Some more programming gems to make you think, reflect, and hopefully laugh a bit.Continue reading
This is a guest post from Sujit Kumar. If you want to contribute guest posts to code(love), email [email protected]
What is Gatsby?
Why use Gatsby?
- Unlike dynamic sites which render the pages on demand, static site generators pre-generate all the pages of the website.
- No more live database querying and no more running through the template engine each time you load a page.
- Performance goes up and maintenance cost goes down.
- Using Gatsby means you can host the CMS in-house and publish the content generated by Gatsby as a static website.
It’s always good to increase the performance of Angular and React applications. This is one way you can do it.
GatsbyJS covers all the buzzwords out there like ReactJS, GraphQL, WebPack etc, but the coolest part is that you’re up and running in no time!
Since Gatsby is built on React you straight away get all the things we love about React, like composability, one-way binding, reusability and a great environment.
Gatsby makes Drupal work as a backend which means that we can get a modern stack frontend and complete static site with Drupal as a powerful backend.
Set up Drupal
- You have to install and configure the JSON API module for Drupal 8.
- Assuming you already have a Drupal 8 site running, download and install the JSON API module.
- Composer require drupal/JSON API
drupal module: install JSON. Or install it manually on Drupal 8 sites.
- Next, we must ensure that only read permission is granted to anonymous users on the API. To do this, go to the permissions page and check the “Anonymous users” checkbox next to the “Access JSON API resource list” permission. If you skip this step, you’ll get an endless stream of 406 error codes.
After this, you should be all set. Try visiting http://yoursite.com/jsonapi and you should see a list of links.
Now we need to work on Gatsby. The first thing we need to do is install the Gatsby client. If you don’t have it installed already, run this through NPM to grab it:
npm install --global gatsby-cli
That’ll give you the “Gatsby” cli tool, which you can then use to create a new project, like so:
gatsby new my-gatsbyjs-app
That command basically just clones the default Gatsby starter repository and then installs its dependencies inside it. Note that you can include another parameter on that command which tells Gatsby that you want to use one of the starter repositories, but to keep things simple we’ll stick with the default. Now if we look at the project we can see a few different directories.
ls -la my-gatsbyjs-app/src/
The pages directory contains the pages. Each file becomes one page and the name is based on the file name. Each of these files contains a react component.
This is the index.js that we just created.
The Layout directory contains a layout that wraps our pages. These layouts are higher order react components that allow defining common layouts and how they should wrap the page. We can place our page where ever we want within the layout using the prop children.
Let’s look at a simple layout component
As you can see, our layout component takes two props.
One is children prop, where the page is wrapped by us.
The second prop is the data. This is actually the data we fetch with the GraphQl query that is at the end of the code snippet – which in this example fetches the title from the gatsby-config.
The last directory is the components. It is used for creating general components. Fire up the newly generated site.
To run the development mode of the site and to get a Rough idea, run the command:
#> DONE Compiled successfully
We’re now up and running! See for yourself at http://localhost:8000
Once complete, you have the basis for a working Gatsby site. But that’s not good enough for us! We need to tell Gatsby about Drupal first.
For this part, we’ll be using the gatsby-source-drupal plugin for Gatsby. First, we need to install it:
npm install --save gatsby-source-drupal
Once that’s done, we just need to add a tiny bit of configuration for it, so that Gatsby knows the URL of our Drupal site. To do this, edit the gatsby-config.js file and add this little snippet to the “plugins” section:
baseUrl: `http://yoursite.com`, //Drupal site url.
apiBase: `jsonapi`, //This the jsonapi endpoint
You’re all set. That’s all the setup that’s needed, and now we’re ready to run Gatsby and have it consume Drupal data.
Let’s start the development environment to see the Gatsby running.
Run this to get Gatsby running:
If all goes well, you should see some output with gatsby default starter:
You can now view gatsby-starter-default in the browser.
View GraphiQL, an in-browser IDE, to explore your site’s data and schema
Note that the development build is not optimized.
To create a production build, use gatsby build
(If you see an error message instead, there’s a good chance your Drupal site isn’t set up correctly and is erroring. Try manually running “curl yoursite.com/jsonapi” in that case to see if Drupal is throwing an error when Gatsby tries to query it.)
You can load http://localhost:8000/ but you won’t see anything particularly interesting yet. It’ll just be a default Gatsby starter page. It’s more interesting to visit the GraphQL browser and start querying Drupal data, so let’s do that.
Fetching data from Drupal with graphql
Load up http://localhost:8000/graphql in a browser and you should see a GraphQL UI called GraphiQL (pronounced “graphical”) with cool stuff like auto complete of field names and a schema explorer.
Clear everything that is on the left side and start inserting the open curly bracket and it will auto insert the closing curly bracket. Then click ctrl + space to view the auto-complete, which will list the all possible entity types and bundles that we can query.
It should look something like this:
For example, if you want to query Event nodes, you’ll enter “allNodeEvent” there, and drill down into that object.
Here’s an example which grabs the fields (field_task_name, field_date and nid) of the TodoList nodes on your Drupal site:
Note that “edges” and “node” are concepts from Relay, the GraphQL library that Gatsby uses under the hood. If you think of your data like a graph of dots with connections between them, then the dots in the graph are called “nodes” and the lines connecting them are called “edges.”
Once you have that snippet written, press “control+Enter” to run it, and you should see a result like this on the right side:
"field_task_name": "Learn Drupal",
"field_task_name": "Complete drupal task",
"field_task_name": "Learn gatsby",
"field_task_name": "Gatsby Project",
Note the same code will give the data from Drupal which includes the reference data, URIs etc.
Pretty cool right? Everything you need from Drupal, in one GraphQL query.
So now we have Gatsby and Drupal all setup and we know how to grab data from Drupal, but we haven’t actually changed anything on the Gatsby site yet. Let’s change that.
Displaying drupal data on the Gatsby site
The cool thing about Gatsby is that GraphQL is so baked in that it assumes that you’ll be writing GraphQL queries directly into the pages or the components.
In your codebase, check out src/pages/displaynodes.js.
(Note, this assumes you have a node type named “Page”).
All we’re doing here is grabbing the node (task name and task date) via the GraphQL query at the bottom, and then displaying them in a table format.
Here’s how that looks on the frontend:
And that’s it! We are displaying Drupal data on our Gatsby site!
Sujit Kumar is VP of Strategy & Marketing at Valuebound taking care of all aspects of lead generation, company and brand promotion and sales activity. He brings nearly 14+ years of marketing experience, strategic thinking, creativity, and operational effectiveness. Prior to joining Valuebound, Sujit worked in marketing management positions with professional services firms.
I crawled through the Internet and found you these potential gems. Don’t judge them too harshly.
Subtext: O’Reilly really does have awesome programming books.
Subtext: HTML is not a programming language, y’all
Subtext: StackOverflow does have some pretty savage answers.
Subtext: Always write comments for your (imaginary) team members, and most likely, the most important team member of all: future you.
Subtext: This is how I remember the difference between false positives and false negatives for data science.
