Name: ADOPTING DATA SCIENCE AND ML: A BLUEPRINT FOR FINANCIAL PROFESSIONALS
Start: 2018-08-09T17:30:00-04:00
End: 2018-08-09T19:30:00-04:00
Location: Anchor room

IMPORTANT:
Due to space restrictions at the event location, this event is invitation only. Please request an invitation at https://datascienceinfinance.splashthat.com

WORKSHOP SUMMARY
Agenda:
5.30-6.00: Refreshments, Registration & Networking
6.00-6.45: Part 1: Adopting Data Science and Machine Learning in the Enterprise
6.45-7.30: Part 2: Sentiment Analysis with Edgar Datasets : A NLP case study

Summary:
Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing and scaling quantitative models. Even though there are multiple solutions and platforms available to build machine learning solutions, challenges remain in adopting machine learning in the enterprise.In this talk we will illustrate a step-by-step process to enable replicable AI/ML research within the enterprise using QuSandbox.

In Part 1, we will discuss the challenges and best practices of adopting data science and machine learning solutions in financial companies.

In Part 2, we will demonstrate a case study in Python to use Natural Language Processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google and Azure APIs. We will compare an contrast scores from various APIs and discuss how these scores can be used to augment traditional quantitative research and for trading decisions. At the end of this talk, participants can see a concrete picture on how to take their machine learning from a research and prototype to a scalable production model deployable in the cloud.

Speaker
Sri Krishnamurthy, CFA, CAP is the founder of QuantUniversity.com, a data and Quantitative Analysis Company and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than 15 years of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications. Prior to starting QuantUniversity, Sri has worked at Citigroup, Endeca, MathWorks and with more than 25 customers in the financial services and energy industries. He has trained more than 1000 students in quantitative methods, analytics and big data in the industry and at Babson College, Northeastern University and Hult International Business School. Sri is leading development efforts in creating a platform called QuSandbox for adopting open source and analytics solutions within regulated industries.

This workshop is hosted by QuantUniversity. We acknowledge CIC for their support. Visit www.analyticscertificate.com for our offerings.

If you missed our last meet up on Data science and Machine Learning for Finance, check the video here: https://www.youtube.com/watch?reload=9&v=B_GMNpuldiA

Sri Krishnamurthy

QuantUniversity Meetup

Technology

Big Data

Machine Learning

Data Analytics

Data Visualization

Predictive Analytics

Quantitative Analysis

Data Mining

Business Analytics

Data Science

Financial Technology

Financial Engineering

FinTech

ADOPTING DATA SCIENCE AND ML: A BLUEPRINT FOR FINANCIAL PROFESSIONALS

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ADOPTING DATA SCIENCE AND ML: A BLUEPRINT FOR FINANCIAL PROFESSIONALS

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