As reliable data becomes more and more important to nearly every industry, data scientists who can analyze and interpret this data are more in demand than ever. With multiple programming languages out there, however, which is best to learn to become a data scientist? Is mastering Python, one of the most popular options, enough to succeed in this career?
Python is enough for data science, as it is widely used throughout the industry and designed to work well for both big data and app development. While experienced programmers may choose to master two programming languages, Python’s popularity ensures that users will be able to work in the field.
The rest of this article will take a closer look at why Python is sufficient for learning data science, focusing on its versatility, user support, and effectiveness when used for big data. It will also outline some of the methods you can use to begin learning this popular programming language.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
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Why is Python Enough for Data Science?
If you’re at all interested in data science, you’ve likely heard about Python. The programming language, which was invented in the late 1980s and released in 1991, has become increasingly popular. In fact, according to a 2020 study by Statista, Python is now the most popular programming language available. It’s also the programming language used by incredibly successful companies like Venmo, Reddit, and Instagram, further increasing its appeal.
While many sources, including Analytics India Magazine, will tell you that Python alone is not enough to land a high-ranking data scientist job, Python is perfect for someone looking to learn a programming language to break into the industry. Entry-level data scientists specializing in Python can make over $70,000 a year, even without mastering the full back-end technology stack. To put it simply, you can definitely work in data science by focusing on Python.
So what exactly makes Python so popular? Let’s take a closer look at some of the features that have made it the programming language of choice for so many.
One of Python’s biggest benefits is its versatility. Described by its producers as a general-purpose programming language, Python can be customized to develop both desktop and web applications. This makes it perfect for building something that’s never been seen before. Since Python is relatively easy to learn and allows beginners to use fewer lines of code than with other languages, users can focus more on getting creative than the nitty-gritty details.
Accordingly, developers love Python for scripting websites and applications. It’s used in many other different capacities daily across different industries, however. These uses include adding statistical codes into production databases and implementing algorithms, explains DZone. Regardless of what you’re trying to do, Python can likely help you to do it.
Python’s versatility is due in large part to it being open-source. This not only means that it’s free but also that it is developed using a community-based model. Python has an incredibly active open-source community, with programmers from around the world offering advice, troubleshooting issues, and literally building new ways to use this programming language. Python also runs on both Windows and Linux systems and supports multiple platforms.
The open-source license is especially beneficial to organizations looking to modify behaviors from the standard Python settings to create a new, tailored version for development, explains Tech Vidvan. Custom versions can then be distributed within an organization or even to external programmers. Being able to create a purpose-built system that works specifically for the organization using it leads to greater overall efficiency and productivity.
Easy to Troubleshoot
Perhaps the best thing to come from Python’s open-source license is its wide variety of libraries. As previously mentioned, Python is incredibly popular and widely used in industrial and academic circles. This has led to the creation of huge numbers of analytic libraries, a number SimpliLearn predicts will increase as more people begin using this programming language. As user-created content increases, problem-solving will become easier than ever.
Many Python users find that any problem they’re having, regardless of how difficult it may seem, can be solved by conducting a simple Internet search. With the number of users increasing every day, the chances are that another Python user has experienced the same troubles you have. Troubleshooting issues in the community is actually a great way to learn the language. Most Python certification courses will devote time to learning how to debug and handle errors.
With the large user base, there are hundreds of well-tested libraries programmers use daily. Some of the most popular libraries include:
- Machine Learning
- Data Analysis
- Numerical Computing
- Statistical Analysis
Visualization is worth taking a closer look at. Using a visualization library can help users uncover useful insights from data, presenting information in an easy-to-understand format. While visualization libraries exist for the most niche needs, some popular libraries are used across multiple fields and disciplines. Well-known interdisciplinary visual libraries include:
No matter what type of data you’re working with or what insights you’re trying to glean from them, there is undoubtedly a visualization library to help.
Good for Big Data
Big data is on the rise. Virtually every business is seeking a way to manage the enormous volume of data they receive daily and gain insights from this data that can help inform decisions and lead to increased earnings. Data scientists with experience with Python are specially equipped for these tasks, as the programming language is well-known for being great for use with big data.
Python is perfect for big data for many reasons, including its open-source license and a large number of data libraries. It’s also a great fit due to its simplicity of design, as mentioned earlier. Since Python uses fewer lines of code than similar programming languages, it is well-suited for handling large amounts of data and performing repetitive tasks for long periods of time. Python is also a great fit for big data because of its:
- Speed: Python works quickly, making it ideal for handling the sheer quantity of data that makes up big data. Python supports prototyping ideas, which allows the code to run faster, explains Towards Data Science. This also helps to make the code transparent and readable, making it easier to maintain.
- Scope: Python’s programming allows it to support advanced data structures like lists, dictionaries, and sets. In essence, this allows users to simplify data operations, enhancing the scope, and speeding up the entire process.
- Data Processing Support: This feature is built into Python. It’s also considered one of the most important requirements in big data, making Python the best programming language for the job.
Ways to Learn Python
Now that you know why Python is great for data science, you’ll need to know how to get started. While you can pay for online courses through many universities, as well as pay to get officially certified, there are many free options for programmers of all skill levels to master Python at home. Take a look at some of the options below.
Python itself offers a Wiki page full of beginner guides and tutorials that are constantly updated by the community. You can choose from videos and written instructions, as well as practice your new skills with practice assignments and interactive tools. You can even sign up for email courses that will deliver lessons directly to your inbox almost daily.
YouTube is home to tons of videos on learning Python, from the very basics to the most advanced techniques. Full classes are even available on the site, with over twelve hours of lessons presented by expert programmers. I suggest getting started with this introductory video from freeCodeCamp.
Even though Python exists in a digital world, sometimes there’s no better way to teach yourself something than with an old-fashioned book. Even an experienced programmer can benefit from having a reference book on hand. Amazon is home to many highly-reviewed, affordable options like Python Crash Course, 2nd Edition. This hands-on guide uses projects like building an arcade game and a simple web app to teach programming concepts.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
While a true master of programming will want to master different programming languages eventually, learning Python is certainly enough to work in data science. Python offers a relatively simple method for programmers to gain the skills they’ll need to work in big data or app development. Python’s active open-source community also means that beginners will be able to troubleshoot problems and find creative solutions to data analysis simply.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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