Subtext: Python installation errors are basically hell. Still waiting for the day where I can “import everything”.
Subtext: XOR confuses me too.
Most people approach me often ask the same question: what’s the best programming language to learn? The answer is: it depends. I wrote an article that declared the mathematical and analytical skills behind programming are what really matter. Now, I’m a bit wiser –so I’ve had the time to break it down into a more tangible and useful answer.
What is the best programming language to learn? It depends, and you can be much more efficient with your time by knowing which programming language is the best for what you want.
So I’ve broken down the best programming language to learn for a variety of needs. I took into consideration the amount of time you need to invest in a programming language and the power you need for different tasks.
You want a versatile, general-purpose language that can be narrowed to different tasks without too much hassle.
Python is a programming ecosystem with a vast array of communities and libraries for different use cases. From Django for web development to Pandas for data, Python is the Swiss-army knife of programming languages. Its syntax is also very approachable, and there are tons of tutorials and documentation for beginners. These libraries tend to be almost like learning a new syntax or paradigm.
Still, the ability to import libraries of different kinds and have a relatively consistent experience puts Python up here. If you want a simple intro-level programming language, Python is a great choice. With the second most active community on Github (at about slightly under 15% of all active users), you’re sure to find many projects and usable components to play with in Python.
This step-by-step tutorial teaches Python in an accessible manner. It makes it easy for you to go through the basics of everything from data structures to how to structure functions. That makes it ideal for people who don’t have programming experience.
This set of tips is a handy primer for not only learning Python, but really a generalizable way to learn and practice all kinds of different programming languages.
The Zen of Python is more philosophical than practical. Still, it serves as a useful reminder of the ideals of Python programming and the ideals one should strive for. Simple, after all, is better than complex.
This free interactive Codecademy course is a great way to start with Python basics and syntax. Use it to cement the theory you’ve learned and start practicing with Python.
Python is versatile mostly because there are tons of documentation and frameworks. Django is a content management system built on Python. This curated curriculum will help you learn what you need to build fully-fledged websites with Python by tapping into Django.
You’re interested in working with data, in a data analysis or data science capacity or as a data engineer/machine learning engineer
When it comes to the data ecosystem, you’ll want to learn SQL as a domain-specific way to work with data. However, SQL is not a general purpose programming language, but merely a utility to deal with one data type over another. You can think of it as a complex interface to the .sql data format.
There are two obvious choices here, R or Python. Academics tend to use R. It used to have the bulk of good data visualization and analytics libraries. Now, however, the open-source Python community has sprinted to catch up. With the advent of machine learning, the balance has shifted towards Python.
Previously, I wrote about both R vs. Python a few years ago. I came out with the conclusion that both had their uses. It was perhaps best to learn both. Practically speaking however, if you’re dealing with large amounts of data ,Python gets the slightest of edges here as the best programming language to learn for data purposes, especially if you’re coming from a programming background in the first place — it’ll be easier for you to work with Python’s syntax than R.
Python and Data Resources:
I wrote this guide describing the differences between R and Python, and listed a bunch of learning resources for both. I concluded it might be best to learn both, but I’ve since become immersed in the Python ecosystem when it comes to data.
I wrote this tutorial which summarized the frameworks and libraries you need to know to get started doing machine learning with different frameworks, most of which have Python ports or APIs so you can write code in Python (or in any case, Pythonic syntax) and get started.
Pandas was where I really started practicing programming: wrangling datasets is a passion of mine. This tutorial walks through how to use Pandas with an example dataset. You’ll learn how to import data of different formats, transform it in different ways, and then extract and export it.
Another tutorial I wrote helps you port some of the logic and functions in both Excel and SQL to Python. Do everything from importing data to analyzing it in the summary form or filtered form you’ve come to expect.
This curated curriculum takes your Python skills and helps you learn machine learning theory. By pairing the two, you can start working on machine learning projects by the end.
You want to build mobile applications that require access to native functionalities such as a phone camera
Depending on what ecosystem you want to build in, the best language is quite selective. If it’s the Android ecosystem, you’re going to have to learn Java.
Meanwhile, if you’re interested in building for the iOS ecosystem and getting placed on Apple’s App Store, you’ll have to learn Swift. Swift is Apple’s official programming language for its laptops based on MacOS, iOS, or for Apple Watch apps.
There are other ecosystems such as Microsoft, which needs C#. There are also cross-platform programming languages such as React Native. Microsoft doesn’t have as much market share as either Android phones or iPhones. Reach Native doesn’t have access to as many of the specific native functions on either device (and you’ll have to compile down to Swift or Java to get those features). Still, they’re handy languages to know about, even if they might not be the best — unless you were trying to launch on as many platforms as possible.
Learn the ins and outs of Android application development, from building an application to how debug common issues.
This interactive set of courses will help you get through the basics of Swift and building iOS applications. You’ll pass to an intermediate stage/course once done.
If video learning is more of your thing, look no further than this series of video tutorials on Swift topics. They’re broken down into sets of continuous playlists, so you can pick and choose a particular curated playlist or choose a particular topic to focus on.
This React Native tutorial and documentation from Facebook is a fairly comprehensive overlook on how the versatile cross-platform framework works.
You want to build the latest web applications
React.js is a powerful framework to create web interfaces. Practice with this Codecademy course.
This tutorial will summarize all of your theory-based learning towards building a MEAN stack application that will serve as a Reddit clone. This is a full-featured web app that has user authentication, databases through MongoDB, routing and linking through Express and a back-end server through Node.js and a combination of Angular (though React can also be used in this situation). At the end of this tutorial, you should be able to extend your learnings and build full-fledged web apps.
You need to do something that requires very high performance (ex: cryptography)
For tasks that require a lot of compute power and manipulation of lower-level processes such as dynamic memory allocation, it’s best to work in C++. Lower-level tasks require more efficient implementations of memory and space and involve working closer to hardware in order to get higher performance. C++ is lower-level than all of the languages discussed about yet is also still readable and compilable enough so that with some practice, you can be conversant in it.
Python can access lower-level functions with something called Cython using the C programming language. Bitcoin is coded in C++, including its advanced cryptographic features. In order to do something at a highly performant level, you’ll likely need to access C++ and its superior lower-level flexibility.
This edX course, provided by Microsoft, will help you get started with C++ and its basics.
This wiki helps you tackle C++ from A to Z. There’s different sections dedicated to everything from how to write functions in the language to how to deal with different variables and types.
Consolidate all of the theory you’ve learned by practicing with this free C++ course with Codecademy.
Cython allows you to access C++ functions while using Python, combining the versatility of the Python ecosystem with the power of C++.
If you want to look into advanced functions such as cryptography, look through this list of C++ cryptography libraries to get you started.
I hope this tutorial has helped you determine what the best programming language to learn for you. If you have any questions, feel free to ask me at [email protected]. Please leave a comment below if you want to give feedback or if you think I’m missing something 🙂
If you’ve heard about quantum computers, you might get the itch to start working on something in the field. What is quantum computing? How do you get started?
