syllabus

Introduction to Data Science – Organized Syllabus

1. Association Rule Mining

1.1 Introduction to Association Rule Mining

• Frequent Patterns

• Associations

• Correlations

• Basic Concepts and Road Map

1.2 Association Rules

• Definition and Concepts

• Support and Confidence

• Strong Association Rules

1.3 Apriori Algorithm

• Working of Apriori Algorithm

• Candidate Generation

• Frequent Itemset Mining

---

2. Classification and Prediction

2.1 Classification

Introduction to Classification

• Definition of Classification

• Applications of Classification

Issues Regarding Classification

• Data Quality

• Overfitting and Underfitting

• Bias and Variance

Classification Techniques

• Classification by Decision Tree Induction

• Bayesian Classification

• Rule-Based Classification

Evaluation of Classifiers

• Metrics for Evaluating Classifier Performance

  • Accuracy

  • Precision

  • Recall

  • F1-Score

• Holdout Method

• Random Subsampling

---

2.2 Prediction

Introduction to Prediction

• Definition of Prediction

• Applications of Prediction

Issues Regarding Prediction

• Prediction Accuracy

• Model Complexity

Accuracy and Error Measures

• Mean Absolute Error (MAE)

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

Evaluating Accuracy

• Evaluating the Accuracy of a Classifier or Predictor

---

3. Clustering

3.1 Introduction to Clustering

• Cluster Analysis

• Applications of Clustering

3.2 Hierarchical Clustering

• Agglomerative Hierarchical Clustering

• Divisive Hierarchical Clustering

• Comparison: Agglomerative vs Divisive

3.3 Distance Measures

• Distance Measures in Algorithms

  • Euclidean Distance

  • Manhattan Distance

  • Cosine Similarity

3.4 Evaluation of Clustering

• Cluster Validation

• Cluster Quality Measures

---

4. Linear Regression

4.1 Introduction to Linear Regression

• Prediction using Linear Regression

• Regression Concepts

4.2 Gradient Descent

• Cost Function

• Gradient Descent Algorithm

4.3 Linear Regression Models

• Linear Regression with One Variable

• Linear Regression with Multiple Variables

4.4 Advanced Regression Concepts

• Polynomial Regression

• Feature Scaling

• Feature Selection

---

5. Logistic Regression

5.1 Introduction to Logistic Regression

• Classification using Logistic Regression

• Logistic Regression vs Linear Regression

5.2 Logistic Regression Models

• Logistic Regression with One Variable

• Logistic Regression with Multiple Variables

---

6. Deep Learning

6.1 Introduction to Deep Learning

• History of Deep Learning

• Scope and Specifications

• Why Deep Learning Now?

6.2 Neural Network Fundamentals

• Building Blocks of Neural Networks

• Neural Networks Overview

• Units in Neural Networks

• Layers in Neural Networks

• Activation Functions

• Normalization

6.3 Neural Network Architectures

• Forward Neural Networks

• Backward Neural Networks

• XOR Model

6.4 Model Optimization

• Cost Function Estimation

  • Maximum Likelihood

• Hyper-Parameter Tuning

6.5 Deep Learning Hardware

• GPUs and TPUs

• Deep Learning Hardware Requirements

6.6 Convolution Neural Networks (CNN)

• Introduction to CNN

• CNN Architecture

• Applications of CNN

---

7. Case Studies and Practical Applications

7.1 Classification and Prediction Datasets

• Iris Dataset

• Loan Dataset

• Titanic Survival Dataset

7.2 Time Series and Real-World Datasets

• Share Market Dataset

• COVID-19 Dataset

7.3 Practical Data Science Workflow

• Data Collection

• Data Cleaning

• Data Visualization

• Model Building

• Model Evaluation