My Journey From Commerce To Data Science

My Journey From Commerce To Data ScienceMuhsina EBlockedUnblockFollowFollowingFeb 25Image from PexelsEven though I was sure that I won’t enjoy a job as an accountant or a commerce lecturer, I completed my Master’s degree in Commerce in 2016.

After completing my studies, I joined for a one-year fellowship (TIFP) in KSUM, a government agency of our state.

I had no idea what career I have to choose or in which direction I have to go.

I remembered that I had learned some coding at the time of my Bachelor’s degree and I had an interest in learning to code.

Then I decided to give it a try.

I started learning from Codecademy.

I tried HTML, CSS and Javascript intending to look for a job as a front-end web developer.

But it didn’t work out.

I couldn’t find any eagerness in learning web development.

At that time I came to know about Data Science and it was striking.

Especially I loved the blend of coding and statistics.

That was the initial driving key to data science.

In this post, I will be sharing the resources used and methods I followed in the last 9 months to get started in data science in the exact same order which I learned.


Udacity — Bertelsmann Data Science Challenge Course.

At the right time, I came to know about the Bertelsmann Data Science Challenge Scholarship.

I got selected to the program and from where I started my journey towards Data Science.

The first phase was a challenge course.

I had to complete the course by the 3 months deadline.

Top 10% of students from the first phase were offered access to any of three nano-degree courses ( Data Foundations, Business Analyst, Data Analyst) of Udacity.

In this course, I learned basic concept used to describe data, explore data, use statistical research methods, compute simple probabilities, visualize data, basics of python and SQL.

I learned inferential statistics which was an optional part of the challenge course.

Introduction to descriptive statistics and Intro to inferential statistics are free courses in Udacity.

We were given additional materials for SQL and Python.

The course started in May 2018, I joined 1 month late in June.

I completed the course within 2 months but I couldn’t get into the second phase.

But completing the challenge course brought a lot of changes in my learning style.

One of them was I started learning at least 8–10 hours regularly.

The vibe I got from learning the Bertelsmann Data Science Challenge course was enough to move towards my dream.

What I loved most about this course was the immense support from the experts at Udacity and community managers and peers.


Python 3 from UdemyWhile I searching the alternate ways to reach the destination, I completed the “Python 3 course: From beginner to Advanced” from Udemy.

It was interesting at the beginning and later it gets started confusing.

The Python basics at the beginning of the course were helpful.


DataCampPersonally, I love Datacamp.

No matter if you do not have any prior coding experience.

Datacamp is one of the best online platforms for those planning to learn data science.

First I enrolled in Data Analyst with Python track.

At that time I had no confidence that I will be able to understand Machine Learning concepts and algorithms.

I thought understanding those will be a herculean task for me as I am from a non-computer science background.

I decided to learn data analytics initially and I thought after getting a job as a data analyst I can start learning Machine learning.

There are 7 career tracks in DataCamp.

Python ProgrammerData Analyst with PythonData Scientist with PythonR ProgrammerData Analyst with RData Scientist with RQuantitative AnalystI completed Data analyst with Python and then R.

After completing each track I did some projects from DataCamp project library.

What I loved about DatacampBest platform to start if you have no prior experience.

Well structured/ designed content in each career trackDatacamp provides enough to go from zero heroes.

You can start with the very basic concepts and learn complex machine learning algorithms and start solving real-world problems.

Datacamp has a good collection of projects.

After learning, If you are confused about how to solve a real-world problem from beginning to end, the projects section will give you hands-on experience.

After each video content, you can practice what you have learned in the DataCamp coding environment.

You ’ll get step by step instructions on what to do with the given dataset.

If you get stuck somewhere while practicing, you can check for the ‘hints’ option to complete the task or you can also check the answer.


KaggleAcquiring basic analytical and programming knowledge, and course certification are not enough.

Doing real-world projects helps to boost our knowledge and start a career in data science.

I chose Kaggle to find the datasets to do a project my own.

Of course, we can do projects from DataCamp.

They’ll give you instructions on how to.

But no one is going to guide you always either what to do with the data or how to transform the data, etc.

That’s why I decided to do projects apart from DataCamp.

I did an exploratory data analysis on 2016 Free code Camp New Coder Survey data.

Then I participated in the 2018 Kaggle ML & DS Survey Challenge.

When I read about the NFL Punt Analytics Competition, I decided to work on it.

I didn’t submit my kernel in this competition, but I spend some time playing with the data, learning NFL, and trying to find a solution.

It was a great experience in my journey towards data science.


Coursera- IBM Data Science CertificationI was thinking of a more advanced course in data science.

I found 2 courses from Coursera, Data Science Specialization by IBM and Johns Hopkins University.

I decided to start with the course by IBM.

This program consists of 9 courses including a capstone project at the end.

We can avail 7-day free access to the program.

As I didn’t have enough savings to pay for, I thought it would be better if I can complete the program before ending the free trial.

 When I went through the course details, I realized that I can’t finish the course with 7 days without knowing Machine learning.

What I did to solve this problem was, I completed data science tracks using R and Python from DataCamp and then I enrolled in IBM Data Science Certification program.

I finished the course within the free trial period.

Learning from multiple platforms help me clear the concepts better.

I’m not finished yet.

I have a long “to do” list in my pocket.

More Projects from KaggleData science Specialization — Johns Hopkins UniversityMachine Learning by Andrew NgThe Analytics edge of MIT, and many more.

This is how I started my Data Science Journey.

I have learned a lot and a long way to go….. More details

Leave a Reply