The Complete Data Science 2019 Video Course on 4 DVDs

469  Lessons

Duration  27:45 Hours

Course content

Part 1: Introduction

The Field of Data Science - The Various Data Science Disciplines

The Field of Data Science - Connecting the Data Science Disciplines

The Field of Data Science - The Benefits of Each Discipline

The Field of Data Science - Popular Data Science Techniques

The Field of Data Science - Popular Data Science Tools

The Field of Data Science - Careers in Data Science

The Field of Data Science - Debunking Common Misconceptions

Part 2: Probability

Probability - Combinatorics

Probability - Bayesian Inference

Probability - Distributions

Probability - Probability in Other Fields

Part 3: Statistics

Statistics - Descriptive Statistics

Statistics - Practical Example: Descriptive Statistics

Statistics - Inferential Statistics Fundamentals

Statistics - Inferential Statistics: Confidence Intervals

Statistics - Practical Example: Inferential Statistics

Statistics - Hypothesis Testing

Statistics - Practical Example: Hypothesis Testing

Part 4: Introduction to Python

Python - Variables and Data Types

Python - Basic Python Syntax

Python - Other Python Operators

Python - Conditional Statements

Python - Python Functions

Python - Sequences

Python - Iterations

Python - Advanced Python Tools

Part 5: Advanced Statistical Methods in Python

Advanced Statistical Methods - Linear regression with StatsModels

Advanced Statistical Methods - Multiple Linear Regression with StatsModels

Advanced Statistical Methods - Linear Regression with sklearn

Advanced Statistical Methods - Practical Example: Linear Regression

Advanced Statistical Methods - Logistic Regression

Advanced Statistical Methods - Cluster Analysis

Advanced Statistical Methods - K-Means Clustering

Advanced Statistical Methods - Other Types of Clustering

Part 6: Mathematics

Part 7: Deep Learning

Deep Learning - Introduction to Neural Networks

Deep Learning - How to Build a Neural Network from Scratch with NumPy

Deep Learning - TensorFlow 2.0: Introduction

Deep Learning - Digging Deeper into NNs: Introducing Deep Neural Networks

Deep Learning - Overfitting

Deep Learning - Initialization

Deep Learning - Digging into Gradient Descent and Learning Rate Schedules

Deep Learning - Preprocessing

Deep Learning - Classifying on the MNIST Dataset

Deep Learning - Business Case Example

Deep Learning - Conclusion

Appendix: Deep Learning - TensorFlow 1: Introduction

Appendix: Deep Learning - TensorFlow 1: Classifying on the MNIST Dataset

Appendix: Deep Learning - TensorFlow 1: Business Case

Software Integration

Case Study - What's Next in the Course?

Case Study - Preprocessing the 'Absenteeism_data'

Case Study - Applying Machine Learning to Create the 'absenteeism_module'

Case Study - Loading the 'absenteeism_module'

Case Study - Analyzing the Predicted Outputs in Tableau