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A comprehensive collection of important data science topics, including articles, videos, and sample code, that can serve as a valuable resource for learning and staying current on the latest techniques and developments in the field

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Uncovering-Insights: A Daily Journey Through the World of Data Science

A comprehensive collection of important data science topics, including articles, videos, and sample code, that can serve as a valuable resource for learning and staying current on the latest techniques and developments in the field

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git clone https://github.com/ghimiresunil/Uncovering-Insights-A-Daily-Journey-Through-the-World-of-Data-Science.git

Table of Contents

Python

Title Notebook
Enforce Type Hints in Python 🔗
pydash: The kitchen sink of Python utility libraries for doing "stuff" in a functional way 🔗
Pipe: Write clean python Code 🔗
How to Use Zip to Manipulate a List of Tuples 🔗
Python Tricks for Keeping Track of Your Data 🔗
The Right Way to Roll Out Library Updates in Python 🔗
F-strings offer greater versatility than commonly perceived 🔗
Speed Up Your Python Programs with a Simple Change numba 🔗
Make Dot Notation More Powerful in Python 🔗
An Elegant Way To Perform Shutdown Tasks in Python 🔗
Class Methods: What and when to use? 🔗
Hide Attributes While Printing A Dataclass Object 🔗
Simplify Your Functions With Partial Functions 🔗
DotMap: A better alternative to python dictionary 🔗
Prevent Wild Imports With all in Python 🔗
Integer Comparasion between 256 and 257 🔗
Make a Class Object Behave Like a Function 🔗
Feature of Pickle 🔗
Specify Loops and Runs In %%timeit 🔗
Don't Use time.time() To Measure Execution Time 🔗
Use Slotted Class to Improve Your Python Code 🔗
Using Dictionaries In Place of If-conditions 🔗
Run Python Project Directory as a Script 🔗
Import your Python Package as Module 🔗
How to Use Lambda for Efficient Python Code 🔗
Boost Your Efficiency With Specialized Dictionary Implementations in Python 🔗
Tricks to Read, Create, and Run Multiple Files Automatically 🔗
Practices to Make Your Python Functions More Readable 🔗
Send Email using Python 🔗

Pandas

Title Notebook
Pandas vs Polars — Run-time and Memory Comparison 🔗
Avoid This Costly Mistake When Indexing A DataFrame 🔗
70x Faster Pandas By Changing Just One Line of Code 🔗
Exclude the Outliers in Pandas DataFrame 🔗
Supercharge value_counts() Method in Pandas With Sidetable 🔗
Don't Create Conditional Columns in Pandas with Apply 🔗
Write Your Own Flavor Of Pandas 🔗
Create Pandas DataFrame from Dataclass 🔗
Introducing FugueSQL — SQL for Pandas, Spark, and Dask DataFrames 🔗
Alter the Datatype of Multiple Columns at Once 🔗
How to Read Multiple CSV Files Efficiently 🔗
Stop Using The Describe Method in Pandas. Instead, use Skimpy 🔗
Create Pivot Tables, Aggregations and Plots Without Any Code 🔗
Display Progress Bar With Apply() in Pandas 🔗
Python OOP: Explanation and uses of magic methods 🔗

Testing

Title Notebook
DeepDiff — Recursively Find and Ignore Trivial Differences Using Python 🔗

Math Tools

Title Notebook
SymPy: Symbolic Computation in Python 🔗

Statistics

Title Notebook
When using summary statistics, be caution before making any conclusions 🔗

Machine Learning

Title Resource
The Limitations Of Elbow Curve And What You Should Replace It With 🔗
Try This If Your Linear Regression Model is Underperforming 🔗
Theil-Sen Regression: The Robust Twin of Linear Regression 🔗
The Limitations of PCA which many Folks Often Ignore 🔗
How to encode categorical features with many categories? 🔗

Natural Language Processing

Title Notebook
Tokenize Tweets in Python 🔗

ML Journey from Beginning to Advance

Title Resource
Implementation of Machine Learning Algorithm from Scratch 🔗

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A comprehensive collection of important data science topics, including articles, videos, and sample code, that can serve as a valuable resource for learning and staying current on the latest techniques and developments in the field

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