Module 1: Big Data Analysis & Technology Concepts         

• • Big Data Analysis Lifecycle (from business case evaluation to data analysis and visualization)
• • A/B Testing, Correlation
• • Regression, Heat Maps
• • Time Series Analysis
• • Network Analysis
• • Spatial Data Analysis
• • Classification, Clustering
• • Outlier Detection
• • Filtering (including collaborative filtering & content-based filtering)
• • Natural Language Processing
• • Sentiment Analysis, Text Analytics
• • File Systems & Distributed File Systems, NoSQL
• • Distributed & Parallel Data Processing,
• • Processing Workloads, Clusters
• • Cloud Computing & Big Data
• • Foundational Big Data Technology Mechanisms

Module 2: Fundamentals Big Data Analysis & Science       

• • Data Science, Data Mining & Data Modeling
• • Big Data Dataset Categories
• • Exploratory Data Analysis (EDA) (including numerical summaries, rules & data reduction)
• • EDA analysis types (including univariate, bivariate & multivariate)
• • Essential Statistics (including variable categories & relevant mathematics)
• • Statistics Analysis (including descriptive, inferential, correlation, covariance & hypothesis testing)
• • Data Munging & Machine Learning
• • Variables & Basic Mathematical Notations
• • Statistical Measures & Statistical Inference
• • Distributions & Data Processing Techniques
• • Data Discretization, Binning, Clustering
• • Visualization Techniques & Numerical Summaries
• • Correlation for Big Data
• • Time Series Analysis for Big Data

Module 3: Advanced Big Data Analysis & Science    

• • Statistical Models, Model Evaluation Measures (including cross-validation, bias-variance, confusion matrix & f-score)
• • Machine Learning Algorithms, Pattern Identification (including association rules & apriori algorithm)
• • Advanced Statistical Techniques (including parametric vs. non-parametric, clustering vs. non-clustering distance-based, supervised vs. semi-supervised)
• • Linear Regression & Logistic Regression for Big Data
• • Decision Trees for Big Data
• • Classification Rules for Big Data
• • K Nearest Neighbor (kNN) for Big Data
• • Naïve Bayes for Big Data
• • Association Rules for Big Data
• • K-means for Big Data
• • Text Analytics for Big Data
• • Outlier Detection for Big Data

Module 4: Big Data Analysis & Science lab

This course module covers a series of exercises and problems designed to test the participant's ability to apply knowledge of topics covered previously in course modules 4 and 5. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data analysis and science practices as they are applied and combined to solve real-world problems.

As a hands-on lab, this course incorporates a set of detailed exercises that require participants to solve various inter-related problems, with the goal of fostering a comprehensive understanding of how different data analysis techniques can be applied to solve problems in Big Data environments and used to make significant, relevant predictions that offer increased business value.