Data analysis is a topic that is important to everyone in business today. Data analysis is no longer the domain of technical specialists, statisticians, and data scientists. Everyone has access to data today, and we all analyze data as a routine part of our day-to-day jobs. Understanding the basics of analyzing data is important for every business professional today.

Data analysis is much more than applying statistics to data and creating charts and graphs. It is the practice of finding patterns in data and finding meaning in data. Several different data analysis methods and processes exist, each with distinct purpose and application. Underlying all of them, however, are the core activities needed to for data analysis that is purposeful, accurate, meaningful, and valuable: establishing analysis purpose with problem framing, acquiring the right data for analysis, exploring and understanding the data, preparing data for analysis, finding patterns and meaning, data visualization, and communicating findings and conclusions of analysis. Each of these activities has a role in efficiency, effectiveness, and business impact of data analysis. This course examines each of those activities and the skills needed to perform them well.

The course also looks at human factors in data analysis. Cultural influences are discussed a important considerations. Traits of a good analyst – curiosity, imagination, skepticism, and more – are explored along with collaboration and complementary thinking styles. This course provides a comprehensive look at the work of analyzing data. From introductory concepts through all of the technical activities, to the human side of data analysis, you’ll get complete coverage of data analysis fundamentals.

• Data analysis concepts, applications, and processes
• Descriptive and inferential statistics concepts and their applications in data analysis
• Preparing for data analysis – project and problem framing
• Getting the right data for analysis – data searching and data acquisition
• Understanding the data – data exploration with profiling and visualization
• Data cleansing and data structuring
• Data improvement, enrichment, and formatting
• Statistical data analysis techniques
• Algorithmic data analysis techniques
• Data visualization and storytelling
• Data analyst traits and skills

• Business managers who are the de facto data analysts for most organizations
• Business and data analysts whose primary responsibility is finding meaning and insights in data
• Managers of organizations and teams with data analysis responsibilities
• Everyone who uses data for decision making in their day-to-day activities
• Anyone with data analysis responsibilities
• Anyone who has access to data and desire to get more value from the data
• Everyone with an interest in data analysis