Full disclosure: I’m not an expert in the field. I’m just a regular (self-taught) coder. I compiled this tutorial because I was interested in exploring quantum computing. The goal was to define the use cases that made it stand out from classical computing. I also didn’t want to dive too deep into the quantum physics part. Many of the explanations below will be basic, and assume that you have little context in quantum computing.
Also, if this is inartfully explained, or flagrantly wrong, I welcome feedback and will make corrections. And if this is helpful, I appreciate knowing as well 🙂
Introduction to Quantum Computing
Unlike classical computing, quantum computing uses quantum phenomena that intersect with mechanical properties, such as superposition and entanglement. Binary code stores data in either a definite 0 or definite 1 state. Quantum computing uses qubits: bits of data that can coherently rest in a combination of 0 or 1 state probabilities. A qubit can theoretically hold more data than a classical bit. Unfortunately, it is impractical to store a large amount of information in a qubit due to how measurement disturbs a quantum system. To get any further, we have to define three concepts.
Quantum superposition: Quantum superposition allows quantum bits (qubits) to coherently hold together many states of data until the data is decomposed. A piece of data can coherently be in two states before it is measured as one. The most well-known example of this is Schrödinger’s cat. A which posits that a cat might be simultaneously alive or dead in a sealed box based on the probability that a poison might be leaked inside. Only once the observer lifts the sealed box is the final state of the cat revealed. Quantum superposition works metaphorically the same way.
Quantum superposition is what allows quantum computing to be extraordinary. The ability to superimpose extraordinary amounts of data allows for much faster calculations than can be done in classical computing. Mathematically speaking, quantum superposition allow qubits to be linear combinations of different quantum states rather than fixed, mutually exclusive categories. This is what allows for a qubit to store more classical information than the strictly binary classical bit.
Quantum entanglement: Entanglement refers to the correlation between different quantum-level molecules. If one entangled molecule has a clockwise spin, another entangled one might have a counter-clockwise spin, no matter the distance between them. This happens with large molecules and even some small diamonds.
Entanglement means you have to read a whole system of data rather than individual data points. The “information” contained in entangled quantum data includes how the entire system is structured. You cannot isolate information from individual molecules or parts.
This is the beginning of the constraint of quantum computing. Quantum states can capture more data, but you have to capture the entire entangled system to do something useful with it. Recent scientific advances in maintaining the lifetime of quantum entanglement have helped push quantum computing further.
Quantum decoherence: Decoherence is the bogey-man of quantum computing. Whenever quantum states are exposed to an observer they start decomposing, meaning information gets lost as time goes on. Quantum decoherence is a major bottleneck to quantum computing at scale.
TLDR (too long didn’t read): Quantum computers are amazing because they can collapse a lot of data into quantum states rather than just the old “0,1” of physical binary code. You can make simultaneous calculations orders of magnitude above what you can do with your regular computer.
Yet, you have to deal with the messy problem of entangled quantum molecules. You have to read the state of the whole system rather than its individual components. And you have to do all that before the state of the system loses coherence with the passage of time.
Quantum Use Cases
What can that extraordinary quantum computational power allow you to do beyond classical computing if you’re able to capture the data in a coherent manner? Here are some examples.
Perhaps the most well-known example of quantum computing is D-Wave. One common misconception is that D-Wave is building full quantum computers. They’re really building quantum annealers. What’s the difference? In summary, you can use a quantum annealer to find a local “good enough” minimum much faster than a classical computing context, making quantum annealers ideal for factoring numbers and network analysis/optimization. Complex machine learning models can run on a quantum annealer in much less time if you don’t care as much about finding the absolute best answer. Yet, quantum annealers are not set up to run full quantum algorithms.
Boeing uses quantum annealers to facilitate plane research, and healthcare providers use them to calculate the optimal radiology treatment with cancer patients.
Yet, you won’t be able to run Shor’s algorithm on a D-Wave quantum annealer or any full quantum algorithm, and so you wouldn’t be able to use D-Wave to fully crack cryptography patterns (except on a limited basis). That requires a universal gate quantum computer, a different beast than a quantum annealer.
There is a comprehensive catalog of about 50 quantum algorithms. Among the most interesting of those would be Shor’s algorithm which can solve for the prime factors of very large and complex numbers. When people talk about securing devices, blockchains and more for a “post-Quantum” world, they are talking about a world where a quantum computing device is able to calculate Shor’s algorithm and break certain parts of modern cryptography .
Grover’s algorithm helps reverse functions: usually, given X input you find Y output, but here, with a given Y output you can find the X input that initiated it. This is useful for database search. You can search to find a given X and whether it is present in a certain set of data. It could also be used to reverse-engineer user credentials. This might allow attackers to create counterfeit blocks on a blockchain or steal user passwords.
Quantum algorithms in general
Algorithms that are better processed in quantum settings than in classical computing are plenty: there are about 50 examples, ranging from verifying matrix products to Pell’s equation, with polynomial to superpolynomial (exponential) speedup over their classical variants — though whether those speedups are still present after rigorous testing is still an academic matter.
Quantum programming frameworks
Now that you’ve run through some of the theory, what programming frameworks are out there to implement quantum computing concepts?
Qiskit is an open-source quantum computing platform developed in collaboration with IBM’s Q platform. You can run it on quantum computers built by IBM. This allows educators, researchers and businessmen a first look at the possibilities of quantum computing without having one themselves.
Resource: Qiskit-tutorials, available on Github, is a series of Jupyter notebooks that go into the basics of programming with Qiskit. They are community notebooks that serve as both interactive tutorial and a wiki of sorts on quantum computing in general.
Called “Q Sharp”, this is Microsoft’s effort to join the quantum computing fray. Most Q# subroutines will run on a simulator instead of an actual quantum chip. Microsoft’s Visual Basic Studio supports Q#. As Microsoft offers more quantum products, it will become the de facto language of the Microsoft quantum computing ecosystem.
Resource: With this quickstart tutorial, Microsoft gets you up to speed with how to use Q#.
QCL is a high-level programming framework for quantum computing that abstracts away some of the physics associated with quantum phenomenon.
Resource: This simple primer offers an explanation for the roots of QCL and its similarity to existing traditional computer science languages, with a few specific differences (such as the dump function which returns the current quantum state of all qubits) that make it suited to quantum computing, but comfortable enough for traditional computer scientists.
Project Q is an open-source programming framework for quantum computing developed at ETH Zurich. It features a high-level programming language for quantum programming, the ability to customize the compiler, and specific libraries to solve for quantum problems. You can run Project Q on quantum simulators or run it on IBM’s 5-qubit quantum computer.
Resource: This Github repo filled with examples from Project Q code serves as a useful reference and tutorial to explore.
Cirq is Google’s effort to address a chronic problem with limited-qubit quantum computers (namely error-correction). It’s a Python library you can install via pip (pip install cirq). It’s a useful tool that you can access right away if you’re running a Python environment.
Resource: Use this step-by-step tutorial on using Cirq on Medium to understand its capabilities.
