Duration
Data Mining Essentials for Users
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: Applying data mining techniques to extract patterns and insights.
- 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 users with practical skills in collecting historical data, performing data modeling, and using Power Query, Power Pivot, and data visualization for data analytics.
Chapter 3: Data Mining Process
- Understand the Business: Identifying business objectives and goals for data analysis.
- Understand the Data: Exploring historical data and understanding its context.
- Prepare the Data: Collecting and preprocessing historical data for analysis.
- Build the Model: Utilizing Power Query and Power Pivot to create data models and relationships.
- 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: Practical Data Analytics using Power Query, Power Pivot, and Data Visualization
- Introduction to Power Query: Importing, cleaning, and transforming data using Power Query.
- Utilizing Power Pivot: Creating data models and relationships for advanced data analysis.
- Data Visualization with Excel: Creating interactive dashboards and reports to visualize insights.
- Hands-on exercises: Participants will apply Power Query, Power Pivot, and data visualization techniques to simulate data analytics scenarios using sample datasets.
Programme scope, delivery arrangements and suitability are confirmed during enquiry.