Vinay Borhade

Being a B.E. Computers it was natural to get a job in an IT organization. Got to work on several technologies since last 14 years and understood the Insurance and Finance (Investment Banking) domains. To sum it all, the applications we created were more or less CRUD operations on data, a bit of data massaging and data presentation on Dasboards. Somewhere around 2014, while working for an IB, got introduced to concepts of Variations/Deviations, Monte Carlo simulations, Anova etc which are normal to Quantitative analysis for banks. These were statistical concepts overall to understand the variations amongst data. It helped me overall to draw conclusions on historical bank data or features and find correlations. However, what was missing was explaining the behavior in different scenarios or in presence or absence of some features (or data varibles to be simple). Given some statistical background  i could dive into the easiest of Machine Learning techniques like Regressions (Linear, Logistics). All the efforts were to perform the  exploratory data analysis. Around in a year since, I decided to get my hands dirty into predictive analysis, in essence to analyze the trend and forecast an event or numbers given historical trend. Exposing myself to decision trees, SVM , clustering algoritgms like KNN, apriori and FP for rules mining, I got to understand the world of ML. Initially, I could create models but improvng the accuracy and tuning them furthermore was a challenge. That's when I started taking the concepts seriously and tried to grab some material to grasp mathematics behind those models, which took around 6-8 months but was worth it. Now, having explored the world of as AI and ML, with sound industry experience, I have been training data science aspirants through reputed learning centres and few private tutoring for professionals. Recently, I have also began work on predictive analysis on my stock portfolio, in attempt to run a start-up.

The journey from IT peprogramm to a data scientist wasn't as easy as picking a book and reading about, but required good amount of efforts to practice on variety of data.

For a non IT professional, additional challenge would be to grasp a programming language, like R or Python, which is must to try you hands on the concepts.

Best of luck!!