D-Wave Leap offers an interactive cloud platform where you can operate on D-Wave annealers online. You can work in Python and Jupyter notebooks and have immediate access to a D-Wave 2000Q quantum computer. You get a minute of free QPU time which you can use to solve between 400 and 4000 problems.
Resource: This link allows you access to a set of Jupyter notebooks where you can try D-Wave Leap.
Quantum Computing Careers
What are the career prospects of working with quantum computing? For now, the field is mostly academic in nature — and there are few commercial use cases. A search on Indeed.com returns no results for quantum computer or quantum programmer. There are some research roles/internships such as the following from Microsoft. With IBM, Microsoft and Google making big bets in the space however, more quantum careers are surely coming.
Quantum Computing Resources
If you want to follow the space, here are a few great communities and resources to keep track of.
This Medium publication hasn’t been updated recently, but it features many interesting articles on quantum computing concepts. Anastasia Marchenkova, a quantum physicist whose passion is quantum computing, writes most of the content.
Microsoft offers a newsletter dedicated around the latest quantum computing updates as well as industry news. While it’s focused on selling Microsoft products, you can gain valuable insights here.
Quanta Magazine takes a different approach from the rest of the resources in this space, focused on quality storytelling. It acts as a compelling story-driven overview into advances in quantum computing and the people who make them.
The Stack Exchange for Quantum Computing offers deeper answers on quantum computing theory and quantum programming frameworks.
Check out this subreddit for the latest trending quantum computing discussions and articles. With over 10,000 subscribers, it is one of the largest communities dedicated to quantum computing.
Quantum Computing Courses
Coursera offers this course from Saint Petersburg State University. It covers quantum algorithms, including the two most common discussed (Shor’s algorithm, and Grover’s Algorithm).
This free course offered by University of Toronto (it offers a verified certificate for $49 USD) will go over the use cases of quantum computing in machine learning, and where machine learning can benefit from quantum computing advantages.
This free video series from Michael Nielsen goes over the theory of qubits in detail, allowing you to get an introductory view to quantum computing theory. Buckle up, finish the whole series, and you’ll be capable of tackling basic implementation of that theory.
This free course on MIT’s open platform teaches the theory behind quantum computation. Professor Peter Shor , who was the inventor of Shor’s Algorithm, teaches it.
These set of notes about quantum computing by Ronald de Wolf (a full-time professor at the University of Amsterdam) serve as a text-heavy and notation-heavy deep dive into quantum computing topics. Regard it as a textbook for whenever you need a deep dive on a particular subject.
I hope you enjoyed this introduction — I’d love feedback on what specific topics and resources I can build in the space. Comment below if you have any ideas!
What is digital literacy?
“Digital literacy is the ability to use information and communication technologies to find, evaluate, create, and communicate information, requiring both cognitive and technical skills.” is the textbook definition given by the American Library Association. At code(love), we think it has to go further.
Digital literacy involves a set of foundational skills that are required to navigate the 21st century. These new 21st century skills will allow anybody to navigate the emerging technologies of today. It will empower everybody to fully interface with the rich ecosystem of applications and digital services that are being developed.
Why does it matter?
With high job satisfaction for technical jobs such as data scientist, high compensation levels, the ability to create and interact with new digital technologies has never been more important.
Digital literacy skills are needed to thrive in a world where many of the world’s richest companies are software and hardware technology companies such as Facebook, Google, and Microsoft.
It also matters because of the flipside. 72% of Americans are scared of a future where they think robots and machines do most of the jobs accorded to humans. That’s almost twice as many as those excited about that possibility. The divide in politics doesn’t seem to between liberals and conservatives so much as people who embrace the future or people who are afraid of it.
Today’s students are going to be confronting a world that is very different than what their high schools and universities are preparing them for. Even these so-called digital natives will need to quickly up their information literacy skills for the 21st century.
The digital divide between those who are digitally literate and those who are not will soon extend to wealth and life outcomes across the board as the digital world takes over.
We have to dig deeper into the specific components that underlie digital literacy and these new literacy skills with how much it matters.
What are the specific components of digital literacy?
- The ability to find relevant and reliable information
- The ability to work with applications
- The ability to build a relevant audience
- The ability to build a website
- The ability to make payments and hold balances securely
- The ability to understand and control your own data
- The ability to understand new technologies
Let’s go look in-depth into each item:
1- The ability to find relevant and reliable information
The ability to find relevant information is how search engine Google has built a multi-billion dollar business. In 2017, people were producing about 2.5 quintillion bytes of data a day, most of it unstructured and hard to query. The Internet isn’t just the world’s largest container of data: it is also its largest attempt at structuring and classifying that data.
In order to be digitally literate, you should navigate that large realm of data and be able to pick out pieces of data and navigate the web.
This is an increasingly relevant skill in a world where media sources are disputed and where more and more authentic replicas of human behavior are being created: take a look at this photorealistic video of President Obama whose words were completely faked using artificial intelligence. The ability to be able to tell what information is relevant, credible and substantive is critical for digital literacy.
Digital content can be filled with inaccuracies. Determining reliable sources is a critical digital skill to have. It’s a critical part of 21st-century skills to have this new form of media literacy and understand digital media to be able to get the best information possible.
A nation with many digital citizens should have ready internet access, a way to curate and access information, and a way to quickly get relevant data.
Sample Stat: Only 17% of people are illiterate now in 2018. This was a reversal from 1820 when only 12% of the world could read and write. Hopefully, digital literacy will follow the same trend and as 80% of people will be able to find relevant information on the Internet.
- Reading comprehension
- Writing or voice-to-text capability
- The ability to quickly navigate search engines and get the most relevant results
- The ability to authenticate information via secondary sources
- The ability to verify providers of information and data
In general, you should be able to write out or communicate your search intent in a way that helps frame the most helpful results, understand how search engines surface certain results and the algorithms they use to determine the best results, and you should be able to quickly evaluate new sources of data for authenticity and reliability.
This Medium article uses StatCounter to suss out which search engines have the most penetration and market share per each market. Google tends to dominate in most countries with above 70% search engine market share — though Yandex leads in Russia, and Baidu leads in China, while Yahoo has a significant share as a search engine in Japan.
This guide for search modifiers will help you tailor down your search patterns to exactly the sort of information you’re looking for on the world’s most popularly used search engine.
2- The ability to work with applications
The world is run with different digital applications. If you’re a salesperson or somebody who has to chase down a list of people as part of your work, you’ve probably used customer relationship management software to track down everybody .
Your day-to-day routine might involve looking through social media applications and all sorts of different work and productivity apps, from spreadsheet software to document processors. Understanding how to work with these tools is a critical part of digital literacy.
The ability to navigate online communities, social networks and more and leave your own digital footprints is a critical part of digital citizenship as well — without participating in the digital discourse and lending your voice to it, your perspective may get lost in a world that has shifted from analog to digital.
Sample Stat: There were 171.8 billion mobile app downloads worldwide in 2017.
