Once you decide you want to get into data science, you need to make some decisions, especially if you transition from another field. For example, are you going to have to learn a programming language? And if so, do you have to learn Python?
You can learn data science without Python. You can learn other languages such as R or Perl or work in a data science field that does not require programming skills. Not learning programming languages limits what kind of work you can perform and make you less competitive in the job market.
Read on to learn more about who uses Python, why it is popular, and whether learning it is difficult. And if you are looking for an alternative to Python, find out what other programming languages data scientists use.
Important Sidenote: We interviewed 100+ 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!
Table of Contents
Who Uses Python?
Python is one of the most popular programming languages. So popular that based on analysis by Tiobe, Python is now the second most commonly used language by programmers, after R.
If you have used YouTube, Instagram, or Dropbox, then you have visited websites that use Python. Disney Animation and Industrial Light Magic (the Star Wars studio) use it. Companies such as Google and Yahoo! and organizations such as NASA and CERN (the particle physics lab in Europe) also rely on Python.
So what makes it so popular that programmers and data scientists use it?
What Does Python Do and Why Is It Popular?
Python is a versatile language used to build software, do front end or full-stack web development, create mobile apps, or work with databases. It is also utilized for machine learning, database, file, and Excel manipulation.
And that’s not all. Python has a host of benefits, including:
- You can learn the basics of Python for free through the Python Software Foundation’s tutorials.
- Python is a free open source coding language. If you want to create extensions or modify it, you can.
- You can find tools such as libraries, frameworks, and packets to increase your productivity.
- A large and supportive community of Python users exists, and you can find ideas, advice, and even mentors.
Python-related jobs are everywhere. Check for Python jobs on Indeed. Not only are there tons of jobs, but the pay is not half bad either. These are all excellent reasons to invest time in learning Python.
How Difficult Is It to learn Python?
Although there is consensus on how quickly someone can learn Python, most people agree on several principles:
- People with previous coding experience can learn it more quickly.
- Python code is easier to understand and read than other languages. You do not need to be a programmer to read Python code.
- The language is widely used, free to use, open-source, and can run on multiple platforms.
- You can learn the basics quickly, but mastering Python will take longer.
Be cautious when you read that you can master Python in a month. Claims for mastering the language are time-dependent. Typically, learning Python in a month will require you to spend 8 hours a day on Python. The advice given more commonly is to devote 2 to 3 hours a day spread over six months.
How to Learn Python: Basic Steps
If you are going to learn Python for data science, you have an advantage over someone who has no idea what they will do with it. That’s because, as you have already learned, Python can do so much that it is easy to spend time on aspects of Python that you will never use in data science.
For example, you could spend time developing mobile apps, but that would not be a good use of your time. Projects focused on data processing will be more helpful to you. Scripts that can automate tasks will be useful.
- Learn the basics. You won’t be able to work in your area until you learn the basic syntax of Python. Even though it is necessary, don’t agonize over learning everything about Python. The next step is more important.
- Begin doing structured projects as soon as you have learned the basics. You can find many sources for projects online, including projects focused on data science. The book Automate the Boring Stuff will give you ideas for projects that will simplify your life.
- Once you have completed structured projects, go ahead, and create your own projects. Projects that include data analysis would consist of tools and algorithms that predict future events, such as the weather.
- Once you find your projects are easy, ratchet up the difficulty. Make your program run faster, handle more data, or be more useful to others.
Tip: Even though you will not be using games in your work, it can be satisfying to make a simple game as a beginner’s project.
Are There Comparable Programs to Python?
Many data scientists prefer R because it was designed to work with data. Python’s strength as a language with so many applications is a weakness in data analysis. A program such as R that is specific to data means the packages and libraries are tools focused on data analysis and representation.
Like Python, R is an open-source language with a large community of developers. There is debate about which one is easier to learn—Python or R, but if you aren’t interested in learning a programming language for anything but data science, you might want to choose R.
Julia is a newer language that is getting a lot of press. Like R, it is geared for data science. Other alternatives include MATLAB, Fortran, and Perl.
Will I Have to Learn These Programs to Get a Job in Data Science?
As a data scientist, you will need to master several skills. For example, business analytics is essential. If you can help a business increase its sales, develop a better product, or save money, you have a highly marketable skill.
Knowledge of statistics is essential. A data scientist needs to understand how statistics can show patterns and be able to turn the numbers into data that anyone can understand. Linear algebra can help you understand the data and how to apply it to real-life situations.
If you have those skills, then you can always learn the coding skills on the job.
Tips on Learning to Code
If you are starting to code, consider these tips. They could keep you from the if-I’d-only-known moments later.
- Start with a language your friends know. Instead of obsessing over which language to start with by reading blogs, chat boards, Reddit threads, start with the one that your friends or acquaintances know. That way, you can reach out to them when you need help instead of someone who might have an agenda (like selling you a course).
- Keep the purpose of coding in mind. The purpose of coding is not to learn a language but to create something helpful. Use sites such as CodePen or Repl.it to get started quickly.
- Learn the principles. New languages will come out, languages will be updated, and you will have to keep up. There is no way that you or anyone can know everything. But the principles are not going to change—a computer needs to be told what to do, and a programming language uses syntax rules to make that happen.
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.
You can learn data science without Python, but at some point, you will need to learn a programming language. Many data scientists prefer using another language, especially R, so if you want to learn just enough coding for a job, then investigate learning R or a similar language. However, Python is a more flexible language, is used by many data scientists, and will make you more marketable.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ 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.
Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.