The term “big data” is everywhere these days, and with good reason. More products than ever before are connected to the Internet: phones, music players, DVRs, TVs, watches, video cameras…you name it. Almost every new electronic device created today is connected to the Internet in some way for some purpose.
The result of all those things connected to the Internet is data. Big, big data. What’s that mean for you? Simply put, it means if you can quickly, accurately, and intelligently sift through data and find trends, you are extremely valuable in today’s tech job market. More specifically, here are five job titles that require data analytics skills and expertise to get ahead.
At first glance, data science seems to be just another business buzzword — something abstract and ill-defined. While data can, in fact, be both of these things, it’s anything but a buzzword. Data science and its applications have been steadily changing the way we do business and live our day-to-day lives — and considering that 90% of all of the world’s data has been created in the past few years, there’s a lot of growth ahead of this exciting field.
While traditional statistics and data analysis have always focused on using data to explain and predict, data science takes this further. It uses data to learn — constructing algorithms and programs that collect from various sources and apply hybrids of mathematical and computer science methods to derive deeper actionable insights. Whereas traditional analysis uses structured data sets, data science dares to ask further questions, looking at unstructured “big data” derived from millions of sources and nontraditional mediums such as text, video, and images. This allows companies to make better decisions based on its customer data.
So how is this all manifesting in the market? Here, we look at three real-world examples of how data science drives business innovation across various industries and solves complex problems.
Demand for data scientists has increased 663% in five years, and the call for machine learning skills is up 809%.* In this free lesson, GA instructor Danny Malter will give you a better understanding of data science, including:
What data science skills could do for your career.
Examples of how data science impacts the real world.
Algorithms in action.
When you’re ready to go further, explore our upcoming Data Science course to cement your foundation in machine learning, predictive models, and Python programming. Or get inspired by these resources:
Hello intrepid data scientist! First off, I’d like to congratulate you; you’re likely reading this post because you’re preparing to interview for a data science job. This means I’ll assume that: (a) you’re the type of person that researches ways to improve and level up in your career, and (b) you’re reached the interview stage — congrats!
As a data science instructor, I’m often asked for advice on how to prepare for a data science interview. In response, I usually bring up three major themes. You need to:
1. Have a background that includes sufficient knowledge of the field of data science to fulfill the job’s tasks.
2. Have implemented that knowledge in some way that the community recognizes.
3. Be able to convince your interviewer of your knowledge and abilities.
1. Knowledge of Data Science
I’ve taken part in interviewing many data scientists and have also been interviewed. Through being on both sides of the table, I’ve seen that there are usually three-ish areas of knowledge that an interviewer is looking for: prerequisite knowledge of data science at large, which includes: mathematics, coding, databases, and the ability to communicate findings and insights; knowledge of the company and its vertical; and knowledge of the tech stack of that company.
If you’re reading this article with a fairly long time horizon and not trying to cram, then you can prepare ahead of time with the knowledge of data science at large by taking a look at this blog post which has a long list of curated resources. If you are reading this and trying to prepare for a data science interview on a short time horizon, this article and this article have a list of questions with answers to get you in the zone.
Knowledge of the company is going to come from research of that company. Read up on the company and if you have time, find second and third degree connections through LinkedIn or people you know and reach out. As a General Assembly alum, I’ve found it incredibly helpful to go to a company’s LinkedIn page, check out who the fellow alumni are, and connect through a LinkedIn message or offering to buy them coffee. Reading up on the company usually takes the form of doing research about the company itself (founding principles, place in the market, investment stage, etc.), but it also takes the form of looking up who you’d be working alongside if you started working there. What does the data team look like? Are there data engineers or other data scientists?
During a data science interview, your background will likely speak to your knowledge of the vertical you’re applying to. In the absence of that, some portfolio projects are a great second option to show your domain expertise.
Thomas Hughes, Manager of Data Science and Machine Learning at Etsy, shared this bit of advice on striking a balance between generalized skills, specific skills, and knowledge in a vertical:
“Companies who do not have much experience in data work generally look for candidates who specialize in their industry vertical. Since they don’t know what they’re looking for, they often will say, ‘I’m looking for someone who has solved problems similar to my problems, which I’m assuming means they have to be coming from my industry.’
More mature companies, with experience in the data space, recognize that many of the techniques are applicable across industries and don’t require industry specific knowledge, and furthermore, someone who’s deeply trained in a specific technique often adds more value than someone who’s just familiar with an industry vertical.”
Theodore Villacorta, Executive Director of Analytics at Warner Brothers, shared with me that, “regarding vertical, your background matters less; it’s more about skills to get data from a database and how you can perform with it.”
Lastly, you need to be fairly well versed in the tech stack that the company primarily uses. Villacorta offers: “Since knowledge of one of the two main open source languages is a strong requisite, along with the ability to use the corresponding SQL packages for those languages, it might be a great idea to showcase those in a portfolio piece. Most organizations have some form of SQL database.” At minimum, be prepared to answer questions about any tech stack that the company uses within the realm of data science and especially be prepared to answer questions about any tech that your resume lists. I usually like to do two things in preparation, to get an idea of what’s being used: first, I’ll head to stackshare.io and see if the company is listed. Second, I’ll look at the skills that current employees list on LinkedIn.
