Data Mining Course Online

15 Lesson

30 Hours

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igmGuru offers a comprehensive Data Mining Training Program designed for learners of all levels. This course covers all essential data mining concepts such as data preprocessing, exploratory data analysis, classification, clustering, association rule mining, text mining, and big data techniques using tools such as Hadoop and Spark. Our Data Mining Course curriculum is designed by industry experts with over 15 years of experience in analytics, machine learning, and real-world data engineering. Enroll in our Data Mining Certification Course to gain practical, hands-on experience through real datasets, case studies, and guided project-based exercises that prepare you to apply data mining techniques effectively in real-world scenarios.

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Overview

Prerequisites

Familiarity with at least one programming language (preferably Python, R, or Java).
Understanding of basic mathematics: Algebra, Probability, Statistics.
Fundamental understanding of databases.

What Will You Learn

Understand the foundations of data mining.
Prepare and preprocess real-world data.
Apply core data mining techniques.
Discover patterns and relationships.
Conduct advanced data analysis.
Work with text, web, and time series data.
Work with big data mining tools.
Build and optimize machine learning pipelines.
Practice ethical and responsible data mining.
Develop a complete end-to-end data mining project.

Key Features

1 On 1 Training Option Available
24 X 7 Lifetime Support & Access
Small Batches Upto 10 Participants
Experienced & Professional Trainers
100% Job Assistance
Flexible Schedule

Course Curriculum

Lesson 1 - Introduction to Data Mining

1. What is data mining?

2. Knowledge Discovery in Databases (KDD) process

3. Data mining vs. statistics vs. machine learning

4. Applications and case studies

Lesson 2 - Data Preprocessing

1. Data cleaning (handling missing values, noise, outliers)

2. Data integration and transformation

3. Normalization and standardization

4. Feature selection & feature extraction

5. Dimensionality reduction basics

Lesson 3 - Exploratory Data Analysis (EDA)

1. Descriptive statistics

2. Visualization techniques

3. Correlation and covariance

4. Detecting patterns and anomalies

Lesson 4 - Classification Techniques

1. Decision Trees (ID3, C4.5, CART)

2. Naïve Bayes

3. k-Nearest Neighbors (k-NN)

4. Support Vector Machines (SVM)

5. Evaluation metrics (accuracy, precision, recall, F1, ROC, AUC)

Lesson 5 - Regression Techniques

1. Linear and nonlinear regression

2. Logistic regression

3. Regularization (Lasso, Ridge)

4. Error metrics (MAE, MSE, RMSE)

Lesson 6 - Clustering Algorithms

1. K-Means clustering

2. Hierarchical clustering

3. DBSCAN and density-based clustering

4. Cluster validation indexes

Lesson 7 - Association Rule Mining

1. Market basket analysis

2. Apriori algorithm

3. FP-Growth algorithm

4. Support, confidence, lift metrics

Lesson 8 - Dimensionality Reduction

1. Principal Component Analysis (PCA)

2. Singular Value Decomposition (SVD)

3. t-SNE and other manifold learning approaches

Lesson 9 - Anomaly & Outlier Detection

1. Statistical approaches

2. Density-based and distance-based methods

3. Isolation Forest

4. Practical anomaly detection scenarios

Lesson 10 - Text Mining & Natural Language Processing

1. Text preprocessing (tokenization, stemming, stopword removal)

2. TF-IDF

3. Sentiment analysis

4. Topic modeling (LDA)

Lesson 11 - Web Mining

1. Web content mining

2. Web structure mining (PageRank & link analysis)

3. Web usage mining

Lesson 12 - Time Series Mining

1. Trends, seasonality, and decomposition

2. ARIMA models

3. Sequence mining

4. Forecasting methods

Lesson 13 - Big Data Mining Techniques

1. Challenges of large-scale data

2. Hadoop ecosystem (HDFS, MapReduce)

3. Spark for data mining

Lesson 14 - Data Mining with Machine Learning Pipelines

1. Model selection

2. Cross-validation

3. Hyperparameter tuning (Grid Search, Random Search, Bayesian Optimization)

Lesson 15 - Ethics, Privacy & Responsible Data Mining

1. Data anonymization

2. Bias and fairness

3. Ethical AI considerations

4. Legal frameworks (GDPR, etc.)

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Course Fees

Online Class Room Program

US $ 799.00

100% Money Back Guarantee

Duration : 30 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 05 Jul 2026
Weekday Batch 06 Jul 2026
Weekend Batch 11 Jul 2026

Corporate Training

Customized Training Delivery Model
Flexible Training Schedule Options
Industry Experienced Trainers
24x7 Support

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Want to know Today's Offer

Data Mining Certification

Upon successfully completing the Data Mining course, we will provide an industry-recognized Course Completion Certificate to all learners. This certificate validates your expertise in data preprocessing, pattern discovery, machine learning techniques, and real-world data analysis.

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Data Mining Course Online