TABLE OF CONTENTS
Module 1: The Value of Data
Give examples of how the analysis of data has led to discovery and innovation.
Describe how data can be used to reduce uncertainty and risk related to organizational decisions.
Reveal the risks of poor data management.
List examples of data sources.
Summarize the uses of predictive and descriptive data mining.
Contrast developed and emergent analytics.
Explain how to mitigate the negative consequences that can arise when using data analytic models to predict human behavior.
Module 2: Working with Data
Perform data exploration using Microsoft Excel.
Apply advanced skills in Microsoft Excel to analyze large amounts of information.
Discuss common errors made when using formulas in Microsoft Excel.
Evaluate how the collation and analysis of multiple sources of data can impact organizational decisions.
Compile relevant data from external sources.
Summarize patterns and insights gleaned from data analysis.
Construct well-designed tables and graphs that effectively communicate important information.
Module 3: Data Typologies and Governance
Describe what data represents.
Explain how data can be classified, captured, and formatted.
Differentiate common data typologies.
Explain how to capture, store and retrieve information from unstructured data.
Compare data warehouses and data lakes.
Describe the dimensions of data quality.
Explain some of the common data cleaning and preparation methods.
Contrast Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP).
Explain the stages of the data life cycle.
Design a data collection system that enables users to efficiently prepare, explore, and interpret data.
Module 4: Business Statistics
Explain the measurement scales applied to data.
Summarize data using statistical terms.
Contrast different descriptive and predictive data mining methods.
Contrast data sampling techniques.
Discuss the importance of accounting for variable interactions.
Apply the rules of probability.
Explain the application of the Central Limit Theorem.
Apply hypothesis testing to a scenario.
Module 5: Optimizing and Forecasting
Differentiate correlation and causation.
Give examples of how linear regression is used in organizations.
Perform regression analysis using Microsoft Excel.
Interpret the output of a regression analysis.
Use Microsoft Excel to solve linear programming problems.
Interpret the output of a linear programming optimization simulation.
Module 6: Other Data Analytic Tools
Explain the use of a NoQL database.
Explain how Apache Hadoop’s MapReduce works.
Describe how Natural Language Processing (NLP) is applied to text mining.
Relate how graph analytics can be applied within different fields.
Perform data analysis using R.
Module 7: Data Visualization
Interpret data analysis in non-technical language.
Recommend visualization strategies for different types of data, purposes, and audiences.
Illustrate how the effective use of visual design components can lead to clear and efficient communication.
Create a compelling statistical narrative through data visualizations.