome curated list of free programming books.

https://github.com/bolshchikov/js-must-watch: A list of the videos you have to watch to really get Javascript.

https://github.com/dypsilon/frontend-dev-bookmarks: A list of resources a front-end developer has bookmarked over many years.

What are some awesome resources you’ve seen on Github? If I’m missing any, let me know in the comments below 🙂

If learning lists are your thing, check out the rest of them on code(love)!

Learning Lists

Seven Free Resources You Need to Learn Javascript

Last time I wrote about learning code, I talked at length about what the best coding language to learn for you was, going through the pros and cons of a few languages, and giving use cases of each one. Without delving into too many spoilers—you should read the piece for all of the insights—Javascript was mentioned heavily.

Javascript has been really big, especially because of the evolution of the MEAN stack, which has allowed for Javascript to control how users view your site’s information (Angular), how you host your site (Node), how your site communicates information (Express) and how it stores it (Mongo). It’s become really popular with startups—in fact as you can see from CB Insights, 81% of billion-dollar startups use Javascript in their technology. It is the top coding language used by successful startups.

Learn Javascript with code(love)

It’s a language that can get you hired, and help you build great new ventures.

I’ve recently been really big on wanting to learn Javascript, so I’ve unleashed these resources. They’re a diverse group, suited to all types of people who want to learn Javascript in different ways.

One cautionary note: as useful as Javascript can be, it may not be the best first programming language to learn. It has a lot of little traps in it that can trip even veteran programmers. If you are an absolute beginner, you may want to check out some more general resources oriented around other languages rather than trying to learn Javascript, such as these.

1- Codecadamy Javascript Track (type: interactive, level: beginner)

What’s not to like about learning by doing? By following the Javascript track of Codecadamy’s interactive courses, you can get the basics of Javascript by working out how to create functions, and build things with it. It’s a great sandbox to learn in—in fact, it was how I first picked up coding.

2-Eloquent Javascript (Type: book, level: beginner)

Still can’t get over learning through books? I can’t blame you. I was never the biggest fan of school, but there is something comforting about having a lot of pages devoted to something.

Eloquent Javascript is a free book that has been converted into HTML format for easy reading. It goes through everything you need to learn Javascript from beginning to end. It’s quite well-written, and has a lot of relevant examples and images to break the text up—it’s a book that really gets at you and challenges you to learn Javascript.

3-LearnJS (type: interactive, level: intermediate)

More learning by doing. I really like resources like this that get at you and challenge you to do stuff. In this case, LearnJS features interactive modules where you are challenged to finish incomplete code so that it matches a desired output. In doing so, you can learn how to use Javascript to do what you want it to do.

4-Scotch.io (type: blog, level: advanced)

I picked up on Scotch.io when I was looking for resources on how to build single-page web applications. The place is a hive of how-tos and resources on how to build with Javascript and its frameworks.

5-Egghead.io (type: video, level: advanced)

I’ve been following the Egghead video series on Angular to learn the framework: they’ve been a breath of fresh air for my learning. Angular.JS is a Javascript framework that allows you to control a lot of what a website visitor would see, from filtering information, to allowing buttons to toggle settings on and off. It’s the framework I’ve been focused on learning. Having so much content organized about it in a coherent and sequential fashion warms my heart—and it will warm yours as well.

6-JSFiddle (type: sandbox, level: beginner)

Whenever you feel the need to play around to learn Javascript, JSFiddle is the easiest way. Plug your code into the module, and watch it come to life with no limits!  I use it to test what some websites will look and feel like without the need of hosting and uploading changes. It’s a great experimental space to see what your code would look like live.

7-Plunker (type: sandbox, level: intermediate)

Similar to JSFiddle, except now you can manage separate pages, which has made it really useful for testing more complex frameworks for Javascript such as Angular.JS. My go-to learning tool these days as I combine that with Egghead for maximum learning.

There you go. The choice is in your hands to build something great now with Javascript. These resources will help along the way.

If you want more resources to learn, check out our other learning lists!

 

Learning Lists

The best coding language for you to learn.

A few people have asked me what would be the most useful or best coding language to learn.

Skipping aside HTML/CSS—I think the answer rests on what you want to do with code.

Javascript and its frameworks are really useful for building something with just one language.

Angular.js can control the front side of the website that displays to your users, Node.js will act as a web server that can host all of your content, Express.js runs in the middle directing where information goes, and MongoDB acts as the storage center for data you accumulate from your users—the MEAN (Mongo/Express/Angular/Node) technology stack—an organizing framework that helps build everything you’d need for a web application—is the one favoured by a whole lot of startups these days. It’s a whole component of technologies that can build everything web-wise based on one language.

I’ve been using Egghead.io and Scotch.io to catch up on my Angular and MEAN stack skills.  Egghead is focused on video tutorials that are structured sequentially, Scotch has some great graphics about the whole process of building web apps, including the following explaining the MEAN stack.