2. Community Recognition
The second piece is the community piece, especially if you have plenty of time before the data science interview. Community is purposefully a fairly amorphous term here. You can attend in-person events like meetups or conferences, or you can also have a community of coworkers, or a community of social media followers. I suggest laying the groundwork naturally. Networking can feel uncomfortable, but finding people you genuinely like being around in this field is usually pretty easy (didn’t anyone tell you that data scientists are the coolest people in any room?). If you don’t find a community that you’re into, try building one: set up a talk featuring other data scientists. Think like a starfish here, not a spider. You’re trying to create interactions and connections that continue to build new interactions in your absence; not interactions and connections that fall into a void once you’re no longer making them happen.
3. Convince Your Interviewer
In your data science interview, you need to convince the interviewer of your capabilities of both areas above. Interviewers are looking to make sure that you’re someone that generally fits into the puzzle board of other employees that make up the company culture. Show them that you’re great at the community thing through past coworkers or your involvement in open source projects online, engagements with people on Twitter, your writing style on blog posts, and the like. As Villacorta mentions, “For everyone, regardless of how cross functional of a role, I think it’s important to find someone who has an ability to collaborate, share resources…I’ll usually ask behavioral questions like ‘tell me a time when…’ in order to get a sense of a candidate’s abilities in this area.”
Hughes explains, “Senior level positions generally need to be providing leadership and influence over non-technical stakeholders. So they need experience explaining how the work they and their team is doing is valuable in non-technical ways.” Demonstrating your knowledge in an interview comes down to staying open. You’ve done the studying, now just get out of your own way.
I like employing the beginner’s mind here. Take every question in as though you’re uncovering the answer alongside the interviewer. In other words, think of it kind of like an archeological dig, rather than a tennis match. When you get an interview question like, “what’s a P value?” you can respond with, “are you curious about calculating and interpreting P values in the context of hypothesis testing in a project? Because I had a great project I worked on [insert teaser to a project here]… or are you looking for a definition?” This gives your interviewer a ton more fodder to work with and opens you up to answer questions in the Situation, Task, Action, Results (STAR) format, especially as it relates to former projects and jobs.
Regardless of where you are in the interviewing process, know that there is a position and great fit for a company for you somewhere. I think it’s helpful to consider the process of interviewing through the lens of a company — they’ve been looking for you! Don’t let your own ego get in the way of letting a genuine interaction take place during the data science interview. Interviews aren’t something you’re “stuck with” having to put up with on your march towards another job. In fact, they can be incredibly rewarding moments to find new areas to learn about in this fascinating field we’re in. Good luck, and let me know how it went!
Python is an interpreted programming language, meaning Python code must be run using the Pythoninterpreter.
Traditional programming languages like C/C++ are compiled, meaning that before it can be run, the human-readable code is passed into a compiler (special program) to generate machine code – a series of bytes providing specific instructions to specific types of processors. However, Python is different. Since it’s an interpreted programming language, each line of human-readable code is passed to an interpreter that converts it to machine code at run time.
So to run Python code, all you have to do is point the interpreter at your code.
Different Versions of the Python Interpreter
It’s critical to point out that there are different versions of the Python interpreter. The major versions you’ll likely see are Python 2 and Python 3, but there are sub-versions (i.e. Python 2.7, Python 3.5, Python 3.7, etc.). Sometimes these differences are subtle. Sometimes they’re dramatically different. It’s important to always know which version is compatible with your Python code.
Run a script using the Python interpreter
To run a script, we have to point the Python interpreter at our Python code…but how do we do that? There are a few different ways, and there are some differences between how Windows and Linux/Mac do things. For these examples, we’re assuming that both Python 2.7 and Python 3.5 are installed.
Our Test Script
For our examples, we’re going to start by using this simple script called test.py.
test.py print(“Aw yeah!”)'
How to Run a Python Script on Windows
The py Command
The default Python interpreter is referenced on Windows using the command py. Using the Command Prompt, you can use the -V option to print out the version.
Command Prompt > py -V Python 3.5
You can also specify the version of Python you’d like to run. For Windows, you can just provide an option like -2.7 to run version 2.7.
Command Prompt > py -2.7 -V Python 2.7
On Windows, the .py extension is registered to run a file with that extension using the Python interpreter. However, the version of the default Python interpreter isn’t always consistent, so it’s best to always run your scripts as explicitly as possible.
To run a script, use the py command to specify the Python interpreter followed by the name of the script you want to run with the interpreter. To avoid using the full path to your script (i.e. X:\General Assembly\test.py), make sure your Command Prompt is in the same directory as your script. For example, to run our script test.py, run the following command:
Command Prompt > py -3.5 test.py Aw yeah!
Using a Batch File
If you don’t want to have to remember which version to use every time you run your Python program, you can also create a batch file to specify the command. For instance, create a batch file called test.bat with the contents:
test.bat @echo off py -3.5 test.py
This file simply runs your py command with the desired options. It includes an optional line “@echo off” that prevents the py command from being echoed to the screen when it’s run. If you find the echo helpful, just remove that line.
Now, if you want to run your Python program test.py, all you have to do is run this batch file.
Command Prompt > test.bat Aw yeah!
How to Run a Python Script on Linux/Mac
The py Command
Linux/Mac references the Python interpreter using the command python. Similar to the Windows py command, you can print out the version using the -V option.
