Book Title
Welcome
I Background and Preparation
1 Programming Bootcamp
2 Overview of Multivariate Calculus
3 Overview of Linear Algebra
4 A Survey of Optimization
5 Overview of Probability Theory
6 Introductory Statistical Analysis
7 A Linear Regression Cheatsheet
8 Analysis of Variance and Design of Experiments
9 Survey Sampling Methods
10 Overview of Simulation Methods
11 A Primer of Times Series and Forecasting
12 Non-Technical Aspects of Quantitative Work
II Introduction to Data Science
13 Data Science Basics
14 Data Preparation
15 Data Visualization
16 Machine Learning 101
17 Data Engineering and Data Management
18 Reporting and Deployment
III Intermediate Data Science
19 Web Scraping and Automated Data Collection
20 Text Mining and Text Analysis
21 Regression and Value Estimation
22 Spotlight on Classification
23 Spotlight on Clustering
24 Feature Selection and Dimension Reduction
IV Advanced Data Science
25 Anomaly Detection and Outlier Analysis
26 Bayesian Data Analysis
27 Network Data Analysis
28 Recommender Engines
29 An Introduction to Deep Learning
30 Natural Language Processing
V Special Topics in Data Science
31 Big Data and Parallel Computing
32 Reinforcement Learning
33 Computer Vision and Image Analysis
34 Data Science with Streams
35 Frequent Event Mining
36 Ranking and Ordering
VI Data Science Through Examples
37 Music Dataset
- 37.1 Spotify Music Datasets
38 Web Scraping and Automated Data Collection
39 Data Engineering and Pipelines
40 Data Understanding
41 Feature Selection and Dimension Reduction
42 Regression and Value Estimation
43 Machine Learning Tasks
44 Text Mining
45 Anomaly Detection and Outlier Analysis
46 Bayesian Data Analysis
47 Network Analysis
48 Recommender Engines
49 Ranking and Ordering
Appendix
A Place holder
B Cheatsheet for the authors
C A List of Projects
References
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Data Analysis, Data Science, Data Understanding

Module 41 Feature Selection and Dimension Reduction

Are there CoD issues? What could we do to reduce these issues? (depending on the tasks)