Business Intelligence - Data Analytics for Smart Decision Making

Details

The course will begin by introducing the importance of data and information analysis in modern management, as well as the key concepts and terminology related to data analysis. Participants will then learn various data analysis techniques, such as descriptive statistics, inferential statistics, hypothesis testing, and regression analysis. The course will also cover data visualization and presentation techniques, such as charts, graphs, and dashboards. Finally, the course will focus on using data to make smart decisions, including how to identify patterns and trends, create predictive models, and perform scenario analysis.

Target Group

This training course is designed for managers and decision-makers in any industry who want to improve their skills in using data to make informed decisions. This includes middle and senior-level managers, executives, and business analysts who want to develop their data analysis skills and gain practical experience in using data to solve business problems. No prior experience in data analysis is required, although a basic understanding of statistics and Excel is helpful.

Goals

• Understand the importance of data and information analysis in modern management and decision-making.
• Learn the essential concepts and terminology related to data analysis and visualization.
• Develop skills in using various data analysis techniques, such as descriptive statistics, inferential statistics, hypothesis testing, and regression analysis.
• Learn how to create effective data visualizations using charts, graphs, and dashboards.
• Understand how to use data to identify patterns and trends, create predictive models, and perform scenario analysis.
• Gain practical experience in applying data analysis techniques to real-world management problems.

Outlines

Introduction to Data and Information Analysis

• Understanding the importance of data and information analysis in modern management
• Key concepts and terminology related to data analysis
• Types of data and data sources
• Overview of data analysis process
• Common data analysis tools and software

Data Analysis Techniques

• Descriptive statistics
• Inferential statistics
• Hypothesis testing
• Regression analysis
• Data quality and data cleaning