Terminal $ python -V Python 2.7
For Linux/Mac, specifying the version of Python is a bit more complicated than Windows because the python commands are typically a bunch of symbolic links (symlinks) or shortcuts to other commands. Typically, python is a symlink to the command python2, python2 is a symlink to a command like python2.7, and python3 is a symlink to a command like python3.5. One way to view the different python commands available to you is using the following command:
This special shebang line tells the computer how to interpret the contents of the file. If you executed the file test.py without that line, it would look for special instruction bytes and be confused when all it finds is text. With that line, the computer knows that it should run the contents of the file as Python code using the Python interpreter.
You could also replace that line with the full path to the interpreter:
However, different versions of Linux might install the Python interpreter in different locations, so this method can cause problems. For maximum portability, I always use the line with /usr/bin/env that looks for the python3.5 command by searching the PATH environment variable, but the choice is up to you.
Next, we’re going to set the permissions of this file to be executable with this command:
Terminal $ chmod +x test.py
Now we can run the program using the command ./test.py!
Terminal $ ./test.py Aw yeah!
Pretty sweet, eh?
Run the Python Interpreter Interactively
One of the awesome things about Python is that you can run the interpreter in an interactive mode. Instead of using your py or python command pointing to a file, run it by itself, and you’ll get something that looks like this:
Command Prompt > py Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>>
Now you get an interactive command prompt where you can type in individual lines of Python!
Command Prompt (Python Interpreter) >>> print(“Aw yeah!”) Aw yeah!
What’s great about using the interpreter in interactive mode is that you can test out individual lines of Python code without writing an entire program. It also remembers what you’ve done, just like in a script, so things like functions and variables work the exact same way.
Command Prompt (Python Interpreter) >>> x = "Still got it." >>> print(x) Still got it.
How to Run a Python Script from a Text Editor
Depending on your workflow, you may prefer to run your Python program directly from your text editor. Different text editors provide fancy ways of doing the same thing we’ve already done — pointing the Python interpreter at your Python code. To help you along, I’ve provided instructions on how to do this in four popular text editors.
Notepad++ is my favorite general purpose text editor to use on Windows. It’s also super easy to run a Python program from it.
Step 1: Press F5 to open up the Run… dialogue
Step 2: Enter the py command like you would on the command line, but instead of entering the name of your script, use the variable FULL_CURRENT_PATH like so:
py -3.5 -i "$(FULL_CURRENT_PATH)"
You’ll notice that I’ve also included a -i option to our py command to “inspect interactively after running the script”. All that means is it leaves the command prompt open after it’s finished, so instead of printing “Aw yeah!” and then immediately quitting, you get to see the Python program’s output.
Step 3: Click Run
VSCode is a Windows text editor designed specifically to work with code, and I’ve recently become a big fan of it. Running a Python program from VSCode is a bit complicated to set it up, but once you’ve done that, it works quite nicely.
Step 1: Go to the Extensions section by clicking this symbol or pressing CTRL+SHIFT+X.
Step 2: Search and install the extensions named Python and Code Runner, then restart VSCode.
Step 3: Right click in the text area and click the Run Code option or press CTRL+ALT+N to run the code.
Note: Depending on how you installed Python, you might run into an error here that says ‘python’ is not recognized as an internal or external command. By default, Python only installs the py command, but VSCode is quite intent on using the python command which is not currently in your PATH. Don’t worry, we can easily fix that.
Step 3.1: Locate your Python installation binary or download another copy from www.python.org/downloads. Run it, then select Modify.
Step 3.2: Click next without modifying anything until you get to the Advanced Options, then check the box next to Add Python to environment variables. Then click Install, and let it do its thing.
Step 3.3: Go back to VSCode and try again. Hopefully, it should now look a bit more like this:
3. Sublime Text
Sublime Text is a popular text editor to use on Mac, and setting it up to run a Python program is super simple.
Step 1: In the menu, go to Tools → Build System and select Python.
Step 2: Press command ⌘ +b or in the menu, go to Tools → Build.
Vim is my text editor of choice when it comes to developing on Linux/Mac, and it can also be used to easily run a Python program.
Step 1: Enter the command :w !python3 and hit enter.
Step 2: Profit.
Now that you can successfully run your Python code, you’re well on your way to speaking parseltongue!
Python is a popular and versatile programming language. But what is Python used for? If you’re interested in learning Python or are in the process of learning how to code in Python, your efforts will be greatly rewarded as there’s so much you can do with it. In this article, we’ll explore the top three major uses for Python.
Before we dive into the uses, let’s briefly discuss why Python has so many uses in the first place. What characteristics does Python have that allow it to be so useful? Python is:
Readable: Python is a high-level programming language, meaning it has a higher level of abstraction from machine language and has a simple syntax and semantics (e.g., indentation instead of curly brackets to indicate blocks), which lends to its readability.
Versatile: Python has a large standard library, meaning it comes equipped with a lot of specialized code to handle different tasks. For example, instead of writing your own Python code to read and write CSV files, you can use the csv module’s reader and writer objects. In addition, there are many open-source libraries and frameworks that provide additional value for Python programmers — especially those in machine learning, deep learning, application development, and game development — and scientific computing will find an ample supply of libraries and modules.
What is Python used for? There are so many different tasks that Python can accomplish. You can use it to build recommender systems, create cool charts and graphs, build restful APIs, program robots, conduct scientific computing, manipulate text data or extract text from images; the list goes on and on.
