Python | Machine Learning | Coding

🐼

"Comparison Between SQL and pandas" – A Handy Reference Guide

⚡️ As a data scientist, I often found myself switching back and forth between SQL and pandas during technical interviews. I was confident answering questions in SQL but sometimes struggled to translate the same logic into pandas – and vice versa.

🔸 To bridge this gap, I created a concise booklet in the form of a comparison table. It maps SQL queries directly to their equivalent pandas implementations, making it easy to understand and switch between both tools.

⚡ This reference guide has become an essential part of my interview prep. Before any interview, I quickly review it to ensure I’m ready to tackle data manipulation tasks using either SQL or pandas, depending on what’s required.

📕 Whether you're preparing for interviews or just want to solidify your understanding of both tools, this comparison guide is a great way to stay sharp and efficient.

#DataScience #SQL #pandas #InterviewPrep #Python #DataAnalysis #CareerGrowth #TechTips #Analytics

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10.4K viewsedited 06:20

Python | Machine Learning | Coding | R

Numpy from basics to advanced.pdf

2.4 MB

📕

Mastering NumPy – From Basics to Advanced

NumPy is an essential library in the world of data science, widely recognized for its efficiency in numerical computations and data manipulation. This powerful tool simplifies complex operations with arrays, offering a faster and cleaner alternative to traditional Python lists and loops.

The "Mastering NumPy" booklet provides a comprehensive walkthrough—from array creation and indexing to mathematical/statistical operations and advanced topics like reshaping and stacking. All concepts are illustrated with clear, beginner-friendly examples, making it ideal for anyone aiming to boost their data handling skills.

#NumPy #Python #DataScience #MachineLearning #AI #BigData #DeepLearning #DataAnalysis

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Python | Machine Learning | Coding | R

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🚀 DataCamp has officially partnered with Polars**—a cutting-edge DataFrame library designed for speed and efficiency!

To mark this exciting collaboration, **DataCamp is offering free access to its brand-new course *“Introduction to Polars”* for the next 90 days. 🎉

This course is a great opportunity for learners and professionals alike to master data cleaning, transformation, and analysis with Polars' high-performance engine, lazy execution, and powerful groupby operations.

Unlock the full potential of data workflows and explore how Polars can supercharge large-scale data processing.

🔗 Start learning now:
https://www.datacamp.com/courses/introduction-to-polars

#DataScience #Polars #Python #BigData #DataEngineering #MachineLearning #DataAnalytics #OpenSource #DataCamp #FreeCourse #LearnDataScience

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Python | Machine Learning | Coding | R

🚀

Master Python with Ease!

I've just compiled a set of clean and powerful Python Cheat Sheets to help beginners and intermediates speed up their coding workflow.

Whether you're brushing up on the basics or diving into data science, these sheets will save you time and boost your productivity.

📌 Topics Covered:
Python Basics
Jupyter Notebook Tips
Importing Libraries
NumPy Essentials
Pandas Overview

Perfect for students, developers, and anyone looking to keep essential Python knowledge at their fingertips.

#Python #CheatSheets #PythonTips #DataScience #JupyterNotebook #NumPy #Pandas #MachineLearning #AI #CodingTips #PythonForBeginners

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Python | Machine Learning | Coding | R

🔥 How to become a data scientist in 2025?

1️⃣

First of all, strengthen your foundation (math and statistics) .

✏️ If you don't know math, you'll run into trouble wherever you go. Every model you build, every analysis you do, there's a world of math behind it. You need to know these things well:

✅ Linear Algebra: Link

✅ Calculus: Link

✅ Statistics and Probability: Link

➖

2️⃣

Then learn programming !

✏️ Without further ado, get started learning Python and SQL.

✅

Python: Link

✅ SQL language: Link

✅ Data Structures and Algorithms: Link

➖

3️⃣

Learn to clean and analyze data!

✏️ Data is always messy, and a data scientist must know how to organize it and extract insights from it.

✅ Data cleansing: Link

✅ Data visualization: Link

➖

4️⃣

Learn machine learning !

✏️ Once you've mastered the basic skills, it's time to enter the world of machine learning. Here's what you need to know:

◀️

Supervised learning: regression, classification

◀️

Unsupervised learning: clustering, dimensionality reduction

◀️

Deep learning: neural networks, CNN, RNN

✅

Stanford University CS229 course: Link

➖

5️⃣

Get to know big data and cloud computing !

