Complete Data Science Course
Contents
1. Part 1 Introduction
2. The Field of Data Science - The Various Data Science Disciplines
3. The Field of Data Science - Connecting the Data Science Disciplines
4. The Field of Data Science - The Benefits of Each Discipline
5. The Field of Data Science - Popular Data Science Techniques
6. The Field of Data Science - Popular Data Science Tools
7. The Field of Data Science - Careers in Data Science
8. The Field of Data Science - Debunking Common Misconceptions
9. Part 2 Probability
10. Combinatorics
11. Bayesian Inference
12. Probability Distributions
13. Probability in Other Fields
14. Part 3 Statistics
15. Statistics - Descriptive Statistics
16. Statistics - Practical Example Descriptive Statistics
17. Statistics - Inferential Statistics Fundamentals
18. Statistics - Inferential Statistics Confidence Intervals
19. Statistics - Practical Example Inferential Statistics
20. Statistics - Hypothesis Testing
21. Statistics - Practical Example Hypothesis Testing
22. Part 4 Introduction to Python
23. Python - Variables and Data Types
24. Python - Basic Python Syntax
25. Python - Other Python Operators
29. Python - Iterations
30. Python - Advanced Python Tools
31. Part 5 Advanced Statistical Methods in Python
32. Advanced Statistical Methods - Linear regression with StatsModels
33. Advanced Statistical Methods - Multiple Linear Regression with StatsModels
34. Advanced Statistical Methods - Linear Regression with sklearn
35. Advanced Statistical Methods - Practical Example Linear Regression
36. Advanced Statistical Methods - Logistic Regression
37. Advanced Statistical Methods - Cluster Analysis
38. Advanced Statistical Methods - K-Means Clustering
39. Advanced Statistical Methods - Other Types of Clustering
40. Part 6 Mathematics
41. Part 7 Deep Learning
42. Deep Learning - Introduction to Neural Networks
43. Deep Learning - How to Build a Neural Network from Scratch with NumPy
44. Deep Learning - TensorFlow 2.0 Introduction
45. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks
46. Deep Learning - Overfitting
47. Deep Learning - Initialization
48. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules
49. Deep Learning - Preprocessing
50. Deep Learning - Classifying on the MNIST Dataset
51. Deep Learning - Business Case Example
52. Deep Learning - Conclusion
53. Appendix Deep Learning - TensorFlow 1 Introduction
54. Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset
55. Appendix Deep Learning - TensorFlow 1 Business Case
56. Software Integration
57. Case Study - What's Next in the Course
58. Case Study - Preprocessing the 'Absenteeism_data'
59. Case Study - Applying Machine Learning to Create the 'absenteeism_module'
60. Case Study - Loading the 'absenteeism_module'
61. Case Study - Analyzing the Predicted Outputs in Tableau
You'll get a comprehensive Data Science course from beginner to advanced level..