MEAN Stack from scotch.io with code(love)

MEAN Stack from scotch.io with code(love)

They’ve got great tutorials on how you can go about building nifty applications such as basic search engines, and new ways to validate forms (making sure that when you create input forms, people are actually putting in valid criterion). With Angular itself, you can animate a website and make it move, with not too much in terms of setup, which is pretty nifty.

Python is very readable and legible, and has recently become the introductory language of choice for universities teaching computer science majors. It’s fantastic for playing around with data, and doing all sorts of nifty things you wouldn’t have thought possible with its various community modules, such as scraping web pages in their entirety, and doing advanced scientific data analysis. I started out with Learn Python, which suited my fashion of learning by doing.

Java and lower-level languages (languages that are closer to interacting with computer hardware) that are a bit more difficult to interpret for human eyes are wonderful for understanding more of how code actually works—and how you’re interacting with the computer. Java is also something that is used for mobile development on the Android ecosystem, which is something that will always be in demand.

If we want to switch briefly from knowledge to money, I’ve seen a lot of demand for iOS developers, and Objective-C and SWIFT aren’t that hard to pick up. Ruby, especially when used in conjunction with Rails, is also something a lot of startups are building on for which the learning curve isn’t that high (in fact, there was a children’s book for Ruby).

I myself am personally learning Python for playing with data, Javascript and the MEAN stack for building web applications, and Java for a deeper understanding of computer science, and building things for mobile, which I think is a well-balanced set of languages carrying forward. I’ve got together a bunch of learning lists, and resources to help me and you learn what we need to build great things. But none of these are the best coding language to learn.

The best coding language to learn—and how to go about doing it.

The absolute best thing to learn is to learn how to think like a programmer—learn how to solve problems mathematically, with clean and concise code. Coding languages evolve, they change, they fall in and out of favor. One community might morph into another. The great web applications of the present might be obsolete in a few decades. What won’t change is the need for people to think logically, and solve problems—and make it an automated and easier process with machines.

You can bank on the fact that going forward, if you practice your problem solving skills, you’ll be able to find your best language, and get the knowledge and money you need to build great ventures.

I’ve been opening up Project Euler, a set of programming math and logic problems, and using the Codecademy workspace in Python to try to create clean code to solve these problems. This was something a Google recruiter mentioned as being a great training step to learning code—and I don’t doubt it. I feel sharper and more confident in my ability not only to code—but to think.

The best language to learn is ultimately the language of logic, math, and problem-solving that is at the core of code. What are your thoughts?

Learning Lists

Five things you should know before you learn code.

Download / By Kamil Lehmann

1-Organization

I wish I knew that there should be an organized way to approach learning code, and that learning code wasn’t just about learning in isolation—it is about building knowledge upon knowledge.

I wouldn’t have tried to learn more complex languages like Python before learning about HTML/CSS, the foundation of the web.

You should know about sites like Codeacademy which organize code learning in a structured, and fun fashion. You should know about Bentobox, something that offers you a structured plan to approach learning code.

2-Free resources

I wish I knew just how many free resources were out there to learn code. It would have helped me get a sense of what learning could be done, and where I could go.

You should take a look at things like reSRC, an online directory of free resources to learn code, and this list of 31 free resources to learn how to code.

3-Frameworks

I wish I knew that a lot of coding was built around frameworks, coding templates which set the foundation for easier coding. I wish I knew that one of the cardinal rules of coding was “Don’t Repeat Yourself”—and that means that if someone has built a solution already, go ahead and use it.

Frameworks make coding easier. They build a foundation that you can wrap around your code and play with—invaluable if you’re just beginning to learn how to code.

You should take a look at frameworks such as JQuery, which simplifies interactive elements of a website, and Bootstrap, which simplifies how you style a website.

4-Mentors

I wish I knew just how valuable it was having somebody around who knew what they were doing. When I got stuck, I finally approached some programmers I knew, and they helped me immensely.

You should look for mentors or programs like Ladies Learning Code where you are connected with some.

5-Learning by doing

I wish I knew just how much easier learning code would be if I thought about building projects, and getting my code to fit those practical applications.

Nothing beats struggling through Q and A forums like StackOverflow, looking desperately for the right answer and finding it. The learning you’ll get will flow naturally.

You should look for a great idea, and try to build something to learn code. You’ll be adding to the foundation of the Internet, while learning at the same time.

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These are the things I wish I knew about learning code before I embarked on my journey. It’s far from complete, but looking back, any one of these steps would have helped me learn faster, and would’ve gotten me to be where I want to be in the future—now.

Getting the learning right allows you to build the future you envision, giving you a voice in the participatory process that is the modern digital economy. It empowers you to build what you can: getting it right can mean the difference between the ideas you see through to fruition , to those you have seen languish behind. Don’t hesitate to start now.