Data Science Machine Learning Data Analysis
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This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning

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Free Certification Courses to Learn Data Analytics in 2025:

1. Python
πŸ”— https://imp.i384100.net/5gmXXo

2. SQL
πŸ”— https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql

3. Statistics and R
πŸ”— https://edx.org/learn/r-programming/harvard-university-statistics-and-r

4. Data Science: R Basics
πŸ”—https://edx.org/learn/r-programming/harvard-university-data-science-r-basics

5. Excel and PowerBI
πŸ”— https://learn.microsoft.com/en-gb/training/paths/modern-analytics/

6. Data Science: Visualization
πŸ”—https://edx.org/learn/data-visualization/harvard-university-data-science-visualization

7. Data Science: Machine Learning
πŸ”—https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning

8. R
πŸ”—https://imp.i384100.net/rQqomy

9. Tableau
πŸ”—https://imp.i384100.net/MmW9b3

10. PowerBI
πŸ”— https://lnkd.in/dpmnthEA

11. Data Science: Productivity Tools
πŸ”— https://lnkd.in/dGhPYg6N

12. Data Science: Probability
πŸ”—https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science

13. Mathematics
πŸ”—https://matlabacademy.mathworks.com

14. Statistics
πŸ”— https://lnkd.in/df6qksMB

15. Data Visualization
πŸ”—https://imp.i384100.net/k0X6vx

16. Machine Learning
πŸ”— https://imp.i384100.net/nLbkN9

17. Deep Learning
πŸ”— https://imp.i384100.net/R5aPOR

18. Data Science: Linear Regression
πŸ”—https://pll.harvard.edu/course/data-science-linear-regression/2023-10

19. Data Science: Wrangling
πŸ”—https://edx.org/learn/data-science/harvard-university-data-science-wrangling

20. Linear Algebra
πŸ”— https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

21. Probability
πŸ”— https://pll.harvard.edu/course/data-science-probability

22. Introduction to Linear Models and Matrix Algebra
πŸ”—https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra

23. Data Science: Capstone
πŸ”— https://edx.org/learn/data-science/harvard-university-data-science-capstone

24. Data Analysis
πŸ”— https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis

25. IBM Data Science Professional Certificate
https://imp.i384100.net/9gxbbY

26. Neural Networks and Deep Learning
https://imp.i384100.net/DKrLn2

27. Supervised Machine Learning: Regression and Classification
https://imp.i384100.net/g1KJEA

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience
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πŸƒ Stem-Leaf Plot - An intelligent visualization!

It's a simple and effective way to visualize and compare datasets.

πŸ“Š Imagine we have two datasets: Set 1 (7, 12, 14, 17, 19, 23, 25) and Set 2 (3, 11, 16, 18, 20, 21, 24). We'll use a stem-leaf plot to compare them.

🌿 First, let's create the 'stem' which represents the tens place (0, 1, 2) and the 'leaf' represents the ones place (0-9).

πŸ” By comparing the plots, we can see that Dataset 1 has higher values in the tens place, while Dataset 2 has a more uniform distribution.

🎯 Stem-leaf plots are great for small datasets and provide a clear picture of data distribution. The special thing about a stem-and-leaf diagram is that the original data can be read out of the graphical representation.


Give it a try next time you need to compare datasets!

✍🏽 Have you used stem-leaf plots before?

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience

https://t.iss.one/CodeProgrammer ✈️
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πŸ—‚ 10 β€œReal Data Science Portfolio” Examples

πŸ“ I've brought you 10 of the best portfolios from data science professionals, each of whom has followed a unique path! Check out these 10 and get inspired to build a strong portfolio of your own!πŸ‘‡
1️⃣ Ken Jee Portfolio | Data Scientist
▢️ Field: Sports data analysis
πŸ‘€ Link: Portfolio

2️⃣ Yassine Alouini's Portfolio | Kegel Master
▢️ Domain: Machine Learning and Kegel Competitions
πŸ‘€ Link: Portfolio