- Reading comprehension
- Writing or voice-to-text interface capability
- The ability to quickly navigate application user interfaces
- The ability to navigate accessibility issues
- The ability to use shortcuts
- The ability to recognize app interface cues
This handy guide dives into what makes a website easier to access and lays down a process for how to make apps more usable. It then runs over why usability itself is critical. These ten usability heuristics help dive into the rules behind making sites easy-to-access.
This guide runs through how to interact with an iPhone or iPad, two of the most popular screen interfaces for browsing the web. Learn how to do everything from accessing voice commands to increase the legibility of text.
3- The ability to build your own website
From being an application user, the next important step for digital literacy is to be able to build your own online media. In order to be fully digitally literate, it’s important not just to be a consumer and user, but also a producer or curator.
Having the ability to build your own website brings a whole new world of potential. It is akin to the writing aspect of literacy. It means the difference between merely absorbing the Internet and browsing it to being able to broadcast one’s thoughts on it — taking full advantage of the two-way street the Internet was always meant to be.
You can build simple webpages that help you do everything from displaying your CV and portfolio to sharing your thoughts on different matters, without a line of code. You might build a virtual store to sell your wares. Or you might share your business. With some basic knowledge of code, you can build so much more.
Sample Stat: 1 billion websites were created in 2015. There are close to 2 billion in 2018. Out of those 2 billion, only about 200 million (or 10%) are active.
- Reading comprehension
- Writing or voice-to-text interface capability
- Ability to work with applications/landing page generators
- Ability to interact with text editors
This interactive tutorial helps cover the steps and resources you’d need to understand HTML and CSS, the building blocks of the modern Internet. Once you understand HTML and CSS, you’ll understand how the skeletons of websites are built, and you’ll be able to analyze different webpages.
This review of different website builders gives you a handy way to build your own webpages even if you don’t know any code.
4- The ability to build a relevant audience
Reddit co-founder Aaron Swartz once said that “Everybody has the right to speak on the Internet, what matters is who is heard.”
The ability to create a website or application means very little if you don’t understand how to draw a relevant audience to it, and if you don’t understand how content is surfaced to users around the world.
Writing something, after all, isn’t the same sharing it with millions of people around the world. The ability to make an impact on the Internet means getting your content seen by a targeted audience at scale.
This means working with digital marketing techniques and understanding how to spread content with social media and a variety of digital tools. It means knowing how search engines rank content and then using that knowledge to help showcase your content to people around the Internet.
Sample Stat: Out of the Alexa Top 50 websites by visitor traffic, the top ten only has three countries represented: India, China, and the United States.
- Reading comprehension
- Writing or voice-to-text interface capability
- Ability to use analysis and statistics tools for web traffic such as Google Analytics
- Understanding of social media platforms and how to use them to distribute content
- Understanding of search engines and how to use them to distribute content
- Understanding of how social communities evolve on the Internet, and how to post and distribute content within those communities (ex: Reddit).
Neil Patel has made his living building large audiences for his ventures. Here he walks through all of the different tactics and approaches you can use to build your own relevant audience on the web.
This guide by Google will help you understand what it takes to rank in their search engine index. While everybody can create content, it’s really content that holds staying power in search engine rankings that creates lasting impact. Getting ranked on Google and other search engines the right way and with the right relevant keywords will certainly help you drive relevant audiences.
5- The ability to be able to make payments and hold balances securely
As the Internet gradually moves to a place where payments become part of the infrastructure, to become digitally literate is to combine your financial ability with your technological capabilities.
A decade ago, only about 5% of all retail operations were conducted on the Internet in the United States: now in those same categories, about 13% of retail sales are conducted online. In 2017, online retail sales to American customers crossed the $450bn mark, with rapid year-on-year growth of 16% from 2016.
With a growing amount of payment processors vying to help you send money online from Apple Pay to China’s WePay, it’s clear that e-commerce, unlike the heady days of the early 2000s Internet bust, is here to stay.
This has only been accentuated with the rise of blockchain technologies and cryptocurrencies, new entirely virtual monetary technologies. It’s been accelerated with a drive to online banking. With virtual assets coming into play and more real-world assets being digitized, the critical skill of being able to understand how to securely maintain balances online and to deal with transactions online will grow ever more important.
Sample Stat: According to a survey of 2,000 Americans, only about 8% of Americans hold digital cryptocurrencies.
- Reading comprehension
- Ability to write or give voice-to-text commands
- Basic statistics knowledge
- Understanding of safe practices around authentication and passwords
- Understanding financial interfaces around value transfer
- Basic knowledge on how to maintain privacy and security on the Internet
The following list of payment solutions will get you introduced to the services that help you both receive and send payments online.
This online video series will teach you about the foundations behind digital currencies and how they have evolved into the current stage of financial and technological innovation. It will run over the basics of the blockchain, Bitcoin, and cryptocurrencies.
6- The ability to understand and control your own data
We all generate data as we interact with the Internet. A critical part of understanding the Internet and how to use it safely and consensually is to understand what data is captured from us, and to navigate how and where we can consent to particular uses of our data. We can then navigate the trade-off between our attention and the data we generate for a company with the utility that the company provides us.
We can also make sure that our data is private and that we can deliberately choose who we share it with for whatever purpose we want and we can make conscious choices to avoid companies that violate our data principles. By browsing on the Web, we give away data about ourselves constantly. Having control over that data lets us keep our privacy and security while benefitting from applications.
Sample Stat: 93% of Americans believe it is important to be in control of who gets information about them.
- Reading comprehension
- Ability to write or give voice-to-text commands
- Understanding of how data is processed on the web and transmitted
- Understanding of what data is used for
- Basic knowledge on how to maintain privacy and security on the Internet
This handy guide will walk you through how to leave as little of a digital profile as possible by using encrypted chat and by making sure that the data you share with the world is the sort of data that you want shared.
This article talks about the sweeping new changes new European privacy legislation will bring (GDPR) and serves as a case study of how legislation can affect collective and individual data rights.
7- The ability to understand new technologies
As new technologies evolve, the ability to master them serves as the ultimate foundation of digital literacy. In order to be fully digitally literate, you need to have the foundation to be able to anticipate new technological advances, and to be fully ready to be an early adopter or creator with new trends.
We live in an age where each year brings drastic innovation, from biotechnology advances that allow individuals the power of modifying genomes to artificial intelligence models that can help individuals do tasks that once would have taken thousands of humans to do. To be able to understand those advances and create with them will help take and extend your digital literacy to the point where it is flexible and malleable to new advances, just like a full grasp of literacy allows you to understand and take in new ideas.
Sample Stat: Americans are more afraid of robots than death.
- Reading comprehension
- Ability to write or give voice-to-text commands
- Basic statistics knowledge
- Ability to work with applications/landing page generators
- The ability to quickly navigate search engines and get the most relevant results
- The ability to authenticate information via secondary sources
- The ability to verify who is a provider of information and data
Drawing from her background learning engineering, Dr. Oakley introduces powerful mental frameworks and tools to quickly and efficiently work with new information and challenges. It’s a powerful primer on how to adapt to an ever-changing world where information is king.
