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
- Python, Jupyter, Variables, Printing, Documentation
- Integers, Floats, Booleans, Strings
- Conditionals, for Loops
- Functions, I/O
- Lists, List Operations, Tuples
- Dictionaries, Sets, List Comprehensions
- Recursion
- Generators, Exception Handling 9.Classes and Objects I
- Classes and Objects II
- pandas, matplotlib/seaborn/bokeh
- scikit-learn for machine learning
Course link: https://canvas.harvard.edu/courses/35159/assignments/syllabus
Source: https://www.openintro.org/stat/textbook.php?stat_book=os
- Introduction to Data (https://en.wikipedia.org/wiki/Data)
- Categorical & Numerical Data (https://towardsdatascience.com/data-types-in-statistics-347e152e8bee)
- Probability Tables (https://en.wikipedia.org/wiki/Standard_normal_table)
- Relative Risk (https://en.wikipedia.org/wiki/Relative_risk)
- Correlation Analysis (https://en.wikipedia.org/wiki/Correlation_and_dependence)
- Simple Linear Regression (https://en.wikipedia.org/wiki/Simple_linear_regression)
- Basics of Sampling (https://en.wikipedia.org/wiki/Sampling_(statistics))
- Sampling Distribution (https://en.wikipedia.org/wiki/Sampling_distribution)
- Tests for Means, Proportions & Contingency Tables (https://en.wikipedia.org/wiki/Test_statistic)
- Inferences for Correlation and Simple Linear Regression (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient)
Course link: https://projects.iq.harvard.edu/stat110
Source: https://projects.iq.harvard.edu/stat110/youtube
- Probability and Counting
- Story Proofs, Axioms of Probability
- Birthday Problem, Properties of Probability
- Conditional Probability
- Conditioning Continued, Law of Total Probability
- Monty Hall, Simpson's Paradox
- Gambler's Ruin and Random Variables
- Random Variables and Their Distributions
- Expectation, Indicator Random Variables
- Expectation Continued
- The Poisson Distribution
- Discrete vs. Continuous, the Uniform Distribution
- Normal Distribution
- Location, Scale and LOTUS
- Exponential Distribution
- Moment Generating Functions
- MGFs Continued
- Joint, Conditional, and Marginal Distributions
- Multinomial and Cauchy
- Covariance and Correlation
- Transformations and Convolutions
- The Probabilistic Method
- Beta Distribution
- Gamma Distribution and Poisson Processes
- Order Statistics and Conditional Expectation
- Conditional Expectation Continued
- Conditional Expectation Given
- Inequalities
- Law of Large Numbers and Central Limit Theorem
- Chi-Square, Student-t, Multivariate Normal
- Markov Chains
- Markov Chains Continued
- Markov Chains Continued Further
- A Look Ahead
Course link: https://canvas.harvard.edu/courses/29726/assignments/syllabus
Source: https://github.com/greenore/ac209a-coursework & https://github.com/cs109/a-2017
- Introduction
- Stats & EDA
- Pandas & Scraping
- EDA Viz
- Intro to Regression
- Multiple Linear Regression
- Model Selection
- Regularization
- PCA
- Logistic Regression
- Logistic Regression 2
- kNN Classification
- Discriminant Analysis
- Decision Trees
- Random Forests
- Boosting
- Stacking
- Support Vector Machines 19.Support Vector Machines-2
- A/B Testing
Course link: http://cs109.github.io/2015/pages/videos.html
Source: https://github.com/greenore/ac209b-coursework & https://github.com/cs109/2018-cs109b
- Smoothers & GAMs
- Cluster Analysis
- Anomaly Detection
- Bayesian Statistics
- Deep Neural Network
- Neural Network Basics
- Deep Feed Forward
- Regularization
- Optimization
- CNNs
- RNNs
- Autoencoders
- Generative Models & GANs
Course link: http://www.cs171.org/2018/syllabus/
Source: https://github.com/greenore/cs171-coursework
- Introduction
- Design
- Perception
- Cognition
- Interaction
- Process
- Projects
- Exploration
Course link: https://canvas.harvard.edu/courses/32949/assignments/syllabus
Source: explore
- Basic Statistics and R/Python
- Relationships and Representations, Graph Databases
- Introduction to Spark 2.0
- Spark 2.2 DataFrame API
- Hadoop Distributed File System (HDFS)
- Analysis of Streaming Data with Spark
- Applications of Spark ML Library
- Text processing with Python NLTK
- Basic Neural Network and Tensor Flow
- Further Examples of Tensor Flow
- Analysis of Images, OCR Applications
- Analysis of Speech Signal
- Analysis of Streaming Data
- Time Series with Tensor Flow