This document provides an introduction and overview of data science and analytics. It discusses Sri Krishnamurthy's background and experience in quantitative analytics, data science, and machine learning. It also covers topics like what analytics is, examples of analytics applications, obstacles to adopting analytics, and how companies can become more analytics-driven. Machine learning and AI are discussed as a paradigm shift in finance. The virtuous circle of machine learning, algorithms, hardware, and data is depicted.

1. Introduction to Data Science & Analytics
2018 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
www.QuantUniversity.com
quantuniversity@gmail.com

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Quantitative Analytics and Big Data Analytics Onboarding
• Data Science, Quant Finance and
Machine Learning Advisory
• Trained more than 1000 students in
Quantitative methods, Data Science
and Big Data Technologies using
MATLAB, Python and R
• Building

3. • Founder of QuantUniversity LLC. and
www.analyticscertificate.com
• Advisory and Consultancy for Financial Analytics
• Prior Experience at MathWorks, Citigroup and
Endeca and 25+ financial services and energy
customers.
• Regular Columnist for the Wilmott Magazine
• Author of forthcoming book
“Financial Modeling: A case study approach”
published by Wiley
• Charted Financial Analyst and Certified Analytics
Professional
• Teaches Analytics in the Babson College MBA
program and at Northeastern University, Boston
Sri Krishnamurthy
Founder and CEO
3

4. Motivation
What is Analytics?
Analytics is the discovery and communication of
meaningful patterns in data*
Analytics often favors data visualization to
communicate insight.
*Wikipedia

12. 66 million monthly unique visitors, is used on more than 5.7 million
mobile devices, and has collected a total of 25 million reviews since its
inception in 2004
Local advertising from businesses that want to be featured on Yelp, which
is 76.8 percent of total ad revenue.
Brand advertising, i.e. display and text ads on Yelp.com, which gets lots of
traffic from search engines.

16. Visualizing energy use in NYC buildings

21. Findings from IBM/MIT Sloan study

22. Obstacles for adoption of analytics

26. How can a company become more analytics driven?

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• “AI is the theory and development of computer systems able to
perform tasks that traditionally have required human intelligence.
• AI is a broad field, of which ‘machine learning’ is a sub-category”
What is Machine Learning and AI?
Source: http://www.fsb.org/wp-content/uploads/P011117.pdf

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Machine Learning & AI in finance – A paradigm shift
Stochastic Models
Factor Models
Optimization
Risk Factors
P/Q Quants
Derivative pricing
Trading Strategies
Simulations
Distribution fitting
Quant
Real-time analytics
Predictive analytics
Machine Learning
RPA
NLP
Deep Learning
Computer Vision
Graph Analytics
Chatbots
Sentiment Analysis
Alternative Data
Data Scientist

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The Virtuous Circle of Machine Learning and AI
Smart
Algorithms
Hardware
Data

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The rise of Big Data and Data Science
Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

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Smarter Algorithms
Parallel and Distributing Computing Frameworks Deep Learning Frameworks
1. Our labeled datasets were thousands of times too
small.
2. Our computers were millions of times too slow.
3. We initialized the weights in a stupid way.
4. We used the wrong type of non-linearity.
- Geoff Hinton
“Capital One was able to determine fraudulent credit
card applications in 100 milliseconds”*
*http://go.databricks.com/hubfs/pdfs/Databricks-for-FinTech-170306.pdf

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Hardware

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What should you do ?
Q&A

36. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Contact
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distributedor usedinanyotherpublicationwithoutthepriorwrittenconsentofQuantUniversityLLC.
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