✏️ Large companies are looking for people who can work with large volumes of data.

◀️ Big data tools (e.g. Hadoop, Spark, Dask)

◀️ Cloud services (AWS, GCP, Azure)

➖

6️⃣

Do a real project and build a portfolio !

✏️ Everything you've learned so far is worthless without a real project!

◀️ Participate in Kaggle and work with real data.

◀️ Do a project from scratch (from data collection to model deployment)

◀️ Put your code on GitHub.

✅ Open Source Data Science Projects: Link

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7️⃣

It's time to learn MLOps and model deployment!

✏️ Many people just build models but don't know how to deploy them. But companies want someone who can put the model into action!

◀️ Machine learning operationalization (monitoring, updating models)

◀️ Model deployment tools: Flask, FastAPI, Docker

✅ Stanford University MLOps Course: Link

➖

8️⃣

Always stay up to date and network!

✏️ Follow research articles on arXiv and Google Scholar.

✅ Papers with Code website: link

✅ AI Research at Google website: link

#DataScience #HowToBecomeADataScientist #ML2025 #Python #SQL #MachineLearning #MathForDataScience #BigData #MLOps #DeepLearning #AIResearch #DataVisualization #PortfolioProjects #CloudComputing #DSCareerPath

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Python | Machine Learning | Coding | R

𝗬𝗼𝘂𝗿_𝗗𝗮𝘁𝗮_𝗦𝗰𝗶𝗲𝗻𝗰𝗲_𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄_𝗦𝘁𝘂𝗱𝘆_𝗣𝗹𝗮𝗻.pdf

7.7 MB

1. Master the fundamentals of Statistics

Understand probability, distributions, and hypothesis testing

Differentiate between descriptive vs inferential statistics

Learn various sampling techniques

2. Get hands-on with Python & SQL

Work with data structures, pandas, numpy, and matplotlib

Practice writing optimized SQL queries

Master joins, filters, groupings, and window functions

3. Build real-world projects

Construct end-to-end data pipelines

Develop predictive models with machine learning

Create business-focused dashboards

4. Practice case study interviews

Learn to break down ambiguous business problems

Ask clarifying questions to gather requirements

Think aloud and structure your answers logically

5. Mock interviews with feedback

Use platforms like Pramp or connect with peers

Record and review your answers for improvement

Gather feedback on your explanation and presence

6. Revise machine learning concepts

Understand supervised vs unsupervised learning

Grasp overfitting, underfitting, and bias-variance tradeoff

Know how to evaluate models (precision, recall, F1-score, AUC, etc.)

7. Brush up on system design (if applicable)

Learn how to design scalable data pipelines

Compare real-time vs batch processing

Familiarize with tools: Apache Spark, Kafka, Airflow

8. Strengthen storytelling with data

Apply the STAR method in behavioral questions

Simplify complex technical topics

Emphasize business impact and insight-driven decisions

9. Customize your resume and portfolio

Tailor your resume for each job role

Include links to projects or GitHub profiles

Match your skills to job descriptions

10. Stay consistent and track progress

Set clear weekly goals

Monitor covered topics and completed tasks

Reflect regularly and adapt your plan as needed

#DataScience #InterviewPrep #MLInterviews #DataEngineering #SQL #Python #Statistics #MachineLearning #DataStorytelling #SystemDesign #CareerGrowth #DataScienceRoadmap #PortfolioBuilding #MockInterviews #JobHuntingTips

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Python | Machine Learning | Coding | R

🐍📰 This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph

Link: https://realpython.com/langgraph-python/

#LangGraph #Python #LLMWorkflows #AIAgents #RealPython #PythonTutorials #LargeLanguageModels #AIAgents #WorkflowAutomation #PythonForA

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5.54K viewsedited 08:00

Python | Machine Learning | Coding | R

New to Pandas?

Here's a cheat sheet you can download (2025)

#Pandas #Python #DataAnalysis #PandasCheatSheet #PythonForDataScience #LearnPandas #DataScienceTools #PythonLibraries #FreeResources #DataManipulation

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