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
