1) Introduction to Data Science

     AI: Artificial Intelligence

     Data Scientist

     Big Data

     Languages

     Trends

     Tools

     Lab: Introduction to iPython​

 2) Models

     Machine Learning

     Modeling principles

     Under-fitting, over-fitting

     Lab: Model Complexity

 3) Data Science Process

     Data Types

     Data Manipulations (Cleaning, ETL, Reduction, Transformation, etc.)

     Encodings

     Lab: Data Analysis

 4) Visualizations

     Data Presentation

     Dishonest Charts

     Lab: Visualizations

 5) Probability & Statistics

     Famous Probability Problems

     Statistical Formulas

     Markov Models

     Hidden Markov Models

     Monte Carlo Methods

     Lab: Probability & Statistics

6) Statistical Distributions / Models

     Statistical Models

     Distributions (Bernoulli, Binomial, Poisson, etc.)

     Normal Distribution

     Beta Distribution

     Student’s t-Distribution

     Sampling Methods

     Bias-Variance Trade-off

     Statistical Significance

     Confidence Interval

     z-test

     Student’s t-test

     (Statistical) Power

     Lab: Statistical Distributions

 7) Linear Models

     Linear Regression

     Lasso Regression

     L1 & L2 Regularization

     Logistic Regression

     Lab: Linear Models

 8) Dimensionality Reduction

     Curse of Dimensionality

     PCA: Principal Component Analysis

     SVD: Singular Value Decomposition

     t-SNE: t-Distributed Stochastic Neighbor Embedding

     Lab: Dimensionality Reduction

 9) Supervised Learning

     Perceptron

     kNN: k-Nearest Neighbors

     SVM: Support Vector Machines

     Multi-Class Classification

     Decision Tree (Entropy, Impurity, Information Gain, etc)

     Mutual Information

     MIC: Maximal Information Coefficient

     Importance, Relevance and Error Measures

         - Confusion Matrix, ROC Curve, AUC, Precision & Recall, F-score, tf-idf, etc.

     A/B testing

     Lab: Supervised Learning

10) Time Series Analysis

     Lab: Time Series Analysis

11) Bayesian Methods

     Bayes' Theorem

     Bayesian Reasoning

     Naive Bayes Classifier

     Multi-Armed Bandit

     Lab: Bayesian Methods

12) Unsupervised Learning

      k-Means Clustering

      Expectation Maximization

      Lab: k-Means Clustering

13) Ensemble Methods

      Bootstrapping

      Bagging

      Boosting

      Random Forest

      Unbalanced Classes

      Lab: Ensemble Methods

14) Interesting Laws, Paradoxes and Models

      Benford’s Law

      Power Law

      Class Size Paradox

      Small World Models

      Community Detection Algorithms

      Lab: Interesting Laws, Paradoxes and Models

15) Deep Learning

      Loss/Error Functions

      SGD: Stochastic Gradient Descent

      FFN: Feed Forward Neural Networks

      CNN: Convolutional Neural Networks

      RNN: Recurrent Neural Networks

      LSTM

      Auto-Encoders

      GAN: Generative Adversarial Net(work)s

      Data Augmentation

      Dropout-Regularization

      Transfer Learning

      Tensorflow

      Keras

      Lab: Deep Learning

16) Advanced Topics

      Recommender Systems

      Market Basket Analysis

      Natural Language Processing (NLP)

      Reinforcement Learning (RL)

      Algorithm Complexity

      Big Data

      Map-Reduce

      Cloud (IBM Bluemix, Microsoft Azure, AWS, Google Cloud, etc.)

      Lab: Hadoop, Spark