The best way to think about uses for Python is through the most active and popular disciplines that rely on Python programming:
Artificial intelligence and machine learning
Data analysis and data visualization
1. Artificial Intelligence and Machine Learning
What it is: Artificial Intelligence is a concept that’s more or less the idea of machines or computers that mimic human cognitive functions such as “learning” and “problem-solving.” Activities like driving a car, playing chess, and answering a question are all structured, logic-based things that humans can do that are being implemented by computers today. At the heart of this activity is machine learning, which is the process that a computer takes to learn the relationships between variables in data so well that it can predict future outcomes (usually on unseen data). If data is the input (“knowledge”), the machines understand the relationships between variables (“learning”) and it can predict what the next step is (“outcome”) — then you have machine learning.
How Python is used: Artificial intelligence requires a lot of data, which in turn requires appropriate storage, pre-processing, and data modeling techniques to be implemented. Deep learning is the intermediary component; it’s the use of specialized models (neural networks) that can handle “big data” at scale. Python is a programming language of choice for the machine learning, deep learning, and artificial intelligence community due to it being a minimalistic and intuitive language with a significant number of libraries dedicated to machine learning activities, which reduces the time required to implement and get results. R is another popular language used by machine learning enthusiasts and practitioners, but Python tends to be more popular because of the number of machine learning and AI-related efforts coming from the tech community, which uses Python. For example, TensorFlow is Google’s AI platform and open-source software library used for machine learning and the creation of neural networks for AI purposes.
What it is: Data analysis is the specialized practice of analyzing data, both big and small, for information and insights. Results of data analyses are often visualized, for the benefit of the recipient, and the tools and techniques used to communicate results visually requires the specialization that is known as data visualization. Data analysis and data visualization are not unique to any industry. It’s better to think of them as process-focused roles than industry-specific roles. After all, every company and industry has its own data to work with. What data analysis is not is the management of data from servers and storage, although some data analysts specialize in data management.
How Python is used: Data analysis and data visualization are specialized roles that can implement Python in ways that are integral to the mission of each role. A data analyst will use Python for data wrangling and data transformation, which is converting data from its raw format to a usable, analyzable format. Then, using open-sourced libraries like Pandas, NumPy, and SciPy, data analysts can manipulate and analyze both numerical and categorical data. In order to visualize data locally, additional libraries such as Seaborn, matplotlib, ggplot, and bokeh, can be used. Some data visualization professionals prefer using Python over business intelligence platforms like PowerBI and Tableau because it’s free, easy to learn, and reduces the need to have to use additional software to create visualizations.
What it is: Web development is a catch-all term for creating web applications and application programming interfaces (APIs) for the web. Web development is a highly specialized role that can be explained by the design pattern known as model, view, and controller (MVC). These terms represent the specialized layers of code of a web application or API. The model involves the code for an application’s dynamic data structure, the view involves the code that directly interacts with the user, and the controller is the code that handles user interactions and works to facilitate input going from the view to the model.
How Python is used: Python has several MVC frameworks that can be used for web application development straight out of the box, and this includes Django, turbogears, and web2py. While a web framework is not required for web development, it’s beneficial to use them as they greatly speed up the development progress. For beginners, learning Python’s syntax and the libraries needed for building a web application or API is a high level of effort, but the alternative would involve a much greater effort, as it would require the knowledge and correct use of multiple programming languages instead of Python.
We’ve explored the major uses for Python, which include machine learning and artificial intelligence, data analysis and data visualization, and web development. If you’re currently learning Python programming, then you’re off to a good start, especially if you’re considering pursuing work in any of the aforementioned areas. For those unsure how to start learning Python, I encourage you to read some of our other posts, which provide more details and tips on how to get started.
Data Science is rapidly becoming a vital discipline for all types of businesses. An ability to extract insight and meaning from a large pile of data is a skill set worth its weight in gold. Due to its versatility and ease of use, Python has become the programming language of choice for data scientists.
In this Python cheat sheet, we will walk you through a couple of examples using two of the most used data types: the list and the Pandas DataFrame. The list is self-explanatory; it’s a collection of values set in a one-dimensional array. A Pandas DataFrame is just like a tabular spreadsheet, it has data laid out in columns and rows.
Let’s take a look at a few neat things we can do with lists and DataFrames in Python! Get the pdf here.
Python Cheat Sheet
Create an empty list and use a for loop to append new values.
#add two to each value my_list =  for x in range(1,11): my_list.append(x+2)
We can also do this in one step using list comprehensions:
my_list = [x + 2 for x in range(1,11)]
Creating Lists with Conditionals
As above, we will create a list, but now we will only add 2 to the value if it is even.
#add two, but only if x is even my_list =  for x in range(1,11): if x % 2 == 0: my_list.append(x+2) else: my_list.append(x)
Using a list comp:
my_list = [x+2 if x % 2 == 0 else x \ for x in range(1,11)]
Selecting Elements and Basic Stats
Select elements by index.
#get the first/last element first_ele = my_list last_ele = my_list[-1]
Python is one of the most popular and user-friendly programming languages out there. As a developer who’s learned a number of programming languages, Python is one of my favorites due to its simplicity and power. Whether I’m rapidly prototyping a new idea or developing a robust piece of software to run in production, Python is usually my language of choice.
