The students of ITBM and DSDA had attended a guest lecture on 24th July 2019, the speaker was Mr. Rohit Walimbe, a data scientist working with Cummins.

The session served the purposes of providing a thorough overview of the data analytics space to the students and gave them the necessary knowledge to get started on their data analytics journey.

During the course of the lecture, Mr. Rohit made sure to highlight a program called Apache Spark; its benefits and usability were covered in depth. He explained in detail the basic architecture of Spark, gave us a comprehensive technical rundown of the software, spoke at length about its interoperability and made sure to bring to our notice the vast libraries available for Spark which can help us in generating and running a wide variety of models.

A significant section of the session was dedicated to machine learning models and how they can be applied in the real world to solve commonplace problems, in addition to this he shed some much-needed light on how to deploy these said models after they were created in order to generate maximum usability.

During the course of his address, Mr. Rohit showcased a few case studies that have immense practical applications in the modern world, these cases were concerned with the following topics:

  • Loan Risk Analysis
  • Advertising Analytics: Click Prediction
  • Market Basket Analysis, and
  • Identification of Suspicious Behaviour of Individuals from Videos.

We were also directed to an online repository of data analytics knowledge called Data Bricks. We were told that this website can function as an effective springboard for people who want to begin their foray into the data analysis spectrum as the material was tailored for everyone from beginners to seasoned veterans in this area.

He dedicated a sizeable portion of the session to answer our questions about the future of data analytics and its present scope of applications.

It was indeed a lovely session to attend and I can say with absolute confidence that my opinion about data analysis has changed for the better post listening to the lecture.

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