My Journey Into Data Science

Quite a number of people have asked me about my switch from Chemical Engineering to Data Science. How did I do it? When did I do it? Why did I do it? I felt today (January 6, 2018) was a befitting day to answer these questions as it marks the third year since I enrolled for my first programming course. I hope sharing my story would give some insight into what I did to become a Data Scientist and encourage budding “anythings” everywhere to pursue their passion fiercely.

My first exposure to Data Science was from a book that had nothing to do with Data Science

In March 2014, I stumbled on a book called The Power of Habit: Why We Do What We Do in Life and Business by Charles Duhigg. In a section of the book called The Habits of Organizations, Charles wrote about a large retail chain that used data on what a female customer bought to predict the likelihood that she was pregnant. To put it lightly, I was mind blown and I had to find out more.

I searched everywhere for what this sorcery was called. After a few months and with the help of my friends, I stumbled on something very similar to what I read in The Power of Habit. It was called was Business Analytics.

This discovery came at a tipping point for me because, at the time, I was in my final year of college and had just finished an internship with an Oil & Gas company. My experience there made me weary of taking up Chemical Engineering as a career because I felt like it just wasn’t for me. This realization also made me open to new challenges and pivoting career wise. Business Analytics seemed to fit right into that.

I created my first Data Science learning path from an answer on Quora

By 2014, I had graduated and began my National Youth Service Corps. During my NYSC, I stumbled on Quora from a Twitter recommendation and I loved it.

In case you are wondering, IDEALLY, NYSC is a one-year mandatory program in Nigeria where you are deployed to a state you aren’t affiliated with to serve in some capacity as either a government worker, teacher or anything else really.

On Quora, I found out that Business Analytics had many names and one was Data Science. I also found a very helpful answer which I recommend to this day for anyone looking to start out as a Data Scientist: How can I become a Data Scientist?

This answer helped shape my first ever learning path for Data Science in January 2015 (Forgive my terrible handwriting).

Written January 2015. Other courses on the left side of the page are The Analytics Edge and Google Analytics

Written January 2015. Other courses on the left side of the page are The Analytics Edge and Google Analytics

I completed 15 MOOCs on Data Science within a year

I primarily learnt Data Science through online courses. I never used a book (I tried). All the courses were free (because I didn’t care for a certificate) and where they were not free like Coursera, I got 100% Financial Aid.

I kissed a lot of frogs when it came to online courses so if you are looking for a loose guide on how to get started in Data Science I’ll save you the stress and focus only on the courses that were worthwhile.

1. Learnt Programming

This was the very first thing on my learning path and the scariest of them all. It was scary because I didn’t have a Computer Science background and the only time I was exposed to programming in College, I absolutely hated it. However, this time I felt I had all the time in the world and nothing to lose so I enrolled for Codecademy’s Learn Python course.

The course was so hard and a lot of it did not make sense to me. I could spend as much as two weeks trying to get a while loop to work and I had no idea what file I/O meant but by sheer brute force, I completed the course.

This was the first time I completed an online course after numerous attempts to do so previously. That gave me some confidence to keep on learning.

2. Learnt core Data Science

A lot of people ask me why I choose to use R over Python. It was by sheer coincidence that my first exposure to Data Science was in R from a course called The Analytics Edge from MIT on edX.

The ten-week course uses a case study approach to teach different parts of Data Science from Machine Learning to Visualization to Optimization using R. It was very demanding and very rewarding. The amazing experience I had on this course is what makes me lean a bit more to R than Python. The course gave me a great foundation and I still refer to my notes from 2015 sometimes.

3. Other helpful courses

Another course I loved, which I took towards the end of 2015, was Data Visualization and Communication with Tableau from Duke University on Coursera. It’s a five-week course that gives a great foundation on the use of Tableau. The instructor is amazing and the best I’ve been exposed to so far.

The next on my list would be Managing Big Data with MySQL from Duke University on Coursera. It’s a four-week course with the same amazing instructor as the Tableau course and teaches both MySQL and Teradata.

