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Big Data,
Analytics, and the
Data Scientist Role (5%) : 3
Quuestions
•
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
•
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
•
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 (3 Questions)
•
Association rules (3
Questions)
•
Linear regression (3
Questions)
•
Logistic Regression (3
Questions)
•
Naïve Bayesian classifiers (3
Questions)
•
Decision trees (3
Questions)
•
Time Series Analysis (3
Questions)
•
Text Analytics (3
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
•
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
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