Duration
Advanced Data Mining for Management
2 days
Overview
Data mining refers to extracting or mining knowledge from large amounts of data. In other words, Data mining is the science, art, and technology of discovering large and complex bodies of data in order to discover useful patterns. Theoreticians and practitioners are continually seeking improved techniques to make the process more efficient, cost-effective, and accurate.
Objective
To provide users with a foundational understanding of data mining concepts, techniques, and practical skills in utilizing tools like Power Query, Power Pivot, and data visualization for data analytics.
Chapter 1: Overview of Data Mining
- What is Data Mining?
- Data Mining History and Origins
- Data Mining vs Data Analytics and Data Warehousing
- How Data Mining Works
- Types of Data Mining
- Applications of Data Mining
- Advantages and Disadvantages of Data Mining
Chapter 2: Knowledge Discovery from Data Steps
- Data Selection: Identifying relevant data sources for analysis.
- Data Preprocessing: Cleaning and transforming raw data for analysis.
- Data Transformation: Structuring and organizing data for modeling.
- Data Mining: Utilizing statistical analysis and machine learning algorithms for pattern identification and prediction.
- Pattern Evaluation: Assessing the validity and significance of mined patterns.
- Knowledge Representation: Visualizing and interpreting mined knowledge.
- Knowledge Refinement: Iteratively refining models and insights based on feedback.
- Knowledge Dissemination: Communicating findings and insights to stakeholders.
Objective
To equip managers with practical skills in collecting historical data, performing data modeling, and using statistical analysis and machine learning algorithms for pattern identification and prediction in historical data.
Chapter 3: Data Mining Process
- Understand the Business: Aligning data mining goals with business objectives and strategies.
- Understand the Data: Exploring historical data and understanding its context.
- Prepare the Data: Collecting and preprocessing historical data for analysis.
- Build the Model: Utilizing statistical analysis and machine learning algorithms for advanced data modeling.
- Evaluate the Results: Analyzing and interpreting insights generated from data modeling.
- Implement Change and Monitor: Incorporating insights into decision-making processes and monitoring performance.
Chapter 4: Advanced Data Mining Techniques
- Statistical Analysis: Introduction to statistical methods for analyzing data distributions, correlations, and trends.
- Machine Learning Algorithms: Overview of machine learning algorithms such as regression, classification, clustering, and neural networks for predictive analytics.
- Practical Exercises: Participants will apply statistical analysis and machine learning algorithms to identify patterns and make predictions in historical data using sample datasets.
Programme scope, delivery arrangements and suitability are confirmed during enquiry.