1. Data Science for Finance and Business
How Data Science Has Impacted The Financial Industry
Data Science for Finance and Business

2. Neal Kumar, CFA Co-Founder & CEO
Quick Background
Neal Kumar, CFA Co-Founder & CEO
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Data – “Growth Spurt”
2.5 quintillion bytes
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
Source: IBM
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4. Data Science Vs. Big Data
Data Science is the art of extracting useful knowledge from data.
Big Data refers to datasets that are too large for traditional systems to capture, store, manage, and analyze.
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Relevance
In fact, we are generating so much data that it’s physically impossible to store it all. Financial services sectors, including securities and investment services and banking, have the most digital data stored per firm on average.
Source: McKinsey Global Institute
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6. Relevance (cont’d)
Source: McKinsey Global Institute

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Relevance (cont’d)
Source: McKinsey Global Institute
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8. How can Data Science help us make better decisions?
Data Science allows us to make decisions based on historical data rather than on a hunch.
A traditional venture capitalist could select investments based on her experience and intuition.
Or, using DDD, she could base her selection based on an analysis of past successful companies.
Source: Data Science for Business, Provost & Fawcett
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9. Data Science Methods – High-Level Overview
Methods to learn a model generally fall into two groups:
In supervised learning, we want to learn relationships between input (features) and output variables (target).
If we don’t have a specific target, the learning problem is called unsupervised.
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10. Data Science Methods – High-Level Overview (cont’d)
Continuous Data
Categorical Data
Supervised
Regression
Classification
Unsupervised
Dimensionality Reduction
Clustering
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11. Applications in Finance, Fintech, and Economics
Use of credit decision technology to provide access to credit for people with little or no credit history.
“Banking the Underbanked”
Source: kreditech.com
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12. Applications in Finance, Fintech, and Economics (cont’d)
Analysis of massive amounts of data to create unparalleled investment strategies.
Open invite hack sessions where people around the world can submit trading algorithms and profit share if such algorithms are successful
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13. Applications in Finance, Fintech, and Economics (cont’d)
Robo-advisors are online wealth management services that provide automated, algorithm-based portfolio management advice.
Angel and VC investors are utilizing data science to help improve odds that they are investing in winning ideas.
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14. Applications in Finance, Fintech, and Economics (cont’d)
FP&A and PE Portfolio companies are using data science to boost top lines and improve efficiency.
Economic consulting firms and macroeconomic forecasting teams within companies and financial institutions use data science to help predict leading economic indicators.
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15. Capturing the Full Potential of Big Data
Source: McKinsey Global Institute
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Building The Skills
Data Science
Statistics
Machine Learning
Software Engineering
Domain Expertise
Data Engineering
Mathematics
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Thank you!
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