How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists with Serious Guides

Please be noted that the questions below are not in order, therefore feel free to skip to any part of the questions where you find suitable.

I hope that by sharing my experience in this post would shed light on how to pursue a data science career and give you some general guides to hopefully make your learning journey more enjoyable.

Let’s get started!   The International Data Corporation (IDC) predicts that worldwide revenues for big data and business analytics will reach more than l$210bn in 2020.

According to the LinkedIn WorkForce Report in August 2018 for the United States, there was a national surplus of people with data science skills in 2015.

Three years later, the trend has changed tremendously in the opposite way as more companies are facing shortages of people with data science skills with big data being increasingly used to generate insights and make decisions.

Economically speaking, it is all about SUPPLY and DEMAND.

The good news is: The “tables” are now turned.

The bad news is: With rising job opportunities in data science, still, a lot of aspiring data scientists are facing challenges in getting their foot in the door simply because of their lack of data science skills gap relative to the requirements in the current job market.

In the coming section, you’ll see how to improve data science skills to close the “gap”, stand out among pool of other candidates and eventually increase your chances in landing your dream job.


What are the skill sets required and how to cover them up?.I’ll be very honest with you.

To learn ALL the skills sets in data science is next to impossible as the scope is way too wide.

There’ll always be some skills (technical/non-technical) that data scientists don’t know or haven’t learned as different businesses require different skill sets.

In general — in my opinion based on my experience and learning from other data scientists — there are some CORE skill sets that must be learned to become a data scientist.

Technical skills.

Math and statistics, programming, and business knowledge.

Despite an excellent proficiency in programming regardless of the languages used, we— as a data scientist — should be able to explain our model results to stakeholders in the language of business context and supported by math and statistics.

I still remember when I first started out in data science I read this textbook — An Introduction to Statistical Learning — with Applications in R.

I highly recommend this textbook for beginners as the book focuses on the fundamental concepts of statistical modelling and machine learning with detailed and intuitive explanations.

If you are a mathematically hardcore person, perhaps you would prefer this book: The Elements of Statistical Learning.

To learn programming skills, especially for beginners without prior experience, I’d suggest to focus on learning one language (personally I prefer Python!????) since the concepts are applicable to other languages if needed and Python is more easier to learn.

The importance and and usage of Python or R has been a subject of debate in data science.

Personally, I think the focus should be on how you can help businesses solve problems, regardless of the languages used.

Finally, I can’t stress enough that the understanding of business knowledge is extremely crucial as I have also included in one of my articles (You can refer to it here).

Soft skills.

In fact, soft skills are more important than hard skills.

Surprised?.I hope not.

LinkedIn surveyed 2,000 business leaders and the soft skills that they’d most like to see their employees have in 2018 are: Leadership, Communication, Collaboration and Time Management.

And I truly believe these soft skills play an essential part in data scientist’ day-to-day work.

In particular, I learned the hard way on the importance of communication skills which you can read it here.


How to choose the right bootcamps and online courses when there are plenty of them out there?.With the hype surrounding AI and data science and many people jumping on the bandwagon, a lot of MOOCs, bootcamps, online courses, workshops (Free/Paid) are mushrooming to hopefully not “miss the boat”.

There are many resources out there.

Be resourceful.

So the question is: How to choose the learning materials that are suitable to you?My approach to filter and select the right online courses/workshops for me: 3.

Is learning from open source sufficient to become a data scientist?.I’d say that learning from open source is sufficient to get yourself started in data science and anything beyond is to develop your career further as a data scientist, again, depending on business needs.


Should a beginner (from a totally different background) start with reading materials to understand the basics?.What book would you suggest?.There’s no fixed path in learning as all roads lead to Rome.

Reading materials is definitely a great start to understand the fundamentals which I did the same way as well!Just be aware of not trying to read and memorize nitty-gritty of the maths and algorithms.

Because chances are, you’ll forget everything without really applying the concepts to real problems when it comes to coding.

Just know and understand enough to get yourself started and move on to the next step.

Be practical.

Don’t try to be perfect in knowing everything simply because perfectionism is unknowingly the best reason of procrastination and not moving forward.

Below are some of the books that I’d suggest to understand the basics of Python, machine learning and deep learning (Hope it helps!): 5.

How to balance between understanding business problems (formulating solutions) and developing technical skills (coding, core math knowledge etc.

)?.I started off by developing my technical skills before going into understanding business problems and formulating solutions.

Business problems give you WHAT and WHY.

To solve a business problem, one has to first how to solve the problem.

And the HOW comes from technical skills.

Again, the approach depends on situation and my suggestion is mainly based on personal experience.


How can we overcome the challenges of starting a career as a data scientist?.One of the major challenges faced by many aspiring data scientists (including me) is that data science is an ocean of information .

We could easily lose our focus by getting overwhelmed with all the advice and resources (Online courses, workshops, webinars, meetups, you name it…) that come from different directions.

Stay focused.

Know what you have and what you need and ALL IN.

Throughout my data science journey, challenges are uncountable but are also what have shaped who I am today.

I’ll try my best to explain the main challenges faced by me and how to overcome them: Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work.

And the only way to do great work is to love what you do.

— Steve Job 7.

How to put my work experience in my resume so that I will be hired and my experience will be counted?.I believe there is a misconception here — you’ll not be hired solely based on the experience in your resume.

In fact, your resume is one of the ways to get the first entrance ticket to your next stage of application — interview.

Therefore, learning how to write work experience in resume is truthfully important to get the entrance ticket.

Studies have shown that the average recruiter scans a resume for six seconds before deciding if the applicant is a good fit for the role.

In other words, to pass the resume test, your resume only has six seconds to make the right impression with a prospective employer.

Personally, I referred to the following resources to polish my resume: 8.

What kind of portfolio can help us to get a first job in data science or machine learning?.In my very first article on Medium, I mentioned the importance of building a portfolio.

Having a well-polished resume is not enough to get you an interview without a good portfolio.

After the first glance at your resume, prospective employers want to understand more about your background and this is where your portfolio comes in.

While you might wonder how to build a portfolio from scratch, start by documenting your learning journey.

Share your learning experience, mistakes, takeaways — technical or non-technical — through social media platforms (LinkedIn, Medium, Facebook, Instagram, Personal blog — it doesn’t matter).

Interesting in talking in front of a video recorder?.Then start by making videos (interview with other aspiring/well-established data scientists) and share on YouTube.

Good at writing?. More details

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