Others worth mentioning are: Introduction to BigData with Apache Spark (A four course series) from UCBerkeley on edX and Excel for Data Analysis and Visualization from Microsoft on edX.

How I started my blog — where the real learning started

If you read a lot of Quora answers or articles on how to become a better Software Engineer/Data Scientist/Designer and the likes, you’d see a recurring advice: Do personal projects to deepen your skill set. I had tried to do that a few times in 2015 but I wasn’t able to do anything reasonable because, frankly, I was not ready.

By 2016, I had slowed down on online courses because 90% of the courses had the same content and assumed you’re a beginner so it became a bit repetitive. By this time, I felt I was ready to start doing personal projects using a blog. The writing part was not an issue because I used to write in High School. My issue, however, was around consistency and creativity. Was I creative enough to put together interesting projects and could I do it consistently? You never know until you try, right? And that’s how I started my blog The Art and Science of Data in June 2016. My learning grew exponentially working on the content for my blog.

I wrote my first two posts within a month and then went on a year-long hiatus

My first post was Predicting The English Premier League Standings which I posted in September 2016 and then What Twitter Feels about Network Providers in Nigeria which was posted in October 2016. The amount of positive responses absolutely floored me. I got about 1,500 views and numerous responses on both posts and for the first time, I felt confident in my skills.

This experience taught me that creativity is not some talent that you either have or don’t. Creativity is born by experience and confidence in your skills because the possibilities of what can be done expands with the more you know.

Then I went on a year-long hiatus on my blog. This happened for many reasons.

  1. I had tried to write a blog post in December 2016 that was a hot mess. I cleaned it up later and used it for my Women in Machine Learning and Data Science Workshop called The ABC-XYZ of Data Science.
  2. After that, I had what I’ll call “The Data Scientist’s block”. I literally had no ideas and could not think up anything useful or interesting.
  3. My approach to my blog is a bit different from most data science blogs because mine involves a lot of research and iterations. It also makes my publishing cycle much longer than others.
  4. Work was grueling and adulting was catching up with me so I became a couch potato.

I finally had an idea in June 2017 on billionaires and with the help of my friends, I published A Data Driven Guide to Becoming a Consistent Billionaire in October 2017 (yes, it took me four months to put it together).

Within three days of publishing, it had 30,000 views. It was everywhere. A sizable number of sites plagiarized the post and I didn’t care. My work was good enough to be plagiarized!

My Little Victories So Far

Apart from the 40,000 views I’ve gotten so far on my post A Data Driven Guide to Becoming a Consistent Billionaire, 2017 was an interesting year for me. For the first time, the work I have put in for the past three years was being validated.

  1. I won a United Nations Data Visualization Contest with my Tableau visualization on “Visualizing Malaria: The Killer Disease Killing Africa” which looked something like this.

2. I got invited to speak at Stanford’s Women in Data Science Conference holding in Nigeria on the exact same topic as this post.

3. I have numerous collaborations lined up for 2018 both in Nigeria and abroad.

4. I facilitated a workshop at The Women in Machine Learning and Data Science in November 2017.

Truthfully, I’m a bit surprised that I got this far. I remember writing in my notepad “Rosebud, you will never be good enough for this” but here I am. I still have a lot of learning to do but I am also grateful for where I am today.

My Advice for You

I’m no expert neither am I John Maxwell who gives nuggets of self-help advice but here are a few things that have really helped me.

  1. Don’t be afraid to let go of something that’s not working out. It took me till 2016 to fully let go of my Oil & Gas dreams even though I knew I was not passionate about it.
  2. Don’t be afraid to be called crazy. I cannot count the number of times people subtly and not-so-subtly told me I was crazy for leaving Chemical Engineering especially when Data Science was relatively new in Nigeria. It used to get to me but now I smile and say to myself “When I blow, you’ll understand”.
  3. Read. Read. Read.The books that opened up this field to me had nothing to do with Data Science. Reading expands your realm of possibilities.
  4. Love to learn. Have learning goals every year and stick to a medium (books/audio/video/classroom) that works best for you.
  5. Always, always put your best foot forward. Let the work that you put out there be the very best work it could be. It would speak for you. 99% of the opportunities I have gotten today came, in part, because of my blog.
  6. Most importantly, you are not an island. Have a tight-knit support system that would tell you the truth even when it hurts. You’d be better for it.

