Data Science Methods and Algorithms [2024]

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Henrik Johansson

38:04:31

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1. Introduction to Data Science Methods and Algorithms

1. Introduction.mp4

24:16

2. Setup of the Anaconda Cloud Notebook.mp4

14:22

3. Download and installation of the Anaconda Distribution (optional).mp4

21:05

4. The Conda Package Management System (optional).mp4

35:00

2. Master Python for Data Handling

1. Overview of Python for Data Handling.mp4

28:25

2. Python Integer.mp4

14:12

3. Python Float.mp4

10:50

4. Python Strings I.mp4

15:03

5. Python Strings II Intermediate String Methods.mp4

22:36

6. Python Strings III DateTime Objects and Strings.mp4

27:33

7. Overview of Python Native Data Storage Structures.mp4

03:00

8. Python Set.mp4

15:20

9. Python Tuple.mp4

27:35

10. Python Dictionary.mp4

30:00

11. Python List.mp4

33:57

12. Overview of Python Data Transformers and Functions.mp4

03:06

13. Python While-loop.mp4

19:20

14. Python For-loop.mp4

17:02

15. Python Logic Operators and conditional code branching.mp4

31:00

16. Python Functions I Some theory.mp4

03:20

17. Python Functions II create your own functions.mp4

33:53

18. Python Object Oriented Programming I Some theory.mp4

14:10

19. Python Object Oriented Programming II create your own custom objects.mp4

39:20

20. Python Object Oriented Programming III Files and Tables.mp4

27:17

21.1 Text file.txt

21. Python Object Oriented Programming IV Recap and More.mp4

58:21

3. Master Pandas for Data Handling

1. Master Pandas for Data Handling Overview.mp4

11:21

2. Pandas theory and terminology.mp4

11:13

3. Creating a Pandas DataFrame from scratch.mp4

30:47

4. Pandas File Handling Overview.mp4

02:51

5. Pandas File Handling The .csv file format.mp4

18:48

6. Pandas File Handling The .xlsx file format.mp4

23:20

7. Pandas File Handling SQL-database files and Pandas DataFrame.mp4

15:08

8. Pandas Operations & Techniques Overview.mp4

03:11

9. Pandas Operations & Techniques Object Inspection.mp4

19:34

10. Pandas Operations & Techniques DataFrame Inspection.mp4

18:53

11. Pandas Operations & Techniques Column Selections.mp4

21:04

12. Pandas Operations & Techniques Row Selections.mp4

21:11

13. Pandas Operations & Techniques Conditional Selections.mp4

21:27

14. Pandas Operations & Techniques Scalers and Standardization.mp4

23:08

15. Pandas Operations & Techniques Concatenate DataFrames.mp4

29:21

16. Pandas Operations & Techniques Joining DataFrames.mp4

19:30

17. Pandas Operations & Techniques Merging DataFrames.mp4

30:48

18. Pandas Operations & Techniques Transpose & Pivot Functions.mp4

34:31

19. Pandas Data Preparation I Overview & workflow.mp4

05:23

20. Pandas Data Preparation II Edit DataFrame labels.mp4

20:16

21. Pandas Data Preparation III Duplicates.mp4

22:23

22. Pandas Data Preparation IV Missing Data & Imputation.mp4

54:35

23.1 geyser.csv

23.2 geyser.xlsx

23. Pandas Data Preparation V Data Binnings [Extra Video].mp4

46:32

24.2 insurance data.csv

24. Pandas Data Preparation VI Indicator Features [Extra Video].mp4

33:01

25. Pandas Data Description I Overview.mp4

02:35

26. Pandas Data Description II Sorting and Ranking.mp4

26:51

27. Pandas Data Description III Descriptive Statistics.mp4

31:40

28. Pandas Data Description IV Crosstabulations & Groupings.mp4

30:06

29. Pandas Data Visualization I Overview.mp4

03:35

30. Pandas Data Visualization II Histograms.mp4

42:34

31. Pandas Data Visualization III Boxplots.mp4

33:00

32. Pandas Data Visualization IV Scatterplots.mp4

40:00

33. Pandas Data Visualization V Pie Charts.mp4

45:40

34. Pandas Data Visualization VI Line plots.mp4

50:24

Files.zip

4. Regression, Prediction & Supervised Learning

1. Regression, Prediction, and Supervised Learning. Section Overview (I).mp4

10:15

2. The Traditional Simple Regression Model (II).mp4

35:08

3. The Traditional Simple Regression Model (III).mp4

38:00

4. Some practical and useful modelling concepts (IV).mp4

13:01

5. Some practical and useful modelling concepts (V).mp4

13:01

6. Linear Multiple Regression model (VI).mp4

57:00

7. Linear Multiple Regression model (VII).mp4

36:24

8. Multivariate Polynomial Multiple Regression models (VIII).mp4

10:13

9. Multivariate Polynomial Multiple Regression models (VIIII).mp4

01:06:05

10. Regression Regularization, Lasso and Ridge models (X).mp4

01:29:52

11. Decision Tree Regression models (XI).mp4

01:15:26

12. Random Forest Regression (XII).mp4

01:09:18

13. Voting Regression (XIII).mp4

48:00

Files.zip

5. Classification & Supervised Learning

1. Classification and Supervised Learning, overview.mp4

12:13

2. Logistic Regression Classifier.mp4

01:00:00

3. The Naive Bayes Classifier.mp4

16:55

4. The Decision Tree Classifier.mp4

01:06:40

5. The Random Forest Classifier.mp4

22:09

6. The Voting Classifier.mp4

25:00

Files.zip

6. Cluster Analysis and Unsupervised Learning

1. Cluster Analysis, an overview.mp4

22:16

2. K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms.mp4

26:47

3. Density-Based Spatial Clustering of Applications with Noise (DBSCAN).mp4

34:46

4. Four Hierarchical Clustering algorithms.mp4

21:18

Files.zip

Description

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

What You'll Learn?