The Gartner Hype Cycle walks through the different stages of excitement a new technology brings, and how it can solidify to lasting change. You can use it as a framework to place new technologies into a certain mindset.
Digital literacy shouldn’t just be a rehash of literacy principles for the digital age and our new digital world. It should be a whole new set of metrics and capabilities that can be measured as an indicator of whether countries and nation-states and their citizens are ready for the 21st century. By evolving our understanding of what digital literacy means, we can more meaningfully prepare people for a future too many are currently afraid of.
This job report has been sponsored by Blockgeeks – For courses on how to become a blockchain developer please visit Blockgeeks.com
By now, you’ve probably heard of the growing blockchain and cryptocurrency sector and you’re probably wondering how to get blockchain jobs. It seems to have turned the world upside down. As Bitcoin peaked at a price past $20,000 in late 2017, a mad fever seemed to descend on the whole technology industry.
With both Bitcoin and Ethereum pushing the sector forward with large partnerships, institutional money flowing in, and a wave of technical advancements, the sector is maturing. That brings entrepreneurial talent and demand for new hires.
New projects are now creating their own tokens to be able to raise money and hire teams to execute on their product roadmap. Now is a good time to examine blockchain jobs.
The difference between blockchains and cryptocurrencies
Before we get started, we have to define a few terms.
Blockchains are distributed blocks of data linked together by a specific set of consensus protocols, while cryptocurrencies are the tokens used to access services and applications built on the blockchains in questions. For example, you use the Ether token to access services on the Ethereum blockchain.
You can trade in tokens so they may increase or decrease in price as a function of the overall health of the blockchain they access.
In practice, a cryptocurrency startup will be focused on the value of the token and any underlying application and blockchain they build on top, while a blockchain startup will be focused on building or maintaining blockchains while not necessarily taking advantage of tokens for internal compensation or for fundraising.
This will matter when it comes to compensation, with blockchain startups geared towards conventional salaries and cryptocurrency startups offering slightly higher pay, and token grants to incentivize employees. There is a difference between blockchains and cryptocurrencies after all.
A look at the sector
Are there new blockchain and cryptocurrency jobs? What are the types of companies building products in the space?
Most blockchain and cryptocurrency startups have the same demands for different roles as your standard startup. Web development, product and design needs in blockchain and cryptocurrency are as strong as they are in any regular startup. There are now thousands of open job roles with different blockchain/cryptocurrency startups hosted on popular startups job site AngelList.
Consultancy firms working in the space have flagged a talent gap. Deloitte alone is looking to hire about 25,000 people who are familiar with the blockchain space. Developers can command salaries of over $220,000 USD.
There are currently 401 blockchain and cryptocurrency startups (as of July, 30th, 2018) hiring for full-time roles above $100k USD in compensation on popular startups site AngelList. Using the same search approach, about 800 cryptocurrency and blockchain startups are currently hiring for different roles.
A quick look at CoinMarketCap shows there’s about $200bn in market value in cryptocurrency as of August 2018. There are about 2,000 cryptocurrencies listed, many of them supported by a core company.
About half of all blockchain and cryptocurrency startups on AngelList have tagged themselves as being open to remote job roles.
This compares with the 30% average on AngelList of all companies that are open to remote roles.
Cryptocurrency and blockchain startups are on average almost twice as likely as your standard tech startup to accept distributed team members and remote job roles.
There are also some freelance roles in cryptocurrency and blockchains, with many workers in the space listed on platforms like Upwork. There are, as of August 2018, about 1000 freelancers listed with blockchain as a term.
In comparison to more established careers such as web development and data science, there are less absolute jobs. However, the sector and the jobs attached to it are growing rapidly.
Blockchain developers in the United States earn about $130,000 USD according to ComputerWorld. This is a bit higher than the average for programmers everywhere at $105,000.
Freelance rates for different cryptocurrency functions can be higher than $150 USD per hour.
Increasing numbers of cryptocurrency and blockchain startups use their own cryptocurrency tokens to compensate employees. Think of these as more liquid versions of startup shares, with more liquid markets where you can sell your tokens at different prices.
You might want to choose the sector and get interested in blockchain jobs because it’s growing and there’s higher compensation. You might also choose it because you want to upend traditional hierarchies.
The number of blockchain jobs and crypto jobs posted on AngelList, a popular startups job resource, nearly quadrupled between 2016 and 2017.
There are three main reasons to join the sector.
- You want higher-than-average compensation that is more liquid (albeit with quite some risk).
- You want to learn about blockchains and cryptocurrencies
- You want a flexible/distributed relationship with your work.
Sample job types
There is a need for designers throughout a crypto startup. They help with making any project look good with a consistent brand identity. They can ensure a great user experience and help the adoption curve of new crypto products come off flawlessly.
There different types of software developers in crypto-land. You have the same web development needs as any tech startup would have. You also have a demand for people who are familiar with how to build blockchains or create smart contracts. These demands are often separated. You have developers focused on on-chain work that is more related to the blockchain and its specifics. You also have off-chain developers focused on standard web development tasks.
The above is from a job posting for a blockchain engineer. The focus is on Solidity development and on-chain work and writing smart contracts. Tools such as Truffle and Web3.js that are specific to the Solidity environment come into play.
Meanwhile, this role above is for more of a standard front-end developer role for a cryptocurrency startup. It’s much more focused on providing interface work for smart contracts rather than dealing with the on-chain contract work itself.
Software engineers will find plenty of blockchain jobs available to them, should they have the right mentality and the thirst to learn more about the sector.
Community Management/Investor Relations
Cryptocurrencies are marketed with the strength of their communities. Think of ICOs as a sort of Kickstarter for companies: the strength of your backing community and the network it forms largely determines how much you raise. Most crypto startups will have robust communities focused on Reddit, Telegram, Discord, Slack and traditional social media.
Community management roles in blockchain/crypto fall closer to a combination of customer support and marketing. Community managers play a critical role in the marketing of a cryptocurrency startup, both in terms of curating real use cases and in driving early investors or supporters to broadcast updates.
A subset of community management involves dealing with high-net-worth individuals who form the bulk of ICO investment, coming closer to investment management.
ICO projects will often look at digital marketers to get growth going, both in terms of the whitepaper and product goals but also to get investor interest from the crowd.
As you can see with the sample job role, a lot of the marketing and growth work for cryptocurrency startups is related to community management.
In general, blockchain and cryptocurrency companies that are looking to get user adoption for their products will need growth marketers and salespeople to help. Blockchain technology is just one part of the fight — user adoption is another.
ICOs and crypto startups have to deal with a whole slew of legal issues. One wrong move, and massive fees and legal issues will arise. There is often the need to hire legal help, whether internal or external.
This role above is an example of an in-house counsel role. A lot of focus is placed on legal issues specific to cryptocurrency, especially anti-money laundering regulations.
There are different types of blockchain and cryptocurrency startups you can work for.