3️⃣ Tatman Portfolio | Data Scientist
▢️ Domain: Natural Language Processing (NLP)
πŸ‘€ Link: Portfolio

4️⃣ Robinson Portfolio | Data Scientist
▢️ Field: Statistical analysis and R programming
πŸ‘€ Link: Portfolio

5️⃣ Siraj Raval's Portfolio | AI Instructor
▢️ Field: Machine Learning and Artificial Intelligence
πŸ‘€ Link: Portfolio

6️⃣ Julia Silge's Portfolio | Data Scientist
▢️ Domain: Organized data and data visualization
πŸ‘€ Link: Portfolio

7️⃣ Mueller Portfolio | Developer Scikit-Learn
▢️ Field: Machine learning and open source projects
πŸ‘€ Link: Portfolio

8️⃣ Wickham Portfolio | Data Scientist
▢️ Area: R programming and data visualization
πŸ‘€ Link: Portfolio

9️⃣ Portfolio of FranΓ§ois Puget | Kegel Master
▢️ Domain: Advanced Machine Learning Techniques
πŸ‘€ Link: Portfolio

πŸ”Ÿ Emily's Portfolio | Data Analyst at Disney
▢️ Domain: Data visualization and storytelling
πŸ‘€ Link: Portfolio

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming

https://t.iss.one/CodeProgrammer 🧠
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πŸ”₯ MIT has updated its famous course 6.S191: Introduction to Deep Learning.

The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries.

The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process.

The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available

.

All slides, #code and additional materials can be found at the link provided.

πŸ“Œ Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.iss.one/CodeProgrammer βœ…
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Numpy @CodeProgrammer.pdf
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πŸ³οΈβ€πŸŒˆ "NumPy Library" Tutorial

πŸ‘¨πŸ»β€πŸ’» For the past few days, I've been busy preparing this comprehensive tutorial on the NumPy library for data science, trying to cover all the tips and tricks of this library.

βœ… Why is this booklet different? Because it is not written based on just theoretical concepts, but is the result of my own experiences and learning. It has real and practical examples that will help you better understand #NumPy concepts and use them in your projects.πŸ’―

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.iss.one/CodeProgrammer βœ…
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πŸ‘©β€πŸ’» Prompt Engineering: A Practical Example

This real-world project tutorial covers zero-shot and few-shot prompting, delimiters, numbered steps, role prompts, chain-of-thought prompting, and more. Improve your LLM-assisted projects today.

Link: https://realpython.com/practical-prompt-engineering/

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.iss.one/CodeProgrammer βœ…
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πŸ”– The book that paved the way for me to "data science"!

πŸ‘¨πŸ»β€πŸ’» "Where do I start now?" This was the first and biggest question I faced when I started my Data Science learning journey!

βͺ I was really overwhelmed by the large number of scattered sources, long courses, and specialized books full of heavy terminology. I didn't know how to start and move forward in this direction...

βœ”οΈ But the book Intro to Data Science with Python changed everything for me and gave me a new perspective!

✏️ This book is a complete guide to starting from scratch and is great for both beginners and professionals in this field!! From coding with Python to working with data, visualization, and even AI tools, it explains everything in the simplest and most practical way possible.

πŸ’Έ A great start for anyone looking to learn data science with Python!πŸ‘‡

β”Œ πŸ³οΈβ€πŸŒˆ Intro to Data Science with Python
β”œ
πŸ“„ E-book
β””
🐱 GitHub-Repos

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.iss.one/CodeProgrammer βœ…
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Pandas Introduction to Advanced.pdf
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πŸ“„ "Pandas Introduction to Advanced" booklet

πŸ‘¨πŸ»β€πŸ’» You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need.

βœ”οΈ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.iss.one/CodeProgrammer βœ…
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πŸ”— Machine Learning from Scratch by Danny Friedman

This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one.

This book will be most helpful for those with practice in basic modeling. It does not review best practicesβ€”such as feature engineering or balancing response variablesβ€”or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.


https://dafriedman97.github.io/mlbook/content/introduction.html

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.iss.one/CodeProgrammer βœ…
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