The Python programming language is ideal for folks first learning to program. It abstracts away many of the more complicated elements of computer programming that can trip up beginners, and this simplicity gets you up and running much more quickly!
For instance, the classic “Hello world” program (it just prints out the words “Hello World!”) looks like this in C:
However, to understand everything that’s going on, you need to understand what #include means (am I excluding anyone?), how to declare a function, why there’s an “f” appended to the word “print,” etc., etc.
In Python, the same program looks like this:
Not only is this an easier starting point, but as the complexity of your Python programming grows, this simplicity will make sure you’re spending more time writing awesome code and less time tracking down bugs!
Since Python is popular and open source, there’s a thriving community of Python developers online with extensive forums and documentation for whenever you need help. No matter what your issue is, the answer is usually only a quick Google search away.
If you’re new to programming or just looking to add another language to your arsenal, I would highly encourage you to join our community.
What is Python?
Named after the classic British comedy troupe Monty Python, Python is a general-purpose, interpreted, object-oriented, high-level programming language with dynamic semantics. That’s a bit of a mouthful, so let’s break it down.
Web applications – Popular frameworks like Django and Flask are written in Python.
Desktop applications – The Dropbox client is written in Python.
Scientific and numeric computing – Python is the top choice for data science and machine learning.
Cybersecurity – Python is excellent for data analysis, writing system scripts that interact with an operating system, and communicating over network sockets.
Python is an interpreted language, meaning Python code must be run using the Pythoninterpreter.
Traditional programming languages like C/C++ are compiled, meaning that before it can be run, the human-readable code is passed into a compiler (special program) to generate machine code — a series of bytes providing specific instructions to specific types of processors. However, Python is different. Since it’s an interpreted programming language, each line of human-readable code is passed to an interpreter that converts it to machine code at run time.
In other words, instead of having to go through the sometimes complicated and lengthy process of compiling your code before running it, you just point the Python interpreter at your code, and you’re off!
Part of what makes an interpreted language great is how portable it is. Compiled languages must be compiled for the specific type of computer they’re run on (i.e. think your phone vs. your laptop). For Python, as long as you’ve installed the interpreter for your computer, the exact same code will run almost anywhere!
Python is an Object-Oriented Programming (OOP) language which means that all of its elements are broken down into things called objects. Objects are very useful for software architecture and often make it simpler to write large, complicated applications.
Python is a high-level language which really just means that it’s simpler and more intuitive for a human to use. Low-level languages such as C/C++ require a much more detailed understanding of how a computer works. With a high-level language, many of these details are abstracted away to make your life easier.
For instance, say you have a list of three numbers — 1, 2, and 3 — and you want to append the number 4 to that list. In C, you have to worry about how the computer uses memory, understands different types of variables (i.e. an integer vs. a string), and keeps track of what you’re doing.
Implementing this in C code is rather complicated:
However, implementing this in Python code is much simpler:
Since a list in Python is an object, you don’t need to specifically define what the data structure looks like or explain to the computer what it means to append the number 4. You just say “list.append(4)”, and you’re good.
Under the hood, the computer is still doing all of those complicated things, but as a developer, you don’t have to worry about them! Not only does that make your code easier to read, understand, and debug, but it means you can develop more complicated programs much faster.
Python uses dynamic semantics, meaning that its variables are dynamic objects. Essentially, it’s just another aspect of Python being a high-level programming language.
In the list example above, a low-level language like C requires you to statically define the type of a variable. So if you definedd an integer x, set x = 3, and then set x = “pants”, the computer will get very confused. However, if you use Python to set x = 3, Python knows x is an integer. If you then set x = “pants”, Python knows that x is now a string.
In other words, Python lets you assign variables in a way that makes more sense to you than it does to the computer. It’s just another way that Python programming is intuitive.
It also gives you the ability to do something like create a list where different elements have different types like the list [1, 2, “three”, “four”]. Defining that in a language like C would be a nightmare, but in Python, that’s all there is to it.
It’s Popular. Like, Super Popular.
Being so powerful, flexible, and user-friendly, the Python language has become incredibly popular. This popularity is important for a few reasons.
Python Programming is in Demand
If you’re looking for a new skill to help you land your next job, learning Python is a great move. Because of its versatility, Python is used by many top tech companies. Netflix, Uber, Pinterest, Instagram, and Spotify all build their applications using Python. It’s also a favorite programming language of folks in data science and machine learning, so if you’re interested in going into those fields, learning Python is a good first step. With all of the folks using Python, it’s a programming language that will still be just as relevant years from now.
Python developers have tons of support online. It’s open source with extensive documentation, and there are tons of articles and forum posts dedicated to it. As a professional developer, I rely on this community everyday to get my code up and running as quickly and easily as possible.
There are also numerous Python libraries readily available online! If you ever need more functionality, someone on the internet has likely already written a library to do just that. All you have to do is download it, write the line “import <library>”, and off you go. Part of Python’s popularity in data science and machine learning is the widespread use of its libraries such as NumPy, Pandas, SciPy, and TensorFlow.
Python is a great way to start programming and a great tool for experienced developers. It’s powerful, user-friendly, and enables you to spend more time writing badass code and less time debugging it. With all of the libraries available, it will do almost anything you want it to.
