No, you don't need to learn python
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The goal of this workshop is not to disregard the amazing innovations brought about by Machine Learning and AI but to emphasize the rigor, discipline and the effort involved in successfully adopting data science, AI and machine learning in financial organizations.
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No, you don't need to learn python
- 1. No, You don’t need to learn Python 2018 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.quantuniversity.com 11/12/2018 QuantUniversity Meetup Fidelity Boston
- 2. 2 About us: • Data Science, Quant Finance and Model Governance Advisory • Technologies using MATLAB, Python and R • Programs ▫ Analytics Certificate Program ▫ Fintech programs • Platform
- 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. 4 • QuantUniversity Meetup – Nov 29th ▫ Advances in Time Series Analysis • 2-day Advances in Time series Workshop – December – Date TBD Partner events • QWAFAFEW – Nov 20th, Closed End Funds – An Investment Strategy for People Near Retirement Upcoming events
- 5. 5 • How many of you have written a working computer program? 1. Excel Macros 2. C 3. Java 4. MATLAB, R, Python, Julia • How many of you spend more at least 20% of your time programming at work? • How many of you hold the title Data Scientist? Quick poll
- 6. 6 The Data science craze!
- 8. 8 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
- 9. 9 The Virtuous Circle of Machine Learning and AI Smart Algorithms Hardware Data
- 10. 10 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
- 11. 11 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
- 12. 12 Hardware
- 13. 13 Typical Data science process Data cleansing Feature Engineering Training and Testing Model building Model selection Model Deployment
- 14. 14 A Typical Data science pipeline Data cleansing Feature Engineering Training and Testing Model building Model selection Model Deployment
- 15. 15 Intro to Python Programming in C Programming in Python
- 17. 17 PYTHON HAS EVOLVED SIGNIFICANTLY IN THE LAST THREE DECADES REF: https://speakerdeck.com/teoliphant/ml-in-python
- 18. 18 REF: https://speakerdeck.com/teoliphant/ml-in-python PYTHON HAS OUTPACED OTHER PROGRAMMING LANGUAGES
- 19. 19 PYTHON’S FOOTPRINT IS HUGE REF: https://speakerdeck.com/teoliphant/ml-in-python
- 20. 20 SO WHY SHOULDN’T I LEARN PYTHON?
- 21. 21 THE HYPE!
- 22. 22 Market impact at the speed of light!
- 23. 23
- 24. 24
- 25. 25 1. Everyone is talking about Python. You need to take that Python class! 2. It works great for Computer vision! We just need to find that killer use case for finance 3. We shall build everything from scratch! 4. Let’s start from somebody’s Jupyter notebook 5. Python and Open source are FREE! 6. Data Science is cool and fascinating! 7. It just works! I am not sure how though 8. Our model is the best! 9. We have build it; We just need to deploy this in production 10. Data Science ~= Python Ten fallacies of Data Science and AI
- 26. 26 • The Python Ecosystem is huge! • Learning to code with a 1-day class won’t make you a data scientist • You only have so much time. Focus on the right strategies. • If Data science is key to your business, have a comprehensive Data Science strategy 1. Everyone is talking about Python. You need to take that Python class!
- 27. 27 • We have many solutions looking for a problem • Just because you found an interesting example in another domain, you shouldn’t go hunting for a use case unless you have a lot of free time. • Beware of imbalanced class problems • A model that gives 99% accuracy may still not be good enough 2. It works great for Computer vision! We just need to find that killer use case for finance
- 28. 28 • For most problems, someone else has already seen that problem and has solved it! • No point starting from scratch. • You shouldn’t be coding from scratch in Python unless your problem is so unique 3. We shall build everything from scratch!
- 29. 29 • FREE Github repos are abundant • Many classes just teach you rudimentary stuff • Being able to run somebody’s Jupyter notebook doesn’t make you a Python expert • Somebody didn’t write this Jupyter notebook for your problem. 4. Let’s start from somebody’s Jupyter notebook
- 30. 30 • Nothing is Free in life! • You are paying with your time and taking the risk • If may be worth the investment but you need to question if it is worth “your” time! 5. Python and Open source are FREE!
- 31. 31 • Yes it is! No questions • But it is also a rigorous science requiring significant engineering and experimentation • But do go in half-heartedly unless it is a hobby • If you don’t envision you spending at least 40% of your day coding, you may be better off relying on someone else’s expertise 6. Data Science is cool and fascinating!
- 32. 32 • Lots of heuristics; still not a proven science • Interpretability or Auditability of models are important • Beware of black boxes; Transparency in codebase is paramount with the proliferation of opensource tools • Skilled data scientists who are knowledgeable about algorithms and their appropriate usage are key to successful adoption 7. It works. We don’t know how!
- 34. 34 • If you are just starting, it will take quite some time till you build expertise. • The AutoML field is growing significantly • You are better off learning how to evaluate solutions rather than building solutions 8. Our model is the best!
- 35. 35 Claim: • Our models work on all the datasets we have tested on Caution: • Do we have enough data? • How do we handle bias in datasets? • Beware of overfitting • Historical Analysis is not Prediction 9. A prototype model is not your production model
- 36. 36 Data Engineering vs Data Science Engineering/IT • Scaling • Structuring • Design of Experiments • Data Parallel/Task Parallel Quants/Data Scientists • New Algorithms • Try new methods • Effect of Parameters and Hyper Parameters
- 38. 38 10. Python ~= Data Science • Learning Python syntax doesn’t make you a Data science expert Focus on: • Your area of expertise • Proven Data Science best practices • Appropriate infrastructure • A comprehensive Data Science Strategy
- 40. 40 www.QuSandbox.com Model Analytics Studio QuResearchHub QuSandbox Prototype, Iterate and tune Standardize workflows Productionize and share
- 41. Sri Krishnamurthy, CFA, CAP Founder and Chief Data Scientist sri@quantuniversity.com srikrishnamurthy www.QuantUniversity.com www.analyticscertificate.com www.qusandbox.com Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be distributed or used in any other publication without the prior written consent of QuantUniversity LLC. 41