Why Learn Python For Data Science? Before we explore how to learn Python for data science, we should briefly answer why you should learn Python in the first place. In short, understanding Python is one of the valuable skills needed for a data science career. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

Data science experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. According to Indeed, the average salary for a Data Scientist is $121,583. The good news? That number is only expected to increase, as demand for data scientists is expected to keep growing. In 2020, there are three times as many job postings in data science as job searches for data science, according to Quanthub. That means the demand for data scientitsts is vastly outstripping the supply. So, the future is bright for data science, and Python is just one piece of the proverbial pie. Fortunately, learning Python and other programming fundamentals is as attainable as ever. We’ll show you how in five simple steps. But remember – just because the steps are simple doesn’t mean you won’t have to put in the work. If you apply yourself and dedicate meaningful time to learning Python, you have the potential to not only pick up a new skill, but potentially bring your career to a new level.

How to Learn Python for Data Science





Click to View Our How to Learn Python Infographic



First, you’ll want to find the right course to help you learn Python programming. Dataquest’s courses are specifically designed for you to learn Python for data science at your own pace, challenging you to write real code and use real data in our interactive, in-browser interface. In addition to learning Python in a course setting, your journey to becoming a data scientist should also include soft skills. Plus, there are some complimentary technical skills we recommend you learn along the way.



Step 1: Learn Python Fundamentals Everyone starts somewhere. This first step is where you’ll learn Python programming basics. You’ll also want an introduction to data science. One of the important tools you should start using early in your journey is Jupyter Notebook, which comes prepackaged with Python libraries to help you learn these two things. Kickstart your learning by: Joining a community By joining a community, you’ll put yourself around like-minded people and increase your opportunities for employment. According to the Society for Human Resource Management, employee referrals account for 30% of all hires. Create a Kaggle account, join a local Meetup group, and participate in Dataquest’s learner community with current students and alums. Related skills: Try the Command Line Interface The Command Line Interface (CLI) lets you run scripts more quickly, allowing you to test programs faster and work with more data.



Step 2: Practice Mini Python Projects We truly believe in hands-on learning. You may be surprised by how soon you’ll be ready to build small Python projects. We've already put together a great guide to Python projects for beginners, which includes ideas like:

Tracking and Analyzing Your Personal Amazon.com Spending Habits — A fun project that'll help you practice Python and pandas basics while also giving you some real insight into your personal finance.

Analyze Data from a Survey — Find public survey data or use survey data from your own work in this beginner project that'll teach you to drill down into answers to mine insights.

Try one of our Guided Projects — Interactive Python projects for every skill level that use real data and offer guidance while still challenging you to apply your skills in new ways.

But that's just the tip of the iceberg, really. You can try programming things like calculators for an online game, or a program that fetches the weather from Google in your city. You can also build simple games and apps to help you familiarize yourself with working with Python. Building mini projects like these will help you learn Python. programming projects like these are standard for all languages, and a great way to solidify your understanding of the basics. You should start to build your experience with APIs and begin web scraping. Beyond helping you learn Python programming, web scraping will be useful for you in gathering data later. Kickstart your learning by: Reading Enhance your coursework and find answers to the Python programming challenges you encounter. Read guidebooks, blog posts, and even other people’s open source code to learn Python and data science best practices – and get new ideas. Automate The Boring Stuff With Python by Al Sweigart is an excellent and entertaining resource. But we've put together an entire list of data science ebooks that are totally free for you to check out, too. Highlights include:

Related skills: Work with databases using SQL SQL is used to talk to databases to alter, edit, and reorganize information. SQL is a staple in the data science community, and we've written a whole article about why you need to learn SQL if you want a job in data.

Step 3: Learn Python Data Science Libraries Unlike some other programming languages, in Python, there is generally a best way of doing something. The three best and most important Python libraries for data science are NumPy, Pandas, and Matplotlib. We've put together a helpful guide to the 15 most important Python libraries for data science, but here are a few that are really critical for any data work in Python:

NumPy — A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.

pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.

Matplotlib — A visualization library that makes it quick and easy to generate charts from your data.

scikit-learn — The most popular library for machine learning work in Python.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs like you’d find in Excel or Google Sheets. Kickstart your learning by: Asking questions You don’t know what you don’t know! Python has a rich community of experts who are eager to help you learn Python. Resources like Quora, Stack Overflow, and Dataquest’s learner community are full of people excited to share their knowledge and help you learn Python programming. We also have an FAQ for each mission to help with questions you encounter throughout your programming courses with Dataquest. Related skills: Use Git for version control Git is a popular tool that helps you keep track of changes made to your code, which makes it much easier to correct mistakes, experiment, and collaborate with others.

Step 4: Build a Data Science Portfolio as you Learn Python For aspiring data scientists, a portfolio is a must. These projects should include work with several different datasets and should leave readers with interesting insights that you’ve gleaned. Some types of projects to consider:

Data Cleaning Project — Any project that involves dirty or "unstructured" data that you clean up and analyze will impress potential employers, since most real-world data is going to require cleaning.

Data Visualization Project — Making attractive, easy-to-read visualizations is both a programming and a design challenge, but if you can do it right, your analysis will be considerably more impactful. Having great-looking charts in a project will make your portfolio stand out.

