Big Data, Analytics, and the Data Scientist Role (5%) : 3 Questions

• Define and describe the characteristics of Big Data
• Describe the business drivers for Big Data analytics and data science
• Describe the Data Scientist role and related skills  

Data Analytics Lifecycle (8%) : 5 Questions

• Describe the data analytics lifecycle purpose and sequence of phases
• Discovery - Describe details of this phase, including activities and associated roles
• Data preparation - Describe details of this phase, including activities and associated roles
• Model planning - Describe details of this phase, including activities and associated roles
• Model building - Describe details of this phase, including activities and associated roles 

Initial Analysis of the Data (15%) : 9 Questions 

• Explain how basic R commands are used to initially explore and analyze the data
• Describe and provide examples of the most important statistical measures and effective visualizations of data
• Describe the theory, process, and analysis of results for hypothesis testing and its use in evaluating a model 

Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods (40%) : 24 Questions 

• K-means clustering
• Association rules
• Linear regression
• Logistic Regression
• Naïve Bayesian classifiers
• Decision trees
• Time Series Analysis
• Text Analytics 

Advanced Analytics for Big Data - Technology and Tools (22%) : 14 Questions

• Describe the technological challenges posed by Big Data
• Describe the nature and use of MapReduce and Apache Hadoop
• Describe the Hadoop ecosystem and related product use cases
• Describe in-database analytics and SQL essentials
• Describe advanced SQL methods: window functions, ordered aggregates, and MADlib  

Operationalizing an Analytics Project and Data Visualization Techniques (10%) : 6 Questions

• Describe best practices for communicating findings and operationalizing an analytics project
• Describe best practices for building project presentations for specific audiences
• Describe best practices for planning and creating effective data visualizations