Topic 1

Topic	Weight %
1. Introduction to Data Science	5 - 10
2. Linear & Logistic Regression, Support Vector Machines, Regularization, and Time Series	10 - 15
3. Decision Trees, Supervised Segmentation, and Ensemble Methods	8 - 12
4. Classification, Clustering, and Naïve Bayes	8 - 12
5. Neural Networks and Reinforcement Learning	8 - 12
6. Performance Evaluation, Back-Testing, and False Discoveries	5 - 10
7. Textual Analysis and Large Language Models	10 - 15
8. Ethical & Privacy, and Regulation	8 - 12
9. Fintech Applications	15 - 25

Topic 1

Topic 1 Introduction to Data Science & Big Data (Weight 5-10%)
• Data analytic thinking	• Delivering your data
• Adoption of alternative data	• The future of alternative data
• Alternative data vendor profiles	• Building a data product
• The road ahead for investment managers	• Pricing your data
• Marketing and selling your data	• Protecting yourself and your data
• Understanding why “Wall Street” wants data	• Getting started with alternative data and CII (collective intelligence investing)
• Business problems and data science solutions	• Balancing the risks and rewards of CII from different platform types
• Advanced technologies for alpha	• Warming up to CII
• The risks and rewards of alternative data for investment decisions

Topic 2

Topic 2 Machine Learning: Introduction to Algorithms (Weight 5-10%)
• Statistical learning	• Assessing Model Accuracy
• Perceptron neurons	• Sigmoid neurons
• The architecture of neural networks	• Learning with gradient descent
• Implementing a network to classify digits	• Why deep learning matters
• A simple network to classify handwritten digits
• Motivation for using neural nets to recognize handwritten digits
• Organization and resources of the book An Introduction to Statistical Learning: with applications in R

Topic 3

Topic 3 Machine Learning: Regression, Support Vector Machine, Time Series Models (Weight 5-10%)
• Models, induction and prediction	• Multiple linear regression
• Supervised segmentation	• Considerations in the regression model
• Visualizing segmentations	• Concepts of time series
• Classification via mathematical functions	• Statistical models
• Regression via mathematical functions	• Modeling volatility
• Simple linear regression

Topic 4

Topic 4 Machine Learning: Regularization, Regression Trees, Random Forest & Overfitting (Weight 5-10%)
• Tree-Based methods	• Subset selection
• Shrinkage methods	• Overfitting and its avoidance

Topic 5

Topic 5 Machine Learning: Classification & Clustering (Weight 5-10%)
• Calculating and interpreting similarity and distance
•Discussing technical details related to similarities and neighbors
• Describing and evaluating classifiers
• Describing a key analytical framework and calculating expected values

Topic 6

Topic 6 Machine Learning - Performance Evaluation, Support Vector Machines & False Discoveries (Weight 5-10%)
• Visualizing model performance
•Backtesting protocol in the era of machine learning
•An investigation of the false discovery rate and the misinterpretation of p-values
• A data science solution to the multiple-testing crisis

Topic 7

Topic 7 Data Mining & Machine Learning: Naïve Bayes & Text Mining (Weight 5-10%)
• Evidence and probabilities	• Text representation
• Math behind Naïve Bayes classifiers	• Training the Naïve Bayes classifiers
• Optimizing for sentiment analysis	• Evaluation of sentiment analysis results
• Additional text representation approaches beyond “bag of words.”

Topic 8

Topic 8 Big Data & Machine Learning: Ethical & Privacy Issues (Weight 5-10%)
•Big data for business	•Ethical issues
• The ethics test	• The role of business decision-makers
•The five C’s	• Separating ethics and compliance
•Separating ethics and compliance	• The GDPR Embedding Wheel
•Doing good data science	•Oaths and checklists
• The nature of and business risks of artificial intelligence (AI)
• Taking responsibility for our creations (Data’s Day of Reckoning)
• General Data Protection Regulation (GDPR)
• Values that form the cornerstone of an ethical framework for artificial intelligence in business

Topic 9

Topic 9 Big Data & Machine Learning in the Financial Industry (Weight 30-50%)
•Machine learning and finance	•Adoption and Implementation Risks
• Artificial intelligence and its techniques	• Categories of machine learning algorithms
•Define the terms listed in the glossary	• Alternative data and institutional investors
•Applications of AI and Five Technologies Trends that Leap-Frog AI
• Artificial Intelligence in Investment Management
•Four Pillars for Transformation of Investment Management Firms
•Relationship between artificial intelligence, machine learning, and big data, and algorithms
• Drivers of the growth in the use of fintech and adoption of artificial intelligence
• Use cases of artificial intelligence and machine learning in the financial sector
•The micro-financial analysis of artificial intelligence and machine learning uses
•The macro-financial analysis of uses of artificial intelligence and machine learning uses
•Applications of random forest regression algorithm to factor models
•The applications of machine learning algorithms to stock selection
•Applications of machine learning algorithms to empirical asset pricing
•The most common errors made when machine learning techniques are applied to financial data sets
•Using statistical techniques to evaluate trading strategies in the presence of multiple tests
•Text mining and its applications in the insurance industry