Knowledge about Data Science methods, algorithms, theory, best practices, and tasks
Deep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidence
Detailed and deep Master knowledge of Regression, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised Learning
Hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python libraries
Advanced knowledge of A.I. prediction models and automatic model creation
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
Option: To use the Anaconda Distribution (for Windows, Mac, Linux)
Master the Python 3 programming language for Data Handling
Master Pandas 2 and 3 for Advanced Data Handling

Who is this for?

This course is for you, regardless if you are a beginner or an experienced Data Scientist

This course is for you, regardless if you have a Ph.D. or no education or experience at all

What You Need to Know?

The four ways of counting (+-*/)

Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended

Access to a computer with an internet connection

Programming experience is not needed and you will be taught everything you need

The course only uses costless software

Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included

More details

Description
Welcome to the course Data Science Methods and Algorithms with Pandas and Python!
Data Science is expanding and developing on a massive and global scale. Everywhere in society, there is a movement to implement and use Data Science Methods and Algorithms to develop and optimize all aspects of our lives, businesses, societies, governments, and states.
This course will teach you a large selection of Data Science methods and algorithms, which will give you an excellent foundation for Data Science jobs and studies. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist.

This is a five-in-one master class video course which will teach you to master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, Unsupervised Learning, Python 3, Pandas 2 + 3, and advanced Data Handling.
You will learn to master Regression, Prediction and supervised learning. This course has the most complete and fundamental master-level regression content packages on Udemy, with hands-on, useful practical theory, and also automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.
You will learn to master Classification and supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifiers Ensembles and Voting Classifier Ensembles.
You will learn to master Cluster Analysis and unsupervised learning. This part of the course is about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and some useful Machine Learning clustering algorithms ranging from hierarchical cluster models to density-based cluster models.
You will learn to master the Python 3 programming language, which is one of the most popular and useful programming languages in the world, and you will learn to use it for Data Handling.
You will learn to master the Pandas 2 and future 3 library and to use Pandas powerful Data Handling techniques for advanced Data Handling tasks. The Pandas library is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation tool, which is directly usable with the Python programming language, and combined creates the worldâ€™s most powerful coding environment for Data Handling and Advanced Data Handlingâ€¦

You will learn
Knowledge about Data Science methods, algorithms, theory, best practices, and tasks
Deep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidence
Detailed and deep Master knowledge of Regression, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised Learning
Hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python libraries
Advanced knowledge of A.I. prediction models and automatic model creation
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
Option: To use the Anaconda Distribution (for Windows, Mac, Linux)
Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages â€“ golden nuggets to improve your quality of work life
Master the Python 3 programming language for Data Handling
Master Pandas 2 and 3 for Advanced Data Handling
And much moreâ€¦
This course includes
a comprehensive and easy-to-follow teaching package for Mastering Python and Pandas for Data Handling, which makes anyone able to learn the course contents regardless of beforehand knowledge of programming, tabulation software, Python, Data Science, or Machine Learning
an easy-to-follow guide for using the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). You may learn to use Cloud Computing resources in this course
an easy-to-follow optional guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able to install a Python Data Science environment useful for this course or for any Data Science or coding task
content that will teach you many new things, regardless of if you are a beginner or an experienced Data Scientist
a large collection of unique content, and this course will teach you many new things that only can be learned from this course on Udemy
A course structure built on a proven and professional framework for learning.
A compact course structure and no killing time

This course is an excellent way to learn to master Regression, Prediction, Classification, Cluster analysis, Python, Pandas and Data Handling! These are the most important and useful tools for modeling, AI, and forecasting. Data Handling is the process of making data useful and usable for regression, prediction, classification, cluster analysis, and data analysis.
Most Data Scientists and Machine Learning Engineers spends about 80% of their working efforts and time on Data Handling tasks. Being good at Python, Pandas, and Data Handling are extremely useful and time-saving skills that functions as a force multiplier for productivity.

Is this course for you?
This course is for you, regardless if you are a beginner or an experienced Data Scientist
This course is for you, regardless if you have a Ph.D. or no education or experience at all
This course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Regression, Prediction, Python, Pandas, and Data Handling.

Course requirements
The four ways of counting (+-*/)
Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection
Programming experience is not needed and you will be taught everything you need
The course only uses costless software
Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included

Enroll now to receive 35+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!
Who this course is for:
This course is for you, regardless if you are a beginner or an experienced Data Scientist
This course is for you, regardless if you have a Ph.D. or no education or experience at all

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Henrik has a wide instructor/lecturer experience with more than 20 years in roles ranging from University teacher to sports coach to leadership roles in the private and public sectors.Henrik has experience teaching students from all walks of life, from the poor to royalty, and has taught students from nearly all educational backgrounds, from high school to Ph.D.s.Courses given by Henrik are intended to have unique content, and will teach you many new things.

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