Comprehensive Data Mining Course for Real-World Insights

The Data Mining course at Omdena School aims to provide participants with a deep understanding of data mining concepts, techniques, and tools. Throughout the course, students will gain hands-on experience in exploring and analyzing large datasets to discover valuable patterns and insights.

By the end of the course, participants will be equipped with the necessary skills to make data-driven decisions and solve complex problems effectively.

1. Basic Programming Skills in Python

2. Familiarity with basic data analysis techniques and concepts

3. Databases and SQL

4. Machine Learning Basics

Introduction to Data Mining

• Understanding data mining and its significance in the modern world.
• Overview of data mining tasks, algorithms, and applications.
• Data preprocessing techniques for cleaning and preparing datasets.

Exploratory Data Analysis (EDA) and Visualization

• Importance of EDA in understanding data distribution and characteristics.
• Utilizing visualizations to explore datasets effectively.
• Applying statistical measures for data exploration and pattern identification.

Classification Techniques

• Introduction to supervised learning and classification.
• Exploring Decision Trees: ID3, C4.5, CART algorithms.
• Understanding the Naive Bayes Classifier and its applications.

Clustering Techniques

• Introduction to unsupervised learning and clustering.
• Applying k-Means clustering for grouping data points.
• Understanding Hierarchical clustering and its advantages.

Association Rule Mining and Anomaly Detection

• Implementing the Apriori algorithm for association rule mining.
• Mining frequent itemsets and generating association rules.
• Identifying anomalies using techniques like isolation forest and one-class SVM.

Data Mining with Big Data and Parallel Processing

• Handling large datasets using distributed computing.
• Overview of MapReduce and Spark for efficient data mining.
• Scaling data mining algorithms for big data applications.

Real-world Applications and Case Studies

• Applying data mining techniques to real-world datasets from diverse domains.
• Case studies showcasing data mining applications in finance, healthcare, marketing, etc.
• Extracting actionable insights from data-driven decision-making.

 Final Project Presentation

• Participants present their data mining projects to the class.
• Sharing insights and lessons learned during the course.
• Course wrap-up and discussion of future learning opportunities.