Introduction- The Data Science Road Map: Frame the Problem- Understand the Data–Model – Programming Languages: A Survey of Programming Languages for Data Science –Strings - Defining Functions – Python – Data Munging: Problems with Data Content - Problems with Data Content - Regular Expressions.

Visualizations and Simple Metrics: Pie Charts, Bar Charts, Histograms - Means, Standard Deviations, Medians, and Quantiles – Box plots , Scatter plots - Heat maps - Correlations - Time Series - Machine Learning Overview: Historical Context - Supervised versus Unsupervised - Training Data, Testing Data, and the Great Boogeyman of Overfitting. Machine Learning Classification : Classifiers - Evaluating Classifiers.

Unsupervised Learning: The Curse of Dimensionality - Component Analysis - Factor Analysis – Clustering – Regression - Least Squares – Correlation - Linear Regression- LASSO Regression and Feature Selection - Data Encodings and File Formats.

Big Data – Hadoop File System - Spark Overview - Spark Operations – PySpark – MapReduce – Databases: Relational Databases and MySQL – MongoDB - Software Engineering Best Practices: Coding Style - Version Control and Git for Data Scientists - Testing Code - Test-Driven Development - AGILE Methodology – Natural Language Processing.

Time Series Analysis - Probability: The Uniform Distribution and Pseudorandom Numbers – Non discrete, Non continuous Random Variables - Notation, Expectations, and Standard Deviation - Dependence, Marginal and Conditional Probability - Binomial , Poisson , Normal , Exponential , Log-Normal Distribution – Entropy – Statistics: Statistics in Perspective - Hypothesis Testing - Bayesian Statistics - Stochastic Modeling: Markov Chains - The Viterbi Algorithm - Continuous‐Time Markov Processes, Poisson Processes.