Overview
Course Methodology
The course uses a mix of interactive techniques, such as analytical tools, case studies, presentation development, presentation delivery, and individual feedback.
Course Objectives
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
Target Audience
This course is ideal for leaders, managers, and professionals who want to learn how data analytics can help improve organizational decision-making processes.
Target Competencies
- Understanding complex data
- Analyzing data
- Structuring data
- Balanced decision-making
- Visualizing data
- Assessing data patterns
- Influencing
Course Outline
- 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
Schedule & Fees
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