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