Machine Learning Project — If you aspire to work as a data scientist, you definitely will need a project that shows off your ML chops (and you may want a few different machine learning projects, with each focused on your use of a different popular algorithm ).

Your analysis should be presented clearly and visually; ideally in a format like a Jupyter Notebook so that technical folks can read your code, but non-technical people can also follow along with your charts and written explanations. Your portfolio doesn’t necessarily need a particular theme. Find datasets that interest you, then come up with a way to put them together. However, if you aspire to work at a particular company or industry, showcasing projects relevant to that industry in your portfolio is a good idea. Displaying projects like these gives fellow data scientists an opportunity to potentially collaborate with you, and shows future employers that you’ve truly taken the time to learn Python and other important programming skills. One of the nice things about data science is that your portfolio doubles as a resume while highlighting the skills you’ve learned, like Python programming. Kickstart your learning by: Communicating, collaborating, and focusing on technical competence During this time, you’ll want to make sure you’re cultivating those soft skills required to work with others, making sure you really understand the inner workings of the tools you’re using. Related skills: Learn beginner and intermediate statistics While learning Python for data science, you’ll also want to get a solid background in statistics. Understanding statistics will give you the mindset you need to focus on the right things, so you’ll find valuable insights (and real solutions) rather than just executing code.

Step 5: Apply Advanced Data Science Techniques Finally, aim to sharpen your skills. Your data science journey will be full of constant learning, but there are advanced courses you can complete to ensure you’ve covered all the bases. You’ll want to be comfortable with regression, classification, and k-means clustering models. You can also step into machine learning – bootstrapping models and creating neural networks using scikit-learn. At this point, programming projects can include creating models using live data feeds. Machine learning models of this kind adjust their predictions over time. Remember to: Keep learning! Data science is an ever-growing field that spans numerous industries. At the rate that demand is increasing, there are exponential opportunities to learn. Continue reading, collaborating, and conversing with others, and you’re sure to maintain interest and a competitive edge over time. How Long Will It Take To Learn Python? After reading these steps, the most common question we have people ask us is: “How long does all this take?” There are a lot of estimates for how long takes to learn Python. For data science specifically, estimates a range from three months to a year of consistent practice. We’ve watched people move through our courses at lightning speed and others who have taken it much slower. Really, it all depends on your desired timeline, free time that you can dedicate to learn Python programming and the pace at which you learn. Dataquest’s courses are created for you to go at your own speed. Each path is full of missions, hands-on learning and opportunities to ask questions so that you get can an in-depth mastery of data science fundamentals. Get started for free. Learn Python with our Data Scientist path and start mastering a new skill today! Where Can I Learn Python for Data Science? There are tons of Python learning resources out there, but if you're looking to learn it for data science, it's best to choose somewhere that teaches about data science specifically. This is because Python is also used in a variety of other programming disciplines from game development to mobile apps. Generic "learn Python" resources try to teach a bit of everything, but this means you'll be learning quite a few things that aren't actually relevant to data science work. Moreover, working on something that doesn't feel connected to your goals can feel really demotivating. If you want to be doing data analysis and instead you're struggling through a course that's teaching you to build a game with Python, it's going to be easy to get frustrated and quit. There are lots of free Python for data science tutorials out there. If you don't want to pay to learn Python, these can be a good option — and the link in the previous sentence includes dozens, separated out by difficulty level and focus area. If you're serious about it, though, it may be best to find a platform that'll teach you interactively, with a curriculum that's been constructed to guide you through your data science learning journey. Dataquest is one such platform, and we have course sequences that can take you from beginner to job qualified as a data analyst or data scientist in Python. Is Python Necessary in the Data Science Field? It's possible to work as a data scientist using either Python or R. Each language has its strengths and weaknesses, and both are widely-used in the industry. Python is more popular overall, but R dominates in some industries (particularly in academia and research). To do data science work, you'll definitely need to learn at least one of these two languages. It doesn't have to be Python, but it does have to be one of either Python or R. (Of course, you'll also have to learn some SQL no matter which of Python or R you pick to be your primary programming language). Is Python Better than R for Data Science? This is a constant topic of discussion in data science, but the true answer is that it depends on what you're looking for, and what you like. R was built with statistics and mathematics in mind, and there are amazing packages that make it easy to use for data science. It also has a very supporting online community. Python is a much better language for all-around work, meaning that your Python skills would be more transferrable to other disciplines. It's also slightly more popular, and some would argue that it's the easier of the two to learn (although plenty of R folks would disagree). Rather than reading opinions, check out this more objective article about how Python and R handle similar data science tasks, and see which one looks more approachable to you. How is Python Used for Data Science? Programming languages like Python are used at every step in the data science process. For example, a data science project workflow might look something like this:

1 Using Python and SQL, you write a query to pull the data you need from your company database. 2 Using Python and the pandas library, you clean and sort the data into a dataframe (table) that's ready for analysis. 3 Using Python and the pandas and matplotlib libraries, you begin analyzing, exploring, and visualizing the data.

4 After learning more about the data through your exploration, you use Python and the scikit-learn library to build a predictive model that forecasts future outcomes for your company based on the data you pulled.

5 You arrange your final analysis and your model results into an appropriate format for communicating with your coworkers.