Duration
Advanced Data Mining Techniques with Python for Developers and Administrators
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 developers and administrators with a solid understanding of data mining concepts, techniques, and practical skills in implementing data mining techniques using Python.
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: Introduction to various data mining techniques including association, classification, clustering, prediction, sequential patterns, and regression.
- 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 developers and administrators with practical skills in implementing data mining techniques using Python, including association, classification, clustering, prediction, sequential patterns, and regression.
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: Implementing data mining techniques using Python libraries such as scikit-learn, pandas, and numpy.
- Evaluate the Results: Analyzing and interpreting insights generated from data mining techniques.
- Implement Change and Monitor: Incorporating insights into decision-making processes and monitoring performance.
Chapter 4: Advanced Data Mining Techniques with Python
- Association Analysis: Identifying patterns and relationships among variables in datasets.
- Classification: Predicting categorical outcomes based on input variables.
- Clustering: Grouping similar data points together based on certain criteria.
- Prediction: Forecasting future trends or values based on historical data.
- Sequential Patterns: Discovering patterns in sequential data such as time series.
- Regression: Modeling relationships between variables to make predictions.
- Practical Exercises: Participants will implement data mining techniques using Python to analyze sample datasets and solve real-world business problems.
Programme scope, delivery arrangements and suitability are confirmed during enquiry.