Video: https://youtu.be/ky3159dqQ_o?t=30 Advances in Data science, Machine Learning, AI, Optimization and prediction are revolutionizing the way financial professionals are taking decisions. From sifting through large amounts of data to designing strategies to optimizing execution, technology has played a major role in changing the investment game! The 21st Century Financial Professional needs to be cognizant of the tsunami of changes that are changing the industry. In this webinar, Sri Krishnamurthy, CFA, the President of QuantUniversity shares five key trends every financial professional needs to know about. Sri along with Dr.Gustavo Vicentini and Anish Shah, CFA will be leading a full day workshop on the theme on Feb 6th.

1. Data Science in 10 steps:
A framework for developing Data science applications
2019 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.analyticscertificate.com

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About us:
• Data Science, Quant Finance and
Machine Learning Startup
• 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
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Register here:
https://Qumasterclass.splashthat.com

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1. Articulate your business problem
2. The Data questions
1. Do you know what data you need
2. Do you know if the data is available?
3. Do you have the data ?
4. Do you have the right data?
5. Will you continue to have the data?
3. Develop a data acquisition and data prep strategy
1. Do you know how to get the data ?
2. Who gets the data?
3. How do you process it?
4. How do you access it?
5. How do you version and govern the data?
Data science in 10 steps

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4. Explore and Evaluate your data and get it in the right format
5. Define your goal:
1. Summarization
2. Fact finding
3. Understanding relationships
4. Prediction
6. Shortlist (not “Choose” ) the
techniques/methodologies/algorithms
Data science in 10 steps

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7. Evaluate/Establish business constraints and narrow down your
choices of techniques/methodologies/algorithms
1. Cloud/Cost/Expertise/Cost-Value
2. Build/buy/access
8. Establish criteria to know if the methodology/models/algorithms
work
1. Is the process replicable?
2. What performance metrics do we choose?
3. Can you evaluate the performance and validate if the models meet
the criteria?
4. Does it provide business value?
Data science in 10 steps

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9. Fine tune your algorithms and algorithm selection
1. Hyper parameter tuning
2. Bias-variance tradeoff
3. Handling imbalanced class problems
4. Ensemble techniques
5. AutoML
10. How will this process reach decision makers
1. Deployment choices (On-prem/Cloud)
2. Frequency of Data/Model updates
3. Governance/Role/Responsibilities
4. Speed, Scale, Availability, Disaster recovery, Rollback, Pull-Plug
Data science in 10 steps

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How do you monitor the efficacy of your solution?
1. Retuning
2. Monitoring
3. Model Decay
4. Data augmentation
5. Newer innovations
Data science in 10 steps - Bonus

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Use code 50off for 50% off all tickets
Exclusive code for webinar attendees

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Register here:
https://Qumasterclass.splashthat.com