Final answer to the question “What is Python”? Awesome. Python is awesome.
It’s possible to learn Pythonfast. How fast depends on what you’d like to accomplish with it, and how much time you can allocate to study and practice Python on a regular basis. Before we dive in further, I’d like to establish some assumptions I’ve made about you and your reasons for reading this article:
First, I’ll address how quickly you should be able to learn Python. If you’re interested in learning the fundamentals of Python programming, it could take you as little as two weeks to learn, with routine practice. If you’re interested in mastering Python in order to complete complex tasks or projects, or to spur a career change, it’s going to take much longer. In this article, I’ll provide tips and resources geared toward helping you gain Python programming knowledge in a short timeframe.
If you’re wondering how much it’s going to cost to learn Python, the answer there is also, “it depends”. There is a large selection of free resources available online, not to mention the various books, courses, and platforms that have been published for beginners. More on that in a moment.
Another question you might have is, “how hard is it going to be to learn Python?” That also depends. If you have any experience programming in another language such as R, Java, or C++, it’ll probably be easier to learn Python fast than someone who hasn’t programmed before. But learning a programming language like Python is similar to learning a natural language, and everyone’s done that before. You’ll start by memorizing basic vocabulary and learning the rules of the language. Over time, you’ll add new words to your repertoire and test out new ways to use them. Learning Python is no different.
By now you’re thinking, “OK, this is great. I can learn Python fast, cheap, and easily. Just tell me what to read and point me on my way.” Not so fast. There’s a fourth thing you need to consider, and that’s how to learn Python. Research on learning has identified that not all people learn the same way. Some learn best by reading, while others learn best by seeing and hearing. Some people enjoy learning through games rather than courses or lectures. As you review the curated list of resources below, consider your own learning preferences as you evaluate options.
At a bare minimum, you (and your resource) must cover the fundamentals. Without understanding them, you’ll have a hard time working through complex problems, projects or use cases. Examples of Python fundamentals include:
Before you start learning Python, establish a goal for your study. The challenges you face as you start learning will be easier to overcome when you keep your goal in mind. Additionally, you’ll know what learning material to focus on or skim through as it pertains to your goals. For example, if you’re interested in learning Python for data analysis, you’re going to want to complete exercises, write functions, and learn Python libraries that facilitate data analysis. The following are typical examples of goals for Python that might pertain to you:
3. Select a resource (or resources) for learning Python fast.
Python resources can be grouped into three main categories: interactive resources, non-interactive resources, and video resources. In-person courses are also an option, but won’t be covered in this post.
Interactive resources have become common in recent years through the popularization of interactive online courses that provide practical coding challenges and explanations. If it feels like you’re coding, that’s because you actually are. Interactive resources are typically available for free or a nominal fee, or you can sign up for a free trial before you buy.
Non-interactive resources are your most traditional and time-tested; they’re books (digital and paperback) and websites (“online tutorials”). Many first-time Python learners prefer them due to the familiar and convenient nature of these mediums. As you’ll see, there are many non-interactive resources for you to choose from, and most are free.
Video resources were popularized over the past 10 years by MOOCs (massive online open courses) and resembled university lectures captured on video. In fact, they were often supported or promoted by leading universities. Now, there’s an abundance of video resources for various subjects, including programming in Python. Some of these video resources are pre-recorded courses hosted on learning platforms, and others are live-streamed courses provided by online education providers. General Assembly produces a live course in Python that covers Python fundamentals in one week.
Below I’ve compiled a list of resources to help you get a jumpstart on learning Python fast. They fall into the categories laid out above, and at a bare minimum they cover Python basics. Throughout the list, I’ve indicated with an asterisk (*) which resources are free, to the best of my knowledge.
Interactive Resources: Tools and Lessons
CodeAcademy: One the more popular online interactive platforms for learning Python fast. I know many Python programmers, myself included, who have taken CodeAcademy’s Python fundamentals course. It’s great for beginners, and you can knock it out in a week. It will get you excited about programming in Python.
DataCamp: Short expert videos with immediate hands-on-keyboard exercises. It’s on-par with the CodeAcademy courses.
*PythonTutor.com: A tool that helps you write and visualize code step by step. I recommend pairing this tool with another learning resource. This tool makes learning Python fundamentals a lot easier because you can visualize what your code is doing.
Non-interactive resources fall into two sub-categories: books and websites.
In researching books, I noticed a majority of them were actually catered to existing programmers interested in learning Python, or experienced Python programmers looking for reliable reference material (“cookbooks”) or specialized literature. Below, I’ve listed only the books I think are helpful for beginners.
Python Crash Course, 2nd Edition: This book provides a foundation in general programming concepts, Python fundamentals, and problem solving through real-world projects.
At first, my list started off with over 20 examples of websites covering Python fundamentals. Instead of sharing them all, I decided to only include ones that had a clear advantage in terms of convenience or curriculum. All of these resources are free.
*Google’s Python Class: Tutorials, videos, and programming exercises in Python for beginners, from a Python-friendly company.
*Hitchhiker’s Guide to Python: This guide helps you learn and improve your Python code and also teaches you how to set up your coding environment. The site search is incredibly effective at helping you find what you need. I can’t recommend this site enough.
*Python for Everybody: An online book that provides Python learning instruction for those interested in solving data analysis problems. Available in PDF format in Spanish, Italian, Portuguese, and Chinese.
