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Event Details


Objectives & Takeaways: After attending this workshop, audience will be able to understand the core foundations of Data Science. They will also understand the mathematical principles applied vastly in this area. Attendees would also be introduced to popular tools and programming languages (scripting languages) used in Data Science.

Throughout the course of the workshop, the core concepts will be illustrated by examples. The main themes for the hands-on session will be 1) Core Concepts (Bayesian, Regression, Neural Nets) 2) Analyzing Large Datasets for Pattern Recognition 3) Sentiment Analysis.

Target audience: Professionals and students (research). Difficulty: Beginner to Medium.

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Time slot 


9:00 to 9:30


9:30 to 10:45

 Intro to Data Science

10:45 to 11:00

Coffee Break 

11:00 to 1:00 

Concepts in Data Mining / Machine Learning 

1:00 to 1:45

Lunch Break 

1:45 to 3:00

Guest Lecture on Data Analytics

3:00 to 4:00

Text Mining

4:00 to 4:30

Popular Data Sets / Tools / Programming Languages in DS 

4:30 to 5:00

Open Discussion

Speakers :

Pre-requisites: Familiarity with Math and basics of Computer Science. Participants should bring their own Laptop with below software’s installed.

1.       RapidMiner Studio:

2.       (Windows Or Ubuntu:

3.       Python (PyCharm Community Edition):





Getting Started with Data Science

 “A data scientist is someone who knows more statistics than a computer scientist and more computer science than a statistician.”

 A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.

 Why is Data Science important?

Every organization will need someone wearing the data scientist hat just like very organization has people responsible for product, sales, marketing and support. Unfortunately, to date, the tools available to data scientists have been rudimentary. Data scientists have had to learn diverse and complex computer languages for working with data. That world is changing as we create simpler ways for data scientists to use big data.


Surely we need data scientists in machine learning, right? Well, if you have very customized needs, perhaps. But most of the standard challenges that require big data, like recommendation engines and personalization systems, can be abstracted out. For example, a large part of the job of a data scientist is crafting “features,” which are meaningful combinations of input data that make machine learning effective. As much as we’d like to think that all data scientists have to do is plug data into the machine and hit “go,” the reality is people need to help the machine by giving it useful ways of looking at the world.


It’s never easy to automatically surface the most valuable insights from data. There are ways to provide domain-specific lenses, however, that allow business experts to experiment – much like a data scientist. This seems to be the easiest problem to solve, as there are a variety of domain-specific analytics products already on the market.

Workshop/tutorial details:

 Through this session we aim to get the attendees familiar with some of the most frequently used paradigms in Data Science.  We would also be emphasizing on ways in which, one of the hottest areas of Computer Science could be used in many ways. Keeping up with the spirit of Data Science, we would be looking into various techniques that would help us transform the data into knowledge and knowledge into intelligence. This workflow is realized by model building from the available data. And as data is growing in leaps and bounds, it has become essential to come up with clever techniques for storage and retrieval. Towards the end of the session we will explore one of the application areas of data science, Text Mining. We will also get to know about the tools and scripting languages that are often used in Data Science.


Research Areas:

·        Cloud computing

·        Databases and information integration

·        Signal Processing

·        Learning, natural language processing and information extraction

·        Computer vision

·        Information retrieval and web information access

·        Knowledge discovery in social and information networks




Vishwas -

Siva Yellampalli


MS Ramaiah Institute of Technology
MSR Nagar
Bangalore, Karnataka