The Data Science Course: Complete Data Science Bootcamp 2024

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 MN 1ST 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
• Appendix – Additional Python Tools
• Appendix – pandas Fundamentals
• Appendix – Working with Text Files in Python
• Bonus Lecture