Data Science Methods and Algorithms [2024]

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Learn Data Science Methods and Algorithms with Pandas and Python [2024]

1. Introduction to Data Science Methods and Algorithms

1. Introduction

2. Setup of the Anaconda Cloud Notebook

3. Download and installation of the Anaconda Distribution (optional)

4. The Conda Package Management System (optional)

2. Master Python for Data Handling

1. Overview of Python for Data Handling

2. Python Integer

3. Python Float

4. Python Strings I

5. Python Strings II Intermediate String Methods

6. Python Strings III DateTime Objects and Strings

7. Overview of Python Native Data Storage Structures

8. Python Set

9. Python Tuple

10. Python Dictionary

11. Python List

12. Overview of Python Data Transformers and Functions

13. Python While-loop

14. Python For-loop

15. Python Logic Operators and conditional code branching

16. Python Functions I Some theory

17. Python Functions II create your own functions

18. Python Object Oriented Programming I Some theory

19. Python Object Oriented Programming II create your own custom objects

20. Python Object Oriented Programming III Files and Tables

21.1 Text file.txt

21. Python Object Oriented Programming IV Recap and More

3. Master Pandas for Data Handling

1. Master Pandas for Data Handling Overview

2. Pandas theory and terminology

3. Creating a Pandas DataFrame from scratch

4. Pandas File Handling Overview

5. Pandas File Handling The .csv file format

6. Pandas File Handling The .xlsx file format

7. Pandas File Handling SQL-database files and Pandas DataFrame

8. Pandas Operations & Techniques Overview

9. Pandas Operations & Techniques Object Inspection

10. Pandas Operations & Techniques DataFrame Inspection

11. Pandas Operations & Techniques Column Selections

12. Pandas Operations & Techniques Row Selections

13. Pandas Operations & Techniques Conditional Selections

14. Pandas Operations & Techniques Scalers and Standardization

15. Pandas Operations & Techniques Concatenate DataFrames

16. Pandas Operations & Techniques Joining DataFrames

17. Pandas Operations & Techniques Merging DataFrames

18. Pandas Operations & Techniques Transpose & Pivot Functions

19. Pandas Data Preparation I Overview & workflow

20. Pandas Data Preparation II Edit DataFrame labels

21. Pandas Data Preparation III Duplicates

22. Pandas Data Preparation IV Missing Data & Imputation

23.1 geyser.csv

23.2 geyser.xlsx

23. Pandas Data Preparation V Data Binnings [Extra Video]

24.2 insurance data.csv

24. Pandas Data Preparation VI Indicator Features [Extra Video]

25. Pandas Data Description I Overview

26. Pandas Data Description II Sorting and Ranking

27. Pandas Data Description III Descriptive Statistics

28. Pandas Data Description IV Crosstabulations & Groupings

29. Pandas Data Visualization I Overview

30. Pandas Data Visualization II Histograms

31. Pandas Data Visualization III Boxplots

32. Pandas Data Visualization IV Scatterplots

33. Pandas Data Visualization V Pie Charts

34. Pandas Data Visualization VI Line plots

Files.zip

4. Regression, Prediction & Supervised Learning

1. Regression, Prediction, and Supervised Learning. Section Overview (I)

2. The Traditional Simple Regression Model (II)

3. The Traditional Simple Regression Model (III)

4. Some practical and useful modelling concepts (IV)

5. Some practical and useful modelling concepts (V)

6. Linear Multiple Regression model (VI)

7. Linear Multiple Regression model (VII)

8. Multivariate Polynomial Multiple Regression models (VIII)

9. Multivariate Polynomial Multiple Regression models (VIIII)

10. Regression Regularization, Lasso and Ridge models (X)

11. Decision Tree Regression models (XI)

12. Random Forest Regression (XII)

13. Voting Regression (XIII)

Files.zip

5. Classification & Supervised Learning

1. Classification and Supervised Learning, overview

2. Logistic Regression Classifier

3. The Naive Bayes Classifier

4. The Decision Tree Classifier

5. The Random Forest Classifier

6. The Voting Classifier

Files.zip

6. Cluster Analysis and Unsupervised Learning

1. Cluster Analysis, an overview

2. K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms

3. Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

4. Four Hierarchical Clustering algorithms

Files.zip

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تولید کننده: Udemy