Data Science Course Syllabus

Getting Started

• Course Introduction
• Course Material & Lab Setup
• Installation
• Python Basic – Part – 1
• Python Basic – Part – 2
• Advance Python – Part – 1
• Advance Python – Part – 2

Statistics and Probability Refresher, and Python Practice

• Types of Data
• Mean, Median, Mode
• Using mean, median, and mode in Python
• Variation and Standard Deviation
• Probability Density Function; Probability Mass Function
• Common Data Distributions
• Percentiles and Moments
• A Crash Course in matplotlib
• Covariance and Correlation
• Conditional Probability
• Exercise Solution: Conditional Probability of Purchase by Age
• Bayes’ Theorem

Predictive Models

• Linear Regression
• Polynomial Regression
• Multivariate Regression, and Predicting Car Prices
• Multi-Level Models

Machine Learning with Python

• Supervised vs. Unsupervised Learning, and Train/Test
• Using Train/Test to Prevent Overfitting a Polynomial Regression
• Bayesian Methods: Concepts
• Implementing a Spam Classifier with Naive Bayes
• K-Means Clustering
• Clustering people based on income and age
• Measuring Entropy
• Install GraphViz32. Decision Trees: Concepts
• Decision Trees: Predicting Hiring Decisions
• Ensemble Learning
• Support Vector Machines (SVM) Overview
• Using SVM to cluster people using scikit-learn

Recommender Systems

• User-Based Collaborative Filtering
• Item-Based Collaborative Filtering
• Finding Movie Similarities
• Improving the Results of Movie Similarities
• Making Movie Recommendations to People
• Improve the recommender’s results

More Data Mining and Machine Learning Techniques

• K-Nearest-Neighbors: Concepts
• Using KNN to predict a rating for a movie
• Dimensionality Reduction; Principal Component Analysis
• PCA Example with the Iris data set
• Data Warehousing Overview: ETL and ELT
• Reinforcement Learning

Dealing with Real-World Data

• Bias/Variance Tradeoff
• K-Fold Cross-Validation to avoid overfitting
• Data Cleaning and Normalization
• Cleaning web log data
• Normalizing numerical data
• Detecting outliers

Apache Spark: Machine Learning on Big Data

• Lab Set-up Warning & Error Handling
• Installing Spark – Part – 1
• Installing Spark – Part – 2
• Spark Introduction
• Spark and the Resilient Distributed Dataset (RDD)
• Introducing MLLib
• Decision Trees in Spark
• K-Means Clustering in Spark
• TF / IDF
• Searching Wikipedia with Spark
• Using the Spark 2.0 DataFrame API for MLLib

Experimental Design

• A/B Testing Concepts
• T-Tests and P-Values
• Hands-on With T-Tests
• Determining How Long to Run an Experiment
• A/B Test Gotchas

Deep Learning and Neural Networks

• Deep Learning Pre-Requisites
• The History of Artificial Neural Networks
• Deep Learning in the Tensorflow Playground
• Deep Learning Details
• Introducing Tensorflow
• Using Tensorflow, Part 1
• Using Tensorflow, Part 2
• Introducing Keras
• Using Keras to Predict Political Affiliations
• Convolutional Neural Networks (CNN’s)
• Using CNN’s for handwriting recognition
• Recurrent Neural Networks (RNN’s)
• Using a RNN for sentiment analysis
• The Ethics of Deep Learning
• Learning More about Deep Learning

Statistics and Data Science in R – Intro

• Introduction to R
• R and R studio Installation & Lab Setup
• Descriptive Statistics

Descriptive Statistics

• Mean, Median, Mode
• Our first foray into R : Frequency Distributions
• Draw your first plot : A Histogram
• Computing Mean, Median, Mode in R
• What is IQR (Inter-quartile Range)?
• Box and Whisker Plots
• The Standard Deviation
• Computing IQR and Standard Deviation in R

Inferential Statistics

• Drawing inferences from data
• Random Variables are ubiquitous
• The Normal Probability Distribution
• Sampling is like fishing
• Sample Statistics and Sampling Distributions

Case studies in Inferential Statistics

• Case Study 1 : Football Players (Estimating Population Mean from a Sample)
• Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)
• Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)
• Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)
• Case Study 5: A/B Testing (Comparing the means of two populations)
• Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)

Diving into R

• Harnessing the power of R
• Assigning Variables
• Printing an output
• Numbers are of type numeric
• Characters and Dates
• Logicals

Vectors

• Data Structures are the building blocks of R
• Creating a Vector
• The Mode of a Vector
• Vectors are Atomic
• Doing something with each element of a Vector
• Aggregating Vectors
• Operations between vectors of the same length
• Operations between vectors of different length
• Generating Sequences
• Using conditions with Vectors
• Find the lengths of multiple strings using Vectors
• Generate a complex sequence (using recycling)
• Vector Indexing (using numbers)
• Vector Indexing (using conditions)
• Vector Indexing (using names)

Arrays

• Creating an Array
• Indexing an Array
• Operations between 2 Arrays
• Operations between an Array and a Vector
• Outer Products

Matrices

• A Matrix is a 2-Dimensional Array
• Creating a Matrix
• Matrix Multiplication
• Merging Matrices
• Solving a set of linear equations

Factors

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• What is a factor?
• Find the distinct values in a dataset (using factors)
• Replace the levels of a factor
• Aggregate factors with table()
• Aggregate factors with tapply()

Lists and Data Frames

• Introducing Lists
• Introducing Data Frames
• Reading Data from files
• Indexing a Data Frame
• Aggregating and Sorting a Data Frame
• Merging Data Frames

Regression quantifies relationships between variables

• Linear Regression in Excel : Preparing the data.
• Linear Regression in Excel : Using LINEST()

Linear Regression in R

• Linear Regression in R : Preparing the data
• Linear Regression in R : lm() and summary()
• Multiple Linear Regression
• Adding Categorical Variables to a linear mode
• Robust Regression in R : rlm()
• Parsing Regression Diagnostic Plots

Data Visualization in R

• Data Visualization
• The plot() function in R
• Control color palettes with RColorbrewer
• Drawing bar plots
• Drawing a heatmap
• Drawing a Scatterplot Matrix
• Plot a line chart with ggplot