There are startups that have created their own cryptocurrency and are building an ecosystem and products related to it. You might think of such startups as Request Network, Quantstamp and more. However, there are many projects outside of just name-brand companies. Every company that is raising money through an ICO is probably hiring.
There are startups that use a layer of cryptocurrency to help them carry out key parts of their business. The web browser Brave is an ads-free browser that uses its own cryptocurrency, Basic Attention Token, in order to compensate content creators and aggregators.
There are startups, companies, and organizations building their own blockchains or using different blockchains. An example of somebody building their own blockchain is the DFINITY project. There are also popular blockchains such as Stellar and Ripple.
There are startups building tools that make it easier for people to exchange, trade and secure cryptocurrencies such as cryptocurrency exchanges like Coinbase, over-the-counter trading tools such as Circle, and cryptocurrency portfolio trackers like Blockfolio. There are also people providing financial services in cryptocurrencies — funds such as Polychain Capital.
Conventional companies like IBM are looking to hire blockchain engineers to help them understand the space.
Finally, there are accelerators or holding companies that co-own multiple cryptocurrency or blockchain ventures and act as a supervising entity. The largest of these is probably Ethereum-based ConsenSys.
Most cryptocurrency roles are in certain hotspots: Singapore, San Francisco and Zug Valley in Switzerland, for example.
Distributed teams are a force for cryptocurrency startups, with half of all cryptocurrency startups willing to hire for remote roles.
You can work for cryptocurrency startups remotely or on contract without having to move through different borders. This is a fitting extension of a technology that aimed to lower borders for transactions of value.
You are as likely to work in your own home as in an office located in a cryptocurrency hotspot.
The standard startup skillset is essential for blockchain jobs. The archetypes of design, development, and business (hipster, hacker, and hustler) shine through.
There is a lot of demand for people who have experience building production-level blockchain projects because it’s a very rare skill set. The ability to work on blockchain projects outside of the Bitcoin Core team has been relatively new. It came with the explosion of Distributed Apps and Ethereum-based applications.
The more experience developers have with different blockchains, the better — as the blockchain space is highly volatile, with different blockchains iterating and developing and some vanishing off the map entirely. It makes it much easier to get blockchain jobs if you have exposure to different blockchains.
On the business and marketing side, those who have had effective experience running paid ads, and creating organic communities will fit right in. However, cryptocurrency marketing comes with an additional landmine of paying attention to legal and compliance issues. There is a sweet spot for customer service/community management types who can corral the vast organic communities most cryptocurrencies sport on Telegram, Reddit and Discord.
Designers are needed for constructing interfaces and what are called “off-chain” applications. In many ways, most blockchain and cryptocurrency startups have the same need for interface designers as your standard tech startup — so there are plenty of blockchain jobs for designers.
Resources/learning paths for skillsets/knowledge
I’m going to split out here curated learning paths on two dimensions, one focused on the business side of cryptocurrency, the other on the technical part. We’ll have a curated set of learning resources for both.
This will make it easier for you to have specific knowledge and examples of the biggest problems encountered in both blockchain and cryptocurrency and help you get a handle on what blockchain jobs require.
Introduction to blockchain and cryptocurrency
The Crypto Canon is venture capital firm Andreessen Horowitz (a16z)’s effort to curate some of the best articles on blockchain and cryptocurrency. It goes from building blocks and basics to developer tutorials.
This glossary will help you navigate different words and terms in the blockchain space, and will also help you come up with a curated understanding of the non-technical elements of the space.
An important part of the blockchain discussion revolves around who controls the blockchain in question and the meaning of decentralization. The following resources will help clarify this topic.
This article by Vitalik Buterin, Ethereum’s creator, helps explore what is really meant by decentralization — and helps tease out the differences between centralization on an architectural or technological basis, and centralization in a political structure.
This slide deck talks about why governance itself is important, before also breaking down what makes blockchain governance so different from traditional models of governance.
Blockchains are pseudonymous by default. Wallet balances are public but not who owns the wallet. They are not completely private unless that is deliberately designed. Anybody who can track down the identity behind a Bitcoin wallet can trace their every transaction. This was how the FBI seized Silk Road’s assets.
This article discusses how privacy on the blockchain is an uphill battle and suggests a few key weaknesses in the privacy approach of most bitcoin users. It then suggests a few solutions that might help.
Using alternatives to Bitcoin can address privacy concerns. ZCash, a cryptocurrency focused on using zero-knowledge proofs to guarantee privacy writes about how it can prove something is valid without revealing any information about it: the basis of a truly private blockchain.
Another major issue is scale: how many transactions per second a blockchain can process. This becomes a major issue as Bitcoin or Ethereum surge in usage but cannot support the number of transactions. Ethereum can process about 13-15 transactions per second, but Visa can process up to 56,000 transactions per second. Looking through how to bridge this gap is an important problem.
This article sums up the present state of cryptocurrency scale and then proposes some different solutions that are being actively explored.
The Lightning Network is a solution to Bitcoin’s scaling problem. This article talks a little bit about the mechanics behind it and helps explain what exactly a specific solution to a scaling problem (even if it’s off-chain) looks like.
If you want a guide to how to think about cryptocurrencies and get in the position to trade with them practically, this guide, while behind an email wall and targeted mostly to wealth advisors, can help you with a broad overview. Note that the author of this piece isn’t making investment advice here and is, well, me.
This handy tutorial dives into how you can use Golang to quickly get up a prototype blockchain, helping you to understand the basics needed here. It’s a handy way to dive right into the blockchain industry.
This listable I created has many of my favorite resources to learn Solidity, the programming language of choice for applications on Ethereum. It’s a handy resource to getting you blockchain jobs.
CryptoZombies is a game that will help you learn Solidity interactively. It remains to this day of my favorite resources to onboard people into the language.
Long-term view on the sector
Most people compare what is happening to the blockchain and cryptocurrency space to the first Internet bubble. There may a ”mania” right. That creates a more sobering long-term view on cryptocurrency and blockchain.
One interesting difference between cryptocurrency startups and regular ones are the difference between being accorded stock options against more liquid tokens. In practice, the CEO, investors, and board of directors have much more control over standard startups and employees.
The retention of employees in standard startups (bad) might look stable compared to crypto startups that give their employees liquid tokens they can cash out immediately.
One of the biggest strengths of the cryptocurrency boom, the pulling-in of a wave of talented employees with above-average compensation, might come to a screeching halt when things turn sour. This is something to keep in mind as you’re looking for blockchain jobs.
As more and more entrepreneurs start trying new crypto startups as a way to raise funding, there will be more talent focused on the sector. However, as crypto prices fluctuate, the sector becomes unstable. Lots of thought needs to be placed on how this affects the broader economy and jobs in cryptocurrency and blockchain.
Job boards for cryptocurrency jobs and blockchain jobs are under development: here are some good ones:
The path to blockchain jobs
The way you get blockchain jobs and cryptocurrency jobs is often network or referral-based. You may need to know founders of crypto projects to get in. It’s also possible to get in by applying on AngelList or going to cryptocurrency and blockchain events.
