How to Transition into Data Science from a Different Background?
Are you interested in data science but don't have a relevant background? Don't worry, you can still learn the skills and tools you need to become a successful data scientist.
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If you are from a non-computer sciences background, you know the amount of work it is, to crack a job in the world of Data Science. The opportunities of Data Science call for a lot of people but with Data Science being so new to the world (not more than a decade has passed!), there are very few people who are organically qualified to be data scientists as per the norms of the corporate world.
This industry screams growth and opportunity and that is one of the prime reasons why someone would want to transition into the world of Data Science though coming from a very different background.
Note: I am one of the few who know that Data Science can work out for someone, not from a CS background and I hope this article helps you to find the guidance you need to boost your journey.
In this article, we’ll go over how you should approach Data Science as a career transition based on three different segments:
- For someone who has never touched any subject closely related to Data Science in college.
- For someone from a non-CS background but with a couple of relevant subjects relating to Data Science & who wants to be a Data Scientist why not?
For someone who has been working in an industry for a long time but now wants to switch to the fascinating and daunting world of Data Science.
Note: The views in this article are mine alone, feel free to have your own opinion or approaches towards the transition. I am wishing you the best.
Let’s get right into it.
Stage I: You’re not closely related to Data Science but you want to get into it.
Well, in this case, I would say the only effort that you will exert is mental and it needs a lot of patience. There’s no doubt that Data Science is a very technical subject and involves a lot of numbers.
P.S. Try checking this out first, to identify what is the road to follow to make it big in Data Science. You can then move on and understand the things you need to note to accelerate your journey!
Things to note in this case:
- Data Science is just like any other subject, you can always start learning it whenever you find the time.
- It is always early enough, never too late to start.
- Data Science is a combination of computer sciences, statistics, college-level math, lots of logical thinking, and programming languages with other tools that you can use.
- Chart out your skill in each of the domains (or particularly the one you want to go pro in) and go ahead with learning more about each.
- If you want to get into analytics, push your statistics knowledge and also data cleaning, etc. (learn Excel as much as you can, its a blessing for analytics in small datasets and the best tool to begin with)
- For Data Viz, try learning Tableau, PowerBI, etc. but at the same time, understand how visualizations work and how you can make better visuals and dashboards.
- Primarily for the first 2 months of your learning, focus on learning these in the same order — Excel, SQL, Tableau, and if time permits, Python basics.
With this, you can move into stage II and continue learning from there.
Note: It will take time if you are new to Data Science, so just gotta be patient and trust the process. It will work out!
Stage II: You’ve been related to some subjects in Data Science but you haven’t been into it entirely.
This was a similar stage to mine and I can tell you, that it takes quite an effort to study Data Science. It depends on a lot of factors as you will see eventually, but it's not very difficult with the way the world has been opening doors for open-source learning and offering knowledge to anyone who desires it (even if they come from a non-CS background).
Things to note in this case:
- Data Science is a tough field if you try to look at it as a whole. Just start seeing every component that you want to focus on as pieces of the big puzzle, and you’ll be just fine.
- If you want to dwell on the Data Viz side of Data Science, focus on understanding how dashboards and data connections work and learn data storytelling.
- For someone who wants to get into Machine Learning, try understanding how to work with Python or R, if you go with Python — learn libraries like NumPy, Pandas, Scikit Learn, SciPy, Matplotlib, and Seaborn.
- Understand the theoretical concept behind ML to also make more sense of your algorithms. It should take time but understanding the process is more important than coding a high-grade ML algorithm.
- If you want to push your analytics side — learn Inferential Statistics, and understand how data can be used to make data-driven solutions. Learn how to work with data that is unstructured and clean as many datasets as possible.
- Go beyond the normal CRUD commands in SQL to understand perfectly how JOINS work and how to work with MySQL/PostgreSQL. If you want to push it with Excel, learn how to use the Data Analysis Toolpak and how to make Macros.
- Understand how time series data works and know how to pull data from sources and make time series forecasts to push your learning.
More often than not, you will be one of the masses that will learn a lot of tools and get a hang of everything at an intermediate level.
I would highly recommend you to find your niche and go advanced in it. With the amount of knowledge and competition out there in the data science world, try finding your niche and make sure you find your mark in the competition with your unique skills.
Stage III: You’re a pro in an industry already but you want to start in Data Science now!
There are people I know who have been in amazing positions in their life before deciding that they want to be a part of Data Science. It is natural to want to have a change in career after a long time of working in a particular industry and there are a few things I have sourced from people I know who have been in a similar position and can help you in this case.
Things to note in this case:
- Once you are a professional in a particular industry, it might be because of a switch in life choices or a demand to upskill, that brings you to Data Science
- In any case, management roles in Data Science would be happier to have someone with heavy corporate exposure in the industry
- Upskilling in Data Science with your existing knowledge in an industry can be one of the best things that can happen with your career transition. Data Science, while playing on Computer Sciences and also on tools and techniques, relies heavily on domain knowledge.
- With enough domain knowledge, you can be a data scientist in your field by harnessing the power of data for more than what is already being done
- Industry-specific KPIs and metrics can be further developed and automated with Data Science and can open new doors for you too.
- With the additional knowledge of data science tools in your arsenal, you can become trainers in your field and help budding data scientists. The possibilities are unlimited.
- The tools and skills to learn in this stage are the same as what was being done in Stage I and Stage II mentioned earlier in this article.
In any case, it’s best to learn data science and stick to your field of profession because of the way the world is transitioning into data science today. Everything you do, can, and have data involved, and using that in your decision-making, will only make your decisions a whole lot better.
It's tough to transition into the world of data science not because it's difficult to get a job in, but because there are so many people vying for it. The opportunities are seen by everyone and people know that -Data is the future- and so is Data Science.
For anyone who is already immediately skilled in Data Science, stay tuned, I’ll have another part for this article coming in where we discuss how you can go from pro to expert in Data Science.
Yash Gupta is a Data Science Enthusiast & Business Analyst, Freelance Technical Writer, and a Blogger at Medium.com. He's interested in sharing data science knowledge with a larger audience in an easy-to-consume way. He wishes to share his knowledge with everyone who enjoys data as much as he does. He tries to learn something new everyday and loves guiding budding data enthusiasts on their journey.
Original. Reposted with permission.