Good luck 🙂

I want to especially thank my amazing support system and all the people that got me here. They are too numerous to mention but I love you guys so much. I want to especially thank Tobi, Didun and Miracle for the support, the tough love, the brutal feedback and telling me where exactly to put an apostrophe. You have been there from day 1. You know all my struggles. You saw me at the very beginning and still believed I could do it. Thank you for making a better Data Scientist and a better person. I wouldn’t trade you for the world.

16 thoughts on “My Journey Into Data Science

  1. shivangi says:

    Inspiring story sir. I have also started planning to start the career in data science as well.
    your article has motivated me to start it as soon as possible and boosts my confidence to pursue the same.
    “Data science is a blend of various tools, algorithms, and machine learning with the goal to discover hidden pattern from the raw data and primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics, and machine learning. It deals with structured and unstructured data focusing mainly on the present and future scenario by using tools such as R, Rapidminer, BigML, Weka.
    You may be confusing it with the business intelligence (BI), but the main difference lies in the type of data as it only deals with the structured data and focuses on past and present time period. Also, there is a difference in data scientists and data analysts. Data analytics analysis the past recorded data and then tell the ongoing trends whereas the data scientists tell the ongoing trends as well as predict what could be the future trends and tell all the known and unknown events behind the trends.
    Data science is an ongoing process of the vicious cycle which includes the following stages or stages:
    1-discovery: understand and identify various sources, specifications, and other details and run a basic hypothesis test
    2-Data preparation: explore the data, and preprocess using R and doing the ETLT
    3-model planning: determining and identifying the relationship between the explanatory and unexplained variables.
    4-Model building: testing the data and seeing whether it is good to go on or it needs some changes or modification
    5-Operationalize: giving final results or reports giving a clear picture of the trends
    6-communicate results: analyzing whether the desired and achieved results are same or not.
    Now as a career option, if you look into the rising data studies you will find that how data science is and will be the ON DEMAND job and going on the statistics the average salary for the data scientist is approximately Rs 6lakhs and according to the O’Reilly 2018, data science survey average $2000 &$2500.
    I have come across a very committed and reliable training data science institution
    India’s first edtech analytical company providing with 100% placement guaranty


  2. Harshali patel says:

    Thank you for sharing your experience and your journey. It truly is the motivation at least for beginners like me who also want to take a few steps but is hesitant or not confident enough about its implementation.


  3. sameer says:

    Data science is one of the peak demand in IT Field Today. I was thinking to switch my career to Data Science from MBA MARKETING. I was searching in Google the data science field should join data science then your article shows up. I read your article like you mentioned in your article that you are the Chemical Engineering field then you switch to the Data Science field. your story gives me confidence. thanks for sharing your story which inspires me a lot.I’m going to join data science course
    plz suggest me which programing language should I learn such as Python or R programing


  4. Ehmie O. says:

    Hi Rosebud,

    Thank you so much for this post and well done!

    A friend shared the link with me because he and I had been having conversations about different career paths and Data Science in particular.

    I did some research on the field and it seems so vast and interesting. I’ve been so scared to dive in but reading this was encouraging and has made me less scared especially because I noticed some similarities between your journey and mine.

    I’ll be using your tips to craft my own learning path and I hope that in a year or two I’ll be well on way to calling myself a data scientist.

    Keep soaring!


  5. Onyema says:

    I’m sorta late to this post, but you sharing your thoughts and experience in a really sincere way helps put my challenges into perspective.
    Thank you.


  6. sai says:

    You write this post about data science very carefully I think, which is easily understandable to me. As a newbie, this info is really helpful for me. Thanks to you.


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