Data Analytics
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.

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Numpy @CodeProgrammer.pdf
2.4 MB
🏷 Sections of the «NumPy» library
⬅️ From introductory to advanced


πŸ‘¨πŸ»β€πŸ’» This is a long-term project to learn Python and NumPy from scratch. The main task is to handle numerical #data and #arrays in #Python using NumPy, and many other libraries are also used.


✏️ This section shows a structured and complete path for learning #NumPy; but the code examples and exercises help to practically memorize the concepts.


⭕️ Introduction to NumPy
🟠 NumPy arrays
⭕️ Introduction to array features
🟠 Basic operations on arrays
⭕️ Functions for statistical and aggregative purposes
🟠 And...

https://t.me/CodeProgrammer β›ˆβš‘οΈ
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Mastering pandas%22.pdf
1.6 MB
🌟 A new and comprehensive book "Mastering pandas"

πŸ‘¨πŸ»β€πŸ’» If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.

⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.

🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:

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πŸ”Ή How to analyze real business data and draw conclusions,

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πŸ”Ή And improve the speed and accuracy of your analyses significantly.

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Numpy_Cheat_Sheet.pdf
4.8 MB
NumPy Cheat Sheet: Data Analysis in Python

This #Python cheat sheet is a quick reference for #NumPy beginners.

Learn more:
https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python

https://t.me/DataAnalyticsX
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tags: #python #deeplearning

➑ @codeprogrammer
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Data Science Roadmap.pdf
15.5 MB
🏷 Comprehensive Data Science Roadmap Notes

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https://t.me/CodeProgrammer
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Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory

Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.

If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?

In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.

Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory

https://t.me/CodeProgrammer 🌺
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Machine Learning in python.pdf
1 MB
Machine Learning in Python (Course Notes)

I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!

Here’s what you’ll learn:

πŸ”˜ Linear Regression - The foundation of predictive modeling

πŸ”˜ Logistic Regression - Predicting probabilities and classifications

πŸ”˜ Clustering (K-Means, Hierarchical) - Making sense of unstructured data

πŸ”˜ Overfitting vs. Underfitting - The balancing act every ML engineer must master

πŸ”˜ OLS, R-squared, F-test - Key metrics to evaluate your models

https://t.me/CodeProgrammer || Share 🌐 and Like πŸ‘
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