Data Analysis

Data Analysis

Machine Learning

Chapter 01 - Linear Regression
Chapter 02 - Gradient Descent
Chapter 03 - Logistic Regression
Chapter 04 - Neural Network
Chapter 05 - Back Propagation
Chapter 06 - Practical Advices
Chapter 07 - Support Vector Machine
Chapter 08 - K-means, PCA Details
Chapter 09 - Anomaly Detection, Recommender System
Chapter 10 - Stochastic Gradient, Synthetic Data, Ceiling Analysis

Analytics Edge

Week 1 (external) - Intro
Week 2 (external) - Linear Regression
Week 3 (external) - Logistic Regression
Week 4 (external) - CART, Random Forest
Week 5 (external) - Text Analytics
Week 6 (external) - Clustering, Recommendation
Week 7 (external) - Visualization: Heat Map, Social Network, Wordcloud
Week 8 (external) - Linear Optimization
Week 9 (external) - Integer Optimization
Kaggle Competition (external) - Predicting which NY Times Blog Articles Will Be Most Popular
Final Exam (external) - Final Exam: Regression, Clustering, Text Mining

Intro to Computational Thinking and Data Science

Chapter 1 - Modeling
Chapter 2 - Monte Carlo Simulation
Chapter 3 - Optimization Problem
Chapter 4 - State Modeling, Hierarchical Clustering

Process Mining

Week 1 - Process Mining Intro
Week 2 - Alpha Algorithm
Week 3 - Metric, C-nets
Week 4 - Conformance Checking, Dotted Chart
Week 5 - Decision, Social, Organization Mining

Pattern Discovery

Chapter 1 - Apriori, FP Growth
Chapter 2 - Null-invariant, Pattern-Fusion, Constaint
Chapter 3 - Sequential Pattern Mining

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