Contents

1. Introduction to Data Science    

1. Introduction

1.1 What is Data Science?

1.2 Brief History of Data Science

2. The 5 V’s: Volume, Velocity, Variety, Value and Veracity

3. Why Learn Data Science?

3.1 Goals of Data Science

4. Applications of Data Science

5. The Data Science Lifecycle

6. Types of Data

6.1 Structured Data

6.2 Semi-Structured Data

6.3 Unstructured Data

6.4 Differences between Structured, Semi-structured and Unstructured Data

7. Data Sources

7.1 Open Data

7.2 Social Media Data

7.3 Multimodal Data

7.4 Standard Datasets

8. Data Formats

8.1 How is Information Stored in Files?

8.2 Difference between Rasterized images and Vectorized Images

9. Data Scientist’s Toolbox: Data Science Tools, Libraries, and Platforms

9.1 Data Science Tools, Libraries, and Platforms

9.2 Different Ways a Data Scientist can Add Value to Business

2. Data Engineering

1. Introduction

2. Data Engineering vs. Data Science

3. Data Engineering Process

4. Data Engineering Tools and Technologies

5. Data Engineering Concepts

5.1 Data Pipelined

5.2 Data Pipeline Challenges

5.3 Data Warehouse

5.4 Data Warehouse Architecture

5.5 Data Marts

5.6 Data Lakes

6. ETL Process and Data Pipeline

6.1 Steps of the ETL Data Pipeline

6.2 ETL Use Case: Business Intelligence

6.3 ELT – The Next Generation of ETL

7. OLAP and OLAP Cubes

8. Data Integration and Other Data Integration Methods

9. Data Quality Management

10. Data Quality: Why Preprocess the Data?

3. Statistical Data Analysis

1. Role of Statistics in Data Science    

2. Descriptive Statistics

2.1 Measuring the Frequency

2.2 Measuring the Central Tendency- Mean, Median, and Mode

2.3 Measuring the Dispersion: Range, Standard Deviation, Variance, Interquartile Range

2.4   Descriptive Statistics Using the pandas describe() Function

3. Inferential Statistics: Hypothesis Testing, Multiple Hypothesis Testing, Parameter Estimation Methods

3.1 Hypothesis and Hypothesis Testing

3.2 Types of Hypothesis Testing

4. ANOVA

5. Concept of Outlier, Types of Outliers, Outlier Detection Methods

5.1 Outlier Detection Methods

4. Advanced Data Analysis Techniques

1. Measuring Data Similarity and Dissimilarity

2. Data Matrix versus Dissimilarity Matrix

3. Proximity Measures for Nominal Attributes

4. Proximity Measures for Binary Attributes

5. Dissimilarity of Numeric Data: Euclidean, Manhattan, and Minkowski Distances

6. Proximity Measures for Ordinal Attributes

7. Time Series Analysis, Anomaly Detection, and Recommender Systems

8. Advanced Statistical Techniques like Bayesian Statistics and Non-Parametric Methods

5. Data Preprocessing

1. Introduction

2. Data Preprocessing

3. Data Objects and Attribute Types

3.1 What is an Attribute?

3.2 Nominal

3.3 Binary

3.4 Ordinal Attributes

3.5 Numeric Attributes

3.6 Discrete versus Continuous Attributes

4. Data Munging/Wrangling Operations

4.1 Cleaning Data - Missing Values, Noisy Data

4.2 Problems with Data Content

4.3 Formatting Issues

5. Data Transformation – Rescaling, Normalizing, Binarizing, Standardizing, Label and One Hot Encoding

5.1 Rescaling

5.2 Normalizing

5.3 Binarizing

5.4 Standardizing

5.5 Label Encoding

5.6 One-Hot Encoding

6. Data Visualization

1. Introduction to Exploratory Data Analysis

2. Data visualization and Visual Encoding

3. Data Visualization Libraries    

4. Basic Data Visualization Tools

4.1 Histograms

4.2 Bar Charts/Graphs  

4.3 Scatter Plots

4.4 Line Charts

4.5 Area Plots

4.6 Pie Charts

4.7 Donut Charts

5. Specialized Data Visualization Tools

5.1 Box Plots

5.2 Bubble Plots

5.3 Heat map

5.4 Dendrogram

5.5 Venn Diagram

5.6 Treemap Chart

5.7 3D Scatter Plots

7. Machine Learning Fundamentals, Big Data and Distributed Computing

1. Introduction

1.1 Types of Learning

2. What is big data?

2.1 Types of Big Data

3. Big Data Technologies

4. Spark

5. Distributed Data Storage and Processing

8. Deep Learning and Natural Language Processing (NLP)

1. Introduction to Neural Network

2. Deep Learning

3. Deep Learning Frameworks like TensorFlow and PyTorch

4. Sentiment Analysis

5. Named Entity Recognition (NER)

6. Document Classification

9. Ethics and Responsible Data Science

1. Importance of Ethics in Data Science

Government norms for collecting and storing data under Legal and Regulatory Framework

2. Data Collection, Analysis and Model Deployment

2.1 Data Collection Tools

2.2 Model Deployment in Ethical and Responsible Data Science

3. Issues in Data Science

4. Challenges in Achieving Fairness

5. Ethical Implications of Fairness in Data Science

6. Strategies to Promote Fairness

10. Real-World Case Studies, Emerging Trends and Technologies

1. Real-world Application Case Studies

2. Emerging Trends and Technologies in Data Science