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Python | Machine Learning | Coding | R
from SQL to pandas.pdf
1.3 MB
๐Ÿผ "Comparison Between SQL and pandas" โ€“ A Handy Reference Guide

โšก๏ธ 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 โ€“ and vice versa.

๐Ÿ”ธ 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.

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

๐Ÿ“• 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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๐Ÿ”ฅ How to become a data scientist in 2025?


1๏ธโƒฃ First of all, strengthen your foundation (math and statistics) .

โœ๏ธ 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:

โœ… Linear Algebra: Link

โœ… Calculus: Link

โœ… Statistics and Probability: Link

โž–โž–โž–โž–โž–โž–

2๏ธโƒฃ Then learn programming !

โœ๏ธ Without further ado, get started learning Python and SQL.

โœ… Python: Link

โœ… SQL language: Link

โœ… Data Structures and Algorithms: Link

โž–โž–โž–โž–โž–โž–

3๏ธโƒฃ Learn to clean and analyze data!

โœ๏ธ Data is always messy, and a data scientist must know how to organize it and extract insights from it.

โœ… Data cleansing: Link

โœ… Data visualization: Link

โž–โž–โž–โž–โž–โž–

4๏ธโƒฃ Learn machine learning !

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

โ—€๏ธ Supervised learning: regression, classification

โ—€๏ธ Unsupervised learning: clustering, dimensionality reduction

โ—€๏ธ Deep learning: neural networks, CNN, RNN

โœ… Stanford University CS229 course: Link

โž–โž–โž–โž–โž–โž–

5๏ธโƒฃ Get to know big data and cloud computing !

โœ๏ธ Large companies are looking for people who can work with large volumes of data.

โ—€๏ธ Big data tools (e.g. Hadoop, Spark, Dask)

โ—€๏ธ Cloud services (AWS, GCP, Azure)

โž–โž–โž–โž–โž–โž–

6๏ธโƒฃ Do a real project and build a portfolio !

โœ๏ธ Everything you've learned so far is worthless without a real project!

โ—€๏ธ Participate in Kaggle and work with real data.

โ—€๏ธ Do a project from scratch (from data collection to model deployment)

โ—€๏ธ Put your code on GitHub.

โœ… Open Source Data Science Projects: Link

โž–โž–โž–โž–โž–โž–

7๏ธโƒฃ It's time to learn MLOps and model deployment!

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

โ—€๏ธ Machine learning operationalization (monitoring, updating models)

โ—€๏ธ Model deployment tools: Flask, FastAPI, Docker

โœ… Stanford University MLOps Course: Link

โž–โž–โž–โž–โž–โž–

8๏ธโƒฃ Always stay up to date and network!

โœ๏ธ Follow research articles on arXiv and Google Scholar.

โœ… Papers with Code website: link

โœ… 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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๐Ÿ“ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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๐—ฌ๐—ผ๐˜‚๐—ฟ_๐——๐—ฎ๐˜๐—ฎ_๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ_๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„_๐—ฆ๐˜๐˜‚๐—ฑ๐˜†_๐—ฃ๐—น๐—ฎ๐—ป.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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๐Ÿ“ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Over the last year, several articles have been written to help candidates prepare for data science technical interviews. These resources cover a wide range of topics including machine learning, SQL, programming, statistics, and probability.

1๏ธโƒฃ Machine Learning (ML) Interview
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37

ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0

Crack the ML Coding Q&A
https://shorturl.at/CDW08

Deep Learning Interview Q&A
https://shorturl.at/lHPZ6

Top LLMs Interview Q&A
https://shorturl.at/wGRSZ

Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi

Part 2
https://rb.gy/hqgkbg

Part 3
https://rb.gy/5z87be

2๏ธโƒฃ SQL Interview Preparation
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1

SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH

Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9

Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n

How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA

Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE

SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw

6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q

3๏ธโƒฃ Programming Questions
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq

Part 2
https://lnkd.in/gATY4rTT

Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5

Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A

Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN

Python Interview Q&A
https://lnkd.in/gcaXc_JE

5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd

4๏ธโƒฃ Statistics
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5

Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8

Introduction to A/B Testing
https://lnkd.in/g35Jihw6

Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q

5๏ธโƒฃ Probability
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk

Part 2
https://lnkd.in/gQhXnKwJ

Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp

Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj

๐Ÿ”œ All links are available in the GitHub repository:
https://lnkd.in/djcgcKRT

#DataScience #InterviewPrep #MachineLearning #SQL #Python #Statistics #Probability #CodingInterview #AIBootcamp #DeepLearning #LLMs #ComputerVision #GitHubResources #CareerInDataScience


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๐Ÿ“ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Python | Machine Learning | Coding | R
๐Ÿ˜‰ A list of the best YouTube videos
โœ… To learn data science


1๏ธโƒฃ SQL language


โฌ…๏ธ Learning

๐Ÿ’ฐ 4-hour SQL course from zero to one hundred

๐Ÿ’ฐ Window functions tutorial

โฌ…๏ธ Projects

๐Ÿ“Ž Starting your first SQL project

๐Ÿ’ฐ Data cleansing project

๐Ÿ’ฐ Restaurant order analysis

โฌ…๏ธ Interview

๐Ÿ’ฐ How to crack the SQL interview?

โž–โž–โž–

2๏ธโƒฃ Python


โฌ…๏ธ Learning

๐Ÿ’ฐ 12-hour Python for Data Science course

โฌ…๏ธ Projects

๐Ÿ’ฐ Python project for beginners

๐Ÿ’ฐ Analyzing Corona Data with Python

โฌ…๏ธ Interview

๐Ÿ’ฐ Python interview golden tricks

๐Ÿ’ฐ Python Interview Questions

โž–โž–โž–

3๏ธโƒฃ Statistics and machine learning


โฌ…๏ธ Learning

๐Ÿ’ฐ 7-hour course in applied statistics

๐Ÿ’ฐ Machine Learning Training Playlist

โฌ…๏ธ Projects

๐Ÿ’ฐ Practical ML Project

โฌ…๏ธ Interview

๐Ÿ’ฐ ML Interview Questions and Answers

๐Ÿ’ฐ How to pass a statistics interview?

โž–โž–โž–

4๏ธโƒฃ Product and business case studies


โฌ…๏ธ Learning

๐Ÿ’ฐ Building strong product understanding

๐Ÿ’ฐ Product Metric Definition

โฌ…๏ธ Interview

๐Ÿ’ฐ Case Study Analysis Framework

๐Ÿ’ฐ How to shine in a business interview?

#DataScience #SQL #Python #MachineLearning #Statistics #BusinessAnalytics #ProductCaseStudies #DataScienceProjects #InterviewPrