Introduction to Data Analysis Training in Germany

Anyone involved in operations, project management, business analysis, or management, who needs an introduction to data analysis.

• Terms, jargon, and impact of business intelligence and data analytics
• Scope and application of data analysis
• Impact of analytics on gaining competitive advantage and decision support
• Measure the performance of and improvement opportunities for business processes
• Need for tracking and identifying the root causes of deviation or failure
• Basic principles, properties, and application of probability theory and the normal distribution
• Introduction to different methods for summarizing information and presenting results including charts
• Statistical inference and drawing conclusions about the population
• Sample sizes and confidence intervals, and how they influence the accuracy of your analysis
• Forecasting and an introduction to simple linear regression analysis
• Interpret your results and draw sound and relevant conclusions on business
• Methods and algorithms for forecasting future results and to reduce current and future risk
• Process improvement and analysis skills
• Where powerful reference material exists and how to leverage to enhance your decision-making

1. Introduction to Data Analysis and Analytics

• Definition and history
• Current technology environment and the growing availability of data
• Role of the business analyst and data analyst
• Applications for gaining competitive advantages
• Fact-based decision making
• Process tracking and control

2. Application of Probability and Probability Distributions

• Key concepts and essentials
• Decision making under uncertainty
• Random variables
• Population and samples
• The normal distribution
• Many business distributions are nowhere near normal. constraints!
• Establishing confidence intervals

3. Introduction to Data Mining and Data Warehousing

• Data mining concepts and application
• Introduction to application benefits of data warehousing

4. Describing Information Needs

• Identify operational and executive information classes
• Modeling key decisions and the needs for information
• Describing key business transactions and documents
• Map information needs to underlying data
• Executive information needs and the balanced scorecard
• Pivot tables in Excel
• Tracking and managing business process performance
• Selecting measures and targets
• Measuring performance and finding performance gaps
• Root cause analysis

5. Data Exploration Concepts and Formulas

• Basic concepts
• Types of variables
• Selecting dependent and independent variables
• Sample vs. population
• Descriptive measures of a sample
• Key sample parameters
• Variability
• Sampling distributions
• Sample size
• Histograms
• Establishing and analyzing correlation among different variables
• Explanation of variance

6. Introduction to Risk Management

• Uncertainty and risk analysis
• Assessing your organization risk culture and level of risk tolerance
• Identifying, describing, ranking, prioritizing, and controlling risks
• When to use quantitative risk analysis
• Important risk management best practices

7. Forecasting

• Forecasting methods and models
• History of forecasting
• Long and short term forecasts
• Heuristics
• Time series analysis
• Establishing trends and business cycles (i.e., seasonality) and confidence limits
• Selecting independent variables for predictive models including regression techniques

8. Review

• Data analysis and analytics
• Probability and distributions
• Data mining, data warehousing, and the need for information
• Statistic inference, forecasting, and decision support
• Next steps options

9. Additional Resources and Exercises