I self-taught myself SQL after I bombed a technical interview that involved SQL. It got me a bit mad at myself, so I went ahead and started looking for different resources to help me practice and learn SQL. I wasn’t looking to spend any money so I focused on getting the best free resources. The list below is the fruit of my efforts. I hope that it helps you on your journey to learn SQL.
It can be handy to learn SQL. You might be looking to do data analysis with it, or you might look to learn SQL to help answer different questions you have on your data. A quarter of data users use MySQL databases — to get access, you’ll need to learn SQL.
Codeacademy is one of my go-to resources for learning programming because it involves interactive exercises where you get immediate feedback. This is what makes the practice and learning of programming so fluid and rapid. Their section on SQL meets the same bar as the rest of their courses, and of course, it’s free.
This free self-paced mini-lesson on databases and SQL allows you to follow along with what top computer science students at Stanford are learning — all this at a self-directed pace, and for the total cost of zero dollars.
This book by O’Reilly entitled SQL Head First helps you dive deep into SQL topics, with tons of examples and writing to help you learn SQL. It can be completely free with a free trial of their Safari product (though the trial will run out). I classified it as a free resource as it doesn’t seem like O’Reilly’s paywalled it.
W3Schools offers a text + exercises tutorial that breaks down individual SQL functions. I like looking through individual-level examples of different functions, especially when I’ve forgotten the nuances of one or another. You’ll see that several other providers do the same thing as W3Schools, but for me, W3 is the best resource out of the lot.
SQLZoo is one of the coolest free SQL resources out there. The set of rich, interactive exercises placed in real-world settings is about as close as you’ll get to working with SQL in complex production-level environments without actually being hired. I used it a lot to practice SQL as I was picking up data analysis skills and I still use it every once in a while as a refresher.
Mode Analytics offers a cool, intuitive interface along with a case study like approach involving Crunchbase data. It can help you rapidly place your SQL skills in a context that is like real-world uses, and once again, it’s free. They’re trying to upsell you to their paid product in offering this educational resource. That type of content always does well however, because it’s never priced and the focus of the team in question is to deliver maximum quality and substance for your learning objectives.
This Sololearn course is interesting because it has different quizzes to make sure you retain material as you’re learning it. It follows the same conventional structure of teaching you how to select and query data, then teaching you how to sort and manipulate it within a data table, finally moving to joining and modifying tables. Notably, there isn’t much here in the way of aggregation functions or subqueries, which other courses above will teach.
8- SQL for Beginners (Youtube)
If you prefer learning with video content, this 1-hour video tutorial on SQL might just do the trick for you. It starts going over the basics of SQL, then shifts into the end by talking through different functions and examples on video. It is however meant to be an introduction — don’t expect to become an expert in SQL just by watching this video.
9- SQL Tutorial
SQL Tutorial offers an interface like W3Schools without the exercises. In case none of the resources above it seem to be working for you, this is another free resource where you can break down and learn SQL functions one-by-one.
SQLCourse offers a curriculum with specific exercises for different functions. It is another handy way to practice your SQL skills for free. The interface is a bit janky, but it’s workable.
11- Welcome to SQL (KhanAcademy)
If you prefer a set of curated video tutorials that cover different topics, then look no further than the KhanAcademy course that introduces SQL. It runs through to the basics until aggregating data — there are projects in between as well. If you’re already on KhanAcademy, this could be a perfect transition to you learning SQL.
Yet another text + examples format of teaching SQL, TutorialsPoint does offer more of the same: but if you’re looking for more and more practice or different ways to digest SQL material, it might be handy enough for you.
SQLBolt is constructed a bit like a virtual, interactive book on SQL. You can read through it and then go through each exercise, feeling like you’re being guided on your journey by the book-like structure. If you want a more focused and curated approach to learning SQL — this might be it for you.
Another interactive SQL course, meant to run in the browser. It’s free-to-use, but may use quite a few intensive resources as it’s running. (not sure if that’s just how old this tutorial is, or an honest warning). It is free to try if you want to take a look.
15- Use the Index
Use the Index is a very useful advanced training site for SQL. Once you have the basics down, check out this site to see how to optimize your SQL databases and your queries so you can get maximum performance from it.
16 – Schemaverse
Schemaverse is a game built entirely on SQL. I’m a big fan of using gaming to motivate people to learn. I think it could show an exponential effect on motivation and on acquiring SQL skills if you’re somebody who wants to learn, but might get bored out of their mind with conventional tutorials or videos.
The Microsoft documentation around SQL Server can help you understand how to use their software to manage SQL databases and tables — it’s offered for free, so you can take advantage and have a look even if you’re just curious.
SQLFiddle allows you to upload data and play around with different SQL queries to see what data comes back. It is a great way to practice your SQL skills, especially if you don’t have a real-world database to play with. You can take .sql files from elsewhere, upload them, and then try to manipulate the data contained within or query certain selections of data. It is also free to use — which never hurts.
19- MySQL Sandbox
MySQL sandbox allows you to install several MySQL servers in sandbox mode, allowing you to rapidly experiment, test and learn with different data files. It can be a useful way for you to practice what you can do with multiple SQL instances.
One of my favorite free resources to learn SQL or really any programming language is StackOverflow. This is an online Q&A community focused on technical topics where you can ask experts questions or see solutions to existing problems. It’s a fantastic community to immerse yourself in if you’re learning SQL. The specific SQL tag makes sure that you can stay focused on SQL topics.
Another great community for learning is Reddit — where you can see different resources being placed and ranked by users as the days go by. Take part in discussions here or take advantage of articles or discussions posted to spur your own learning exponentially forward.
Hopefully, all these free resources are helpful to you on your journey to learn SQL — they were helpful to me as I started learning. While I leaned on some a bit more than others, I’m always a big fan of curating as many resources as possible because my learning style (very text-heavy) may not be the same as yours.
A lot of research has been done on the predictive abilities of matching how a concept is presented with a preferred learning style. While few models meet the test, it does seem like digesting new materials under a whole bunch of different modes or media formats helps learning.
So if you want to learn SQL, it’s best to go through and try a few of these resources, then choosing and sticking to which ones you like and combating the learning problem from many different angles.
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:
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 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.
This interactive Python tutorial is created by Datacamp, and is more suited to introducing how Python basics work in the context of data science.
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.
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.
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 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.
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.
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.
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.
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
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.
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.
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.
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 exploration and data visualization work together hand-in-hand. Learning how to visualize data in different plots can be important is seeing underlying trends.
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.
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
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.
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.
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 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.
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.
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 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.
This informative Medium piece goes into the theory and statistics behind linear regression, and then describes how to implement it in Sci-Kit Learn.
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.
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.
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.
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
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.
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.
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.
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 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.
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!
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.
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
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.
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!
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:
- Continue working on something in machine learning at all times
- Go to StackOverflow, ask and answer questions
- Read the latest machine learning papers, try to understand them
- Practice your code whenever you can by looking through Github machine learning repositories
- 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.
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?
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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.