Data Mining and Data Science

Contents

1. Introduction to Data Mining
1. Introduction
2. Definition Data Mining
3. Data Mining Issues
4. Stages of Data Mining Process (KDD)
5. Data Mining Techniques/Tasks
6. Knowledge Representation Methods
7. Data Mining Applications
8. Data Preprocessing
2. Data Warehousing
1. Introduction to Data Warehouse
2. Data Warehouse Architecture and its Components
2.1 Data Warehouse Components
3. Data Modeling with OLAP
3.1 Differences between OLTP and OLAP
3.2 Fact Table, Dimension Table, OLAP cube
4. Schema Design
5. Introduction to Machine Learning
6. Introduction to Pattern Matching
7. Case Study Based on Schema Design
3. Classification
1. Introduction
2. Decision Tree
2.1 Construction Principle
2.2 Attribute Selection Measures
2.3 Tree Pruning
3. Rule-Based Classification
3.1 Using IF-THEN Rules for Classification
3.2 Rule Extraction from a Decision Tree
4. Bayes Classification Methods
4.1 Bayes’ Theorem
4.2 Naïve Bayesian Classification
5. Bayesian Networks
6. Parameter and Structure Learning
7. Linear Classifier
8. Perceptron
9. k-Nearest-Neighbor Classifiers
10. SVM Classifiers
10.1 Working of SVM
10.2 Types of SVM
11. Regression
11.1 Linear Regression
11.2 Non Linear Regression
12. Introduction to Prediction
4. Clustering and Association Rule Mining
1. Introduction
2. Hierarchical Methods
2.1 K-Means: A Centroid-Based Technique
3. Introduction to Association Rule Mining
3.1 Market Basket Analysis
3.2 The Apriori Algorithm
3.3 Types of Association Rules
5. Introduction to Data Science
1. Basics of Data
2. What is Data Science?
2.1 Data Science Process
2.2 Stages in Data Science Project
2.3 Stages
2.4 Applications of Data Science in various Fields
3. Basics of Data Analytics
3.1 Types of Analytics: Descriptive, Predictive, Prescriptive
4. Statistical Inference - Populations and samples - Statistical Modeling - Probability Distributions
4.1 Populations
4.2 Samples
4.3 Statistical Modeling
4.5 Statistical Modeling Techniques
4.6 Probability Distribution
6. EDA and Data Visualization
1. What is Exploratory Data Analysis?
2. Steps in EDA
3. Basic Tools (Plots, Graphs and Summary Statistics) of EDA
4. Types of Exploratory Data Analysis
5. Basic Principles of Data Visualization
6. Benefits of Data Visualization
7. Data Visualization Techniques
8. Data Visualization Tools
8.1 Data Visualization Tools for Business
8.2 Data Visualization Tools for Coders