vsistla / ms-in-data-science

Syllabus & reference material

MS in Data Science (Self-Learning @ Harvard)

Python Programming (CSCI E-7)

Course link: https://canvas.harvard.edu/courses/33171/assignments/syllabus
Sources: http://interactivepython.org/runestone/static/thinkcspy/index.html & http://www.data-analysis-in-python.org/index.html

1. Python, Jupyter, Variables, Printing, Documentation
2. Integers, Floats, Booleans, Strings
3. Conditionals, for Loops
4. Functions, I/O
5. Lists, List Operations, Tuples
6. Dictionaries, Sets, List Comprehensions
7. Recursion
8. Generators, Exception Handling 9.Classes and Objects I
9. Classes and Objects II
10. pandas, matplotlib/seaborn/bokeh
11. scikit-learn for machine learning

Statistics (Stat 100)

Course link: https://canvas.harvard.edu/courses/35159/assignments/syllabus
Source: https://www.openintro.org/stat/textbook.php?stat_book=os

1. Introduction to Data (https://en.wikipedia.org/wiki/Data)
2. Categorical & Numerical Data (https://towardsdatascience.com/data-types-in-statistics-347e152e8bee)
3. Probability Tables (https://en.wikipedia.org/wiki/Standard_normal_table)
4. Relative Risk (https://en.wikipedia.org/wiki/Relative_risk)
5. Correlation Analysis (https://en.wikipedia.org/wiki/Correlation_and_dependence)
6. Simple Linear Regression (https://en.wikipedia.org/wiki/Simple_linear_regression)
7. Basics of Sampling (https://en.wikipedia.org/wiki/Sampling_(statistics))
8. Sampling Distribution (https://en.wikipedia.org/wiki/Sampling_distribution)
9. Tests for Means, Proportions & Contingency Tables (https://en.wikipedia.org/wiki/Test_statistic)
10. Inferences for Correlation and Simple Linear Regression (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient)

Probability (Stat 110)

Course link: https://projects.iq.harvard.edu/stat110
Source: https://projects.iq.harvard.edu/stat110/youtube

1. Probability and Counting
2. Story Proofs, Axioms of Probability
3. Birthday Problem, Properties of Probability
4. Conditional Probability
5. Conditioning Continued, Law of Total Probability
6. Monty Hall, Simpson's Paradox
7. Gambler's Ruin and Random Variables
8. Random Variables and Their Distributions
9. Expectation, Indicator Random Variables
10. Expectation Continued
11. The Poisson Distribution
12. Discrete vs. Continuous, the Uniform Distribution
13. Normal Distribution
14. Location, Scale and LOTUS
15. Exponential Distribution
16. Moment Generating Functions
17. MGFs Continued
18. Joint, Conditional, and Marginal Distributions
19. Multinomial and Cauchy
20. Covariance and Correlation
21. Transformations and Convolutions
22. The Probabilistic Method
23. Beta Distribution
24. Gamma Distribution and Poisson Processes
25. Order Statistics and Conditional Expectation
26. Conditional Expectation Continued
27. Conditional Expectation Given
28. Inequalities
29. Law of Large Numbers and Central Limit Theorem
30. Chi-Square, Student-t, Multivariate Normal
31. Markov Chains
32. Markov Chains Continued
33. Markov Chains Continued Further
34. A Look Ahead

Data Science I ( AC 209a)

Course link: https://canvas.harvard.edu/courses/29726/assignments/syllabus
Source: https://github.com/greenore/ac209a-coursework & https://github.com/cs109/a-2017

1. Introduction
2. Stats & EDA
3. Pandas & Scraping
4. EDA Viz
5. Intro to Regression
6. Multiple Linear Regression
7. Model Selection
8. Regularization
9. PCA
10. Logistic Regression
11. Logistic Regression 2
12. kNN Classification
13. Discriminant Analysis
14. Decision Trees
15. Random Forests
16. Boosting
17. Stacking
18. Support Vector Machines 19.Support Vector Machines-2
19. A/B Testing 

Data Science II (AC 209b)

Course link: http://cs109.github.io/2015/pages/videos.html
Source: https://github.com/greenore/ac209b-coursework & https://github.com/cs109/2018-cs109b

Part I

1. Smoothers & GAMs
2. Cluster Analysis
3. Anomaly Detection
4. Bayesian Statistics

Part II

1. Deep Neural Network
2. Neural Network Basics
3. Deep Feed Forward
4. Regularization
5. Optimization
6. CNNs
7. RNNs
8. Autoencoders
9. Generative Models & GANs

Data Visualization (CSCI E-171)

Course link: http://www.cs171.org/2018/syllabus/
Source: https://github.com/greenore/cs171-coursework

1. Introduction
2. Design
3. Perception
4. Cognition
5. Interaction
6. Process
7. Projects
8. Exploration

Big Data Analytics (CSCI E-63)

Course link: https://canvas.harvard.edu/courses/32949/assignments/syllabus
Source: explore

1. Basic Statistics and R/Python
2. Relationships and Representations, Graph Databases
3. Introduction to Spark 2.0
4. Spark 2.2 DataFrame API
5. Hadoop Distributed File System (HDFS)
6. Analysis of Streaming Data with Spark
7. Applications of Spark ML Library
8. Text processing with Python NLTK
9. Basic Neural Network and Tensor Flow
10. Further Examples of Tensor Flow
11. Analysis of Images, OCR Applications
12. Analysis of Speech Signal
13. Analysis of Streaming Data
14. Time Series with Tensor Flow