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8 common questions from aspiring data scientists, answered

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So, you want to be become a data scientist? Great. But you have zero experience and have no clue how to get started in this field. I get it. I’ve been there and I definitely feel you. This is why this post is for you.

All the questions below came from the community through techinasia LinkedIn post, email, and other channels. I hope that by sharing my experience, you will be enlightened on how to pursue a data science career and make your learning journey fun.

Let’s get started!

1. What are the skills required in data science and how can I develop them?

I’ll be very honest with you. To learn all the skills in data science is next to impossible as the scope is way too wide. There will always be some skills (technical/non-technical) that data scientists wouldn’t know or haven’t learned as different businesses require different skill sets.

But based on my experience, there are generally some core skill sets that an aspiring data scientist should learn.

Technical skills

This include math, statistics, programming, and business knowledge. Despite having excellent programming skills, we as data scientists  should always be able to explain our model results to stakeholders in business language, supported by math and statistics.


To learn math and statistics, check out ClaoudML, which was created by Randy Lao.

I also recommend reading the book An Introduction to Statistical Learning, which I read when I was first starting out in data science. It’s really for beginners because it 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 The Elements of Statistical Learning.

To learn programming skills, I suggest focusing on learning just one language (I personally prefer Python) first, since the concepts are also applicable to other languages. Python is also easier to learn.

Finally, I can’t stress enough the importance of business knowledge. Understanding how a business works is extremely crucial, as I have outlined in one of my articles.

Soft skills

Actually, soft skills are more important than hard skills. Surprised?

According to LinkedIn’s survey, the soft skills companies need most in 2018 are leadership, communication, collaboration, and time management. And I truly believe that these skills play an essential part in a data scientist’s day-to-day work.

2. How can I choose the right bootcamps and online courses when there are plenty of them out there?

Here’s my approach to filtering and selecting the right online courses/workshops that are suitable for me:

  • Understand that there’s no single best course that can cover all the materials you need. Some courses overlap in some areas and it’s not worth the bucks to purchase different courses only to repeat most of the materials.
  • Know what you need to learn in the very first place. Never dive into a course simply because of the catchy titles. Remember the technical skills mentioned earlier? Check out data science job descriptions, find out the common skills needed and the ones you lack, and go and search for courses that can help improve your knowledge (theoretical and hands-on).
  • Research on the best courses offered by different platforms.Once you’ve shortlisted a few courses that suit your needs, check out the reviews (very important!) first before you pull out your wallet and enroll. There are also many free courses available on Coursera, Udemy, LyndaCodecademyDataCampDataquest, and many more. Did I also mention YouTube?

Here are some of my personal favorite courses that have helped me tremendously:

  • Machine Learning by Andrew Ng, co-founder of Coursera
  • Python for Data Science and Machine Learning Bootcamp by Jose Portilla
  • Deep Learning A-Z: Hands-On Artificial Neural Networks by Kirill Eremenko and Hadelin de Ponteves
  • Python for Data Science Essential Training by Lillian Pierson
  • The Ultimate Hands-On Hadoop — Tame your Big Data by Frank Kane


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. But you will still need to develop your career further, depending on business needs.


4. 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 also did.

But just be aware of not trying to read and memorize the nitty-gritty of algorithms. 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.

Below are some of the books I recommend to understand the basics of Python, machine learning, and deep learning:


5. How can I find 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 deep into business problems and formulating solutions.

Business problems give you the what and the why. The how comes from technical skills. Again, the approach depends on the situation, and my suggestion is mainly based on my personal experience.


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6. How can we overcome the challenges of starting a career in data science?

One of the major challenges faced by many aspiring data scientists (including myself) is that data science is an ocean of information. We could easily lose our focus by getting overwhelmed with all the advice and resources that come from different directions. So, stay focused.

I’ll try my best to explain the main challenges I faced and how I overcame them.

I was confused with so many resources

I filtered out the noise through the hard way:

  • Listening to podcastsWatching webinars
  • Reading plenty of articles on how to pursue a career in this field
  • Experimenting with different online courses
  • Engaging with the data science community on LinkedIn and learning from them

Ultimately, I focused only on the most helpful resources, which I’m sharing in this post.

I almost gave up

The thought of giving up came to my mind when the learning curve was too steep and I started doubting myself. Am I really capable of doing this? Am I really pursuing the right path?

Passion and patience redirected me back and let me stay on my path. So, keep grinding and hustling.

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 Jobs

Getting a job as a data scientist (or a similar job scope but different title)

I wish I have read these articles earlier:  How to get a job as a Data Scientistand The two sides of Getting a Job as a Data Scientist.

Getting a job was no easy task for me due to the competitive nature of the job market. I submitted tons of resumes to no avail. Something must be wrong, I thought deeply.

So, I revamped my approach and started networking: attending meetups and seminars, sharing my learning experience online, approaching prospective employers in career fairs and sharing sessions in a more systematic way, doing follow-ups upon submitting my resumes, etc. Eventually, things started to change and opportunities knocked on my door.

7. How should I put my work experience in my resume to get hired?

I believe there is a misconception here . You will not be hired solely based on the experience you put in your resume. It’s a way to get the first entrance ticket to the next stage of the application process: the interview.

Studies have shown that the average recruiter scans a resume for six secondsbefore deciding whether the applicant is a good fit for the role. In other words, 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 me get my first job in data science or machine learning?

To build a portfolio from scratch, start by documenting your learning journey—experiences, mistakes, and takeaways (technical or non-technical)—through social media or your personal blog.

Are you comfortable talking in front of a camera? Then start making videos. Are you good at writing? Then start writing on topics you’re passionate about. You can also do podcasts.

My point here is that, with the internet, the opportunities are seriouslyabundant to build your portfolio and gain traction or potentially attract the attention of your prospective employers.

One of the best decisions I’ve made was to engage with the data science community on LinkedIn and document my learning journey on Medium. I learned the most on LinkedIn, given the close-knit data science community in a knowledge-sharing environment.

Gradually, I learned (and am still learning) how to build my portfolio. I received messages from different recruiters offering job opportunities. I even got the chance to grab some coffee and have quick chats with some of them.

Final thoughts

Now that you have the answers to your questions, it’s time to take massive actions toward achieving your goals. No action is too small to make a difference. Just move forward one step at a time. When you’re on the verge of giving up, persistence is key.

If you have any questions or comments, feel free to share your feedback in the comments section.

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