As a young data scientist

My experience as a young data scientist

After spending almost four and a half years in campus, I was now out as  freshman eager to face what the world could offer. Having little knowledge of what title I preferred for my career. I later realized I had passion in data and wanted to pursue data-related career, a story for another day!

A few months after clearing my final exams, I was offered an internship position as a data analyst with an analytics company. This seemed to be in line with what I desired to do, playing around with data to bring out insightful information with the purpose of influencing boardroom strategic decisions. This could picture me making predictions, forecasts, creating amazing visualizations and reports.

For sure, someone could cluster me as having euphoria of having been relinquished to the real world and not having a speedometer to help me plan my expectations. 

Within the first three months as an intern, I was engaged with more research, article writing and spent long hours of data wrangling. I spent a few hours doing data analysis and ‘deep stuff’. I could easily invoke words like ‘that seems hard’, ‘can’t do that’ and ‘the data is too dirty’.

In most cases I'd find myself stuck within a project even after being previewed on how to tackle that project. It could have been hard for me to take a project through the whole pipeline because of the different and dynamic processes I had to follow to achieve the end goal.

Over time however I've been exposed to different kinds of data and varying business needs that require to be solved with data. Dedicated to reach the top ladder as a senior data scientist I have been engaged in understanding fields ranging from Artificial Intelligent, Machine Learning, Deep Learning, Big Data Analytics and Visualization.

Having my hands dirty on Artificial Intelligent has expanded my knowledge in chatbot design and implementation which uses intensive natural language processing. I have been exposed to text processing with a fair look on sentimental analytics and text structuring for analytics purposes. I have had a great milestone on machine learning and its application on datasets. Over time, I have understood the importance of doing an exploratory data analysis before applying machine learning, how to tune your ML to answer your specific questions.

It is clear to me on the need for 'data audit for data science', a crucial step in a data science project. Over time, I have gained confidence in auditing data sources to answer three major questions which are;

  • Is data available in line with business objectives?
  • Does the data contain any anomalies and missing values?
  • What is the rich and reach of the data?

I have gained experience on the Extract Transform and Load (ETL) process with a fair knowledge on data warehousing design and structuring and an advanced use of server query language to produce structured data for visualization. I have applied, measured and weighed different analytic and visualization tools on the way they can be applied to effectively deliver on business immediate and long term needs.

I know I am not there but it is a journey that is certain.


Bob. M

Junior Data Scientist

Nakala Analytics Ltd