Data Science in Action Bootcamp 

On 14Th December, TBC held a Bootcamp on “Data Science in action”, a fun learning experience which was conducted by Mr Suresh Gautam, CEO and Co-Founder of Entenso Data (F1 Soft Group).

The Bootcamp session was specially tailored for Level 5 and Level 6 students but started off with the very basics of Data Science so that students attending the sessions without any prior knowledge of the topic were able to grasp the concept of Data Science.

Gautam then explained how machine learning algorithms are essential tools in the study of data and as such, are used by both big as well as small companies, in order to retain markets, expand their businesses, predict the future of a company based on their past data, and take necessary tactics to expand the business. 

As it was a four-hour session, it was divided into two halves. During the first half, students were introduced to various different Machine Learning Algorithms such as K-Nearest Neighbour, Decision Tree, and Random Forest. They further learnt about their use cases and how important it is to select the appropriate algorithm for data analysis. The first half then ended with a practical demonstration of “Rental Bike (Insights + Predictions) which the students were invited to take part in, whilst using Kaggle as their platform.

During the second half, they were then introduced to a new Model known as Recency, Frequency, & Monetary (RFM) - which is used to assign a company’s customer base to a particular trait that can be used to improve marketing analysis. There were also practicals taking place alongside the guest lecture, which gave students the chance to analyse the outcomes of the data. 

Overall, the Bootcamp session was very effective and presented an exciting learning experience for everyone involved. And the Guest Lecturer’s passion for teaching, his excitement about the possibilities of “Data Science in Action”, and his willingness and ability to share everything he knows in layman’s terms made this session incredibly beneficial.