*Python For You and Me: An online book that covers beginner and advanced topics in Python, in addition to introducing a popular Python framework for web applications.
*Python.org: The official Python documentation. The site also provides a beginner’s guide, a Python glossary, setup guides, and how-tos.
*Programiz in Python: Programiz has a lengthy tutorial on Python fundamentals that’s really well done. It shouldn’t be free, but it is.
*RealPython.com: A large collection of specialized Python tutorials, most come with video demonstrations.
*Sololearn: 92 chapters and 275 related quizzes and several projects covering Python fundamentals that can also be accessed through a mobile app.
Video resources have become increasingly popular, and with good reason: they’re convenient. Why read a textbook or tutorial when you can cover the same material in video format on your computer or mobile device? They fall into two sub-categories, pre-recorded video-courses, and live video courses.
Coursera: A large catalog of popular courses in Python for all levels. Most courses can be taken free, and paid courses come with certifications. You can also view courses on their mobile app.
EdX: Hosts university courses that focus on specific use cases for Python (data science, game development, AI) but also cover programming basics. EdX also has a mobile app.
Pluralsight: A catalog of videos covering Python fundamentals, as well as specialized topics like machine learning in Python.
RealyPython.com: A collection of pre-recorded videos on Python fundamentals for beginners.
*TreeHouse: A library of videos of Python basics and intermediate material.
EvantoTutsPlus: 7.6 hours of pre-recorded videos on Python fundamentals, plus some intermediate content.
*Udacity: Provides a 5-week course on Python basics. Also covers popular modules in the Python Standard Library and other third-party libraries.
Udemy: A library of popular Python courses for learners of all levels. It’s hard to single out a specific course. I recommend previewing multiple beginner Python courses until you find the one you like most. You can also view courses on their mobile app.
General Assembly: This live online course from General Assembly takes all of the guesswork out of learning Python. With General Assembly, you have a curated and comprehensive Python curriculum, a live instructor and TA, and a network of peers and alumni you can connect with during and after the course.
4. Consider learning a Python library.
In addition to learning Python, it’s beneficial to learn one or two Python libraries. Libraries are collections of specialized functions that serve as “accelerators.” Without them, you’d have to write your own code to complete specialized tasks. For example, Pandas is a very popular library for manipulating tabular data. Numpy helps in performing mathematical and logical operations on arrays. Covering libraries would require another post — for now, review this Python.org page on standard Python libraries, and this GitHub page on additional libraries.
5. Speed up the Python installation process with Anaconda.
You can go through the trouble of downloading the Python installer from the Python Software Foundation website, and then sourcing and downloading additional libraries, or you can download the Anaconda installer, which already comes with many of the packages you’ll routinely use, especially if you plan on using Python for data analysis or data science.
6. Select and install an IDE.
You’ll want to install an integrated development environment (IDE), which is an application that lets you script, test, and run code in Python.
When it comes to IDEs, the right one is the one that you enjoy using the most. According to various sources, the most popular Python IDEs/text editors are PyCharm, Spyder, Jupyter Notebook, Visual Studio, Atom, and Sublime. First, the good news: they’re all free, so try out a couple before you settle on one. Next, the “bad” news: each IDE/text editor has a slightly different user interface and set of features, so it will take a bit of time to learn how to use each one.
For Python first-timers, I recommend coding in Jupyter Notebook. It has a simple design and a streamlined set of capabilities that won’t distract and will make it easy to practice and prototype in Python. It also comes with a dedicated display for dataframes and plots. If you download Anaconda, Jupyter Notebook comes pre-installed. Over time, I encourage you to try other IDEs that are better suited for development (Pycharm) or data science (Rodeo) and allow integrations (Sublime).
Additionally, consider installing an error-handler or autocompleter to complement your IDE, especially if you end up working on lengthy projects. It will point out mistakes and help you write code quicker. Kite is a good option, plus it’s free and integrates with most IDEs.
7. When in doubt, use Google to troubleshoot code.
As you work on Python exercises, examples, and projects, one of the simplest ways to troubleshoot errors will be to learn from other Python developers. Just run a quick internet search and include keywords about your error. For example, “how to combine two lists in Python” or “Python how to convert to datetime” are perfectly acceptable searches to run, and will lead you to a few popular community-based forums such as StackOverFlow, Stack Exchange, Quora, Programiz, and GeeksforGeeks.
8. Schedule your Python learning and stick to it.
This is the part that most people skip, which results in setbacks or delays. Now, all you have left is to set up a schedule. I recommend that you establish a two-week schedule at a minimum to space out your studying and ensure you give yourself enough time to adequately review the Python fundamentals, practice coding in your IDE, and troubleshooting code. Part of the challenge (and fun) of learning Python or any programming language is troubleshooting errors. After your first two weeks, you’ll be amazed at how far you’ve come, and you’ll have enough practice under your belt to continue learning the more advanced material provided by your chosen resource.
By this point, we’ve established a minimum learning timeline, you know to select a goal for your study, you have a list of learning resources to choose from, and you know what other coding considerations you’ll need to make. We hope you make the most of these tips to accelerate your Python learning!
