Introduction

Data mining is a crucial technique in business intelligence, scientific research, fraud detection, and artificial intelligence, helping organizations uncover hidden patterns, correlations, and trends in large datasets.

This Data Mining Techniques and Applications Training Course provides a hands-on, practical approach to data preprocessing, pattern discovery, predictive modeling, and real-world applications. Participants will explore advanced algorithms, machine learning models, and visualization techniques to extract meaningful insights and enhance decision-making.

Objectives

By the end of this course, participants will:

1. Understand data mining concepts, methodologies, and applications.
2. Perform data cleaning, transformation, and feature selection.
3. Implement classification, clustering, and association rule mining.
4. Develop predictive models for decision-making.
5. Apply big data mining techniques and cloud-based analytics.
6. Use Python and R for advanced data mining.
7. Work on real-world projects in healthcare, finance, retail, and more.

Who Should Attend?

• Data Scientists & Analysts
• Business Intelligence (BI) Professionals
• IT & Software Engineers
• Finance, Healthcare & Marketing Professionals
• Researchers & Academics
• Anyone looking to master data mining techniques

Course Outline (5 Days)

Day 1: Introduction to Data Mining & Preprocessing

Morning Session

• What is Data Mining?

  • Evolution of data mining & its importance
  • Relationship between data mining, machine learning, and AI
  • Applications in finance, healthcare, e-commerce, cybersecurity

• Data Understanding & Preprocessing

  • Data collection & exploration
  • Handling missing values & data cleaning
  • Data transformation: normalization, scaling, encoding

Afternoon Session

• Feature Engineering & Selection Techniques

  • Importance of dimensionality reduction
  • Principal Component Analysis (PCA) & Feature Selection
  • Hands-on: Cleaning & preprocessing real-world datasets

• Hands-on Exercise

  • Data preparation for a customer segmentation project
  • Feature selection & visualization with Python (Pandas, Scikit-Learn)

Day 2: Classification & Prediction Techniques

Morning Session

• Supervised Learning for Classification

  • Decision Trees, Naïve Bayes, k-Nearest Neighbors (k-NN)
  • Logistic Regression & Support Vector Machines (SVM)
  • Hands-on: Building a classification model for fraud detection

• Model Performance Evaluation

  • Accuracy, Precision, Recall, F1-score, AUC-ROC
  • Cross-validation & overfitting prevention
  • Hands-on: Fine-tuning models for better accuracy

Afternoon Session

• Ensemble Learning: Boosting & Bagging

  • Random Forest, AdaBoost, Gradient Boosting
  • Hands-on: Using ensemble techniques for credit risk prediction

• Hands-on Exercise

  • Implementing classification algorithms for healthcare diagnostics
  • Hyperparameter tuning using GridSearchCV & RandomizedSearchCV

Day 3: Clustering & Association Rule Mining

Morning Session

• Unsupervised Learning for Data Mining

  • K-Means Clustering, Hierarchical Clustering, DBSCAN
  • Evaluating clusters using Silhouette Score & Elbow Method
  • Hands-on: Customer segmentation using K-Means

• Dimensionality Reduction for Clustering

  • PCA & t-SNE for high-dimensional data
  • Visualizing clusters with Matplotlib & Seaborn
  • Hands-on: Applying clustering to e-commerce transaction data

Afternoon Session

• Association Rule Mining & Market Basket Analysis

  • Apriori & FP-Growth algorithms
  • Hands-on: Mining association rules from a retail dataset

• Hands-on Exercise

  • Implementing association rule mining for recommendation systems
  • Generating insights from real-world transaction data

Day 4: Advanced Data Mining Techniques

Morning Session

• Anomaly Detection & Fraud Analytics

  • Isolation Forest, One-Class SVM, Autoencoders
  • Hands-on: Detecting fraudulent transactions in financial data

• Text Mining & Natural Language Processing (NLP)

  • Sentiment analysis, topic modeling (LDA)
  • Hands-on: Analyzing customer reviews & feedback using NLP

Afternoon Session

• Big Data Mining & Cloud-Based Solutions

  • Mining large datasets with Apache Spark
  • Google BigQuery & AWS for data mining
  • Hands-on: Using Spark MLlib for large-scale data mining

• Hands-on Exercise

  • Performing real-time anomaly detection in streaming data
  • Applying big data analytics for social media trends

Day 5: Real-World Data Mining Applications & Capstone Project

Morning Session

• Industry-Specific Data Mining Applications

  • Finance: Fraud detection, risk analysis
  • Healthcare: Disease prediction, medical diagnostics
  • Retail: Customer behavior, recommendation engines
  • Cybersecurity: Intrusion detection, threat analysis

• Automating Data Mining Pipelines

  • Data pipeline automation using Apache Airflow
  • Deploying machine learning models with Flask & FastAPI

Afternoon Session

• Capstone Project & Final Presentations

  • Choose from:
    1. Customer Segmentation for Targeted Marketing
    2. Fraud Detection in Banking Transactions
    3. Recommender System for E-commerce
  • Participants present their models & insights

• Certification & Networking Session

Post-Course Benefits

• Hands-on experience with real-world datasets
• Industry-ready projects for portfolio building
• Expert guidance on applying data mining to real business problems
• Continued learning resources (eBooks, case studies, and code repositories)