Understanding data analytics is a key skill that every manager needs. With the enormous explosion of data that organizations have access to, managers must know where their analysts are getting evidence from and how the data was generated. In addition, it has become imperative for managers to understand how data can be used to drive organizational decisions.
Participants in this course will be able to understand how robust data analysis can be used to drive decision making and how organizations uncover new information to provide avenues for strategic moves. No prerequisite in statistics or data analytics is needed for this course.
By the end of the course, participants will be able to:
- Justify the need for investing organizational resources in data analytics
- Ask the right questions to get the information needed for a robust data analysis project
- Describe the methods and techniques that are used for data analytics projects
- Explain how machine learning and artificial intelligence can help with data analytics
- Communicate and defend data-driven findings and decisions to stakeholders
This course is ideal for leaders, managers, and professionals who want to learn how data analytics can help improve organizational decision-making processes.
Overview of data analytics
- An introduction to business data analytics
- Quantitative data analysis
- Navigating big data
- Data scientists: The job of the 21st century
- Thinking like a data scientist
- The process of data analytics
Gathering the right information
- Focused research
- Getting the data you need
- Asking for data and analytics
- Designing business experiments
- Using the scientific method
- The difference between data and metrics
- Understanding what is being measured
- Trusting your data
- Ensuring data is safe to use
Analyzing data
- The value of data
- Data visualization
- Overview of predictive analytics
- Looking to the future by looking at the past
- Understanding regression analysis
- Evaluating the relationship between variables
- Acting on correlation
- Assessing confidence in findings
Data analysis tools and techniques
- Machine learning
- Investing in artificial intelligence
- Statistical significance
- Linear thinking in a non-linear world
- The pitfalls of data-driven decisions
- Avoiding cognitive traps
- Analytics that can cheat
- Paying attention to outliers
Communicating findings
- The value of data
- Making meaning out of data
- Making charts that persuade
- Giving meaning to numbers
- Communicating uncertainty
- Understanding the likelihood of events
- Challenging data findings
- Ensuring that data is thorough
- Decisions and data
- Influencing through story telling
