Past Meetup

Data Science Beyond the Hype - Use Cases in FinTech

This Meetup is past

366 people went

Details

Topic 1: Use Cases in FinTech
Topic 2: Assessing political fake news on Twitter

Note: The event is FREE of cost to attend. You can invite your friends/colleagues as well.

Agenda:

6:00 PM - Kick off event - DataGiri Intro

6:15 PM - Session 1 - Data Science beyond Hype - Fintech Use cases

7:00 PM - Session 2 - : Assessing political fake news on Twitter

8:00 PM - Networking session

9:00 PM - Event close

Event Information:

Session 1 :- Data is all the rage and data science itself a much-hyped skill. While a lot of seminal work has happened of late in creating significant value by rigorous application of data science techniques in many areas of human life, popular media has tended to extrapolate from early stage prototypes or niche applications to exaggerated visions of what the future holds for entire industries. This talk provides a practical view of data science for early-stage engineers looking to make a foray into this exciting specialization. The talk argues that data science is by no means the most important skill but is indeed one of few core skill sets that are needed to make a difference. The talk explores the question of what does it take for data to bring value for a business and the role that early (and even experienced) engineers play in shaping such applications, in the form of anecdotes accumulated through the last 12 years of doing massive-scale data science across the world. Who Should Attend? - Aspirants who want to learn Data Science- Business Leaders looking for solving business problems with Data Science.

Session 2 :- Political fake news have become a major challenge of our time, and its successful flagging a main source of concern for publishers, governments and social media. The approach we present in this work focuses on Twitter and aims at finding characteristic features (including temporal diffusion and NLP) that can help in the process of automating the identification of tweets containing fake news. In particular, we look into a dataset of four months-worth of tweets related with the 2016 US presidential election. Our results suggest that there are indeed some features (such as favourite and retweet counts, the distributions of followers, or the number of URLs on tweets) that can lead to successful identification of tweets containing fake news.

Profile bio:

Speaker 1 - Rangarajan Vasudevan

Ranga is deeply interested in helping companies grow their businesses using big data and data science.
He is Big data veteran including business strategy, solutions, technologies, architecture, and data science.
Strong academic background in core computer science topics of databases, distributed systems, networking, software development, data mining and algorithms.

Speaker 2 - Axel Oehmichen
Axel Oehmichen received both a MEng in Computer Science and Applied Mathematics from ENSEEIHT (France) and a MSc in Advanced Computing from Imperial College London in 2012. After working for a year in the City for Societe Generale CIB, he came back to Imperial College where he joined the Data Science Institute as a researcher and PhD student. He is currently leading the development of the OPAL project and finishing his PhD.

Who Should Attend:

- Aspirants who want to learn Data Science
- Business Leaders looking for solving business problems with Data Science

The event is FREE of cost to attend
RSVP now to reserve your spot at the event!!

We are a community of data scientists, data geeks and data hackers that meets regularly to discuss data science methods, topics, tools & technologies.

We discuss topics like: data science, data mining, machine learning, machine intelligence, deep learning, predictive analytics, artificial intelligence, big data analytics, NLP and statistical analysis and more....

This is a meetup for hands-on people directly involved in data science projects.

Please note: Absolutely no recruiters, HR people, Sales & Marketing people, Events & Conference people, brokers or agents. No exceptions.