I also teach data science in Python. My students learn Python to build machine learning models but I’m always excited to hear of the other ways they’ve leveraged the programming language. One of my students told me they used it to web-scrape online basketball statistics just so they could analyze the data to win an argument with friends. Another student decided to expand on her knowledge of Python by learning Django, a popular framework, which she uses to build web apps for small businesses.
Before taking the plunge into data science, we all had fundamental questions (and concerns) about learning Python. If this sounds like you, don’t worry. Before I started learning Python, I spent several months convincing myself to start. Now that I’ve learned, my only regret was not starting sooner.
If you’re interested in learning Python, I want to share my biggest reasons for why you should. Two of these reasons are inherent to Python; one of them is a benefit of Python that I experienced first-hand, and some of the examples I discuss come from things I have researched. My goal is to give you enough information to help make an educated decision about learning Python, and I really hope that you choose to learn.
1. Python is easy to learn.
Long before I learned Python, I struggled to learn another object-oriented programming language in high school: Java. From that experience, I realized that there’s a difference between learning to program, and learning a programming language. I felt like I was learning to program, but what made Java difficult to learn was how verbose it was: the syntax was difficult for me to memorize, and it requires a lot of code to be able to do anything.
Comparatively, Python was much easier to learn and is much simpler to code. Python is known as a readable programming language; its syntax was designed to be interpretable and concise, and has inspired many other coding languages. This bodes well for first-timers and those who are new to programming. And, since it typically requires fewer lines of code to perform the same operation in Python than in other languages, it’s much faster to write and complete scripts. In the long run, this saves developers time, which can then be used to further improve their Python.
One observation I’ve made of Python is that it’s always improving. There have been noticeably more updates to the language in the last 5-10 years than in prior decades, and the updates have often been significant. For example, later versions of Python 3 typically benchmark faster completion times on common tasks than when carried out in Python 2. Every release in Python 3 has come with more built-in functions, meaning “base” Python is becoming more and more capable and versatile.
Learning is not an individual process; often you will end up learning a lot from “peers.” According to various sources, Python has one of the largest and most active online communities of learners and practitioners. It’s the most popular programming language to learn; it’s one of the most popular programming languages for current developers; and among data scientists, it’s the second most common language known and used. All of this translates into thousands of online posts, articles (like this one!), and resources to help you learn.
Speaking of online learning, Python is also tremendously convenient to learn. To learn the fundamentals of Python, there are a lot of learning tools out there — books, online tutorials, videos, bootcamps — I’ve tried them all. They each have their merits but ultimately having options makes it easier to learn. Once you start learning, the resources don’t stop. There are dozens of really good tutorials, code visualizers, infographics, podcasts, and even apps. With all of these resources at your disposal, there’s really no reason why you can’t learn!
To get into any of these use cases would require another post. Regardless, you might be wondering what allows Python to be such a versatile programming language? A lot of it has to do with the various frameworks and libraries that have been built for Python.
Libraries are collections of functions and methods (reusable and executable code) with specific intents; and frameworks more or less are collections of libraries. If you ask any Python developer, they can name at least a half-dozen libraries they use. For example, I often use NumPy, Pandas, and Scikit-learn — the holy trinity for data scientists — to perform math and scientific operations, manipulate and analyze data, and build and train models, respectively. Many Python-based web developers will name Django as one of their preferred frameworks for building web applications.
While it’s true that libraries are written for most programming languages and not just Python, Python’s usability, readability, and popularity encourage the development of more libraries, which in turn makes Python even more popular and user-friendly for existing developers and newcomers. When you learn Python, you won’t just be learning base Python, you’ll be learning to use at least a library or two.
3. Python developers are in demand.
Many people learn to program to enhance their current capability; others to change their careers. I started off as one of the former but became the latter. Before data science, I built digital ad campaigns and a lot of my work was automatable. My only problem was that I didn’t know how to code. Although I eventually learned how, in the process of learning Python for my work, I was presented with different job opportunities where I could use Python, and learned about different companies who were looking for people experienced in Python. And so I made a switch.
There are a lot of Python-related roles in prominent industries. According to ActiveState, the industries with the most need for Python are insurance, retail banking, aerospace, finance, business services, hardware, healthcare, consulting services, info-tech (think: Google), and software development. From my own experience, I would add media, marketing, and advertising to that list.
Why so many? As these industries modernized, companies within them have been collecting and using data at an increasing rate. Their data needs have become more varied and sophisticated, and in turn, their need for people capable of managing, analyzing, and operationalizing data has increased. In the future, there will be very few roles that won’t be engaged in data, which is why learning Python now is more important than ever — it’s one way to bullet-proof your career and your job prospects.
A lot of top tech companies value Python programmers. For instance, to say that Google “uses” Python is an understatement. Among Google engineers, It’s a commonly used language for development and research, and Google’s even released their own Python style guide. Google engineers have developed several libraries for the benefit of the Python community including Tensorflow, a popular open-source machine learning library. YouTube uses Python to administer video, access data, and in various other ways. Python’s creator Guido van Rossum, a Dutch programmer, was hired by Google to improve their QA protocols. And most importantly, the organization continues to recruit and hire more people skilled in Python. Other notable tech companies who frequently hire for Python talent include Dropbox, Quora, Mozilla, Hewlett-Packard, Qualcomm, IBM, and Cisco.
Lastly, with demand often comes reward. Companies looking to hire people skilled in Python often pay top dollar or the promise of increased salary potential.