Advance Data Mining

Course Overview

Advance Data Mining this course focuses on advanced data mining concepts used in real-world analytics, research, and enterprise applications. Learners will gain strong practical skills in discovering patterns, building predictive models, and handling large-scale data.

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Module 1: Introduction to Data Mining

• Data mining vs data analytics vs machine learning

• Data mining process and real-world use cases

• Types of data and data sources

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Module 2: Data Preprocessing & Feature Engineering

• Data cleaning and handling missing values

• Data transformation and normalization

• Feature selection and dimensionality reduction

• Handling imbalanced datasets

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Module 3: Classification Techniques

• Decision Trees

• Naïve Bayes

• k-Nearest Neighbors (KNN)

• Support Vector Machines (SVM)

• Model evaluation metrics

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Module 4: Clustering Techniques

• K-Means clustering

• Hierarchical clustering

• DBSCAN

• Cluster validation techniques

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Module 5: Association Rule Mining

• Apriori algorithm

• FP-Growth algorithm

• Market basket analysis

• Rule evaluation metrics (support, confidence, lift)

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Module 6: Advanced Data Mining Techniques

• Ensemble methods

• Anomaly and outlier detection

• Text mining basics

• Web and social media data mining

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Module 7: Big Data & Scalable Data Mining

• Introduction to big data analytics

• Data mining with large datasets

• Overview of Spark-based data mining

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Module 8: Tools & Implementation

• Python for data mining

• NumPy, Pandas, Scikit-learn

• Visualization with Matplotlib & Seaborn

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Module 9: Real-World Projects

• End-to-end data mining project

• Case studies from business and industry

• Model building, evaluation, and deployment overview

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Who Can Enroll

• Students and fresh graduates

• Data analysts and professionals

• Anyone interested in advanced data analysis

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Outcomes

• Strong understanding of advanced data mining techniques

• Ability to work on real datasets

• Industry-ready practical skills

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Duration

• Validity: 365 Days

• Mode: Online / Recorded Sessions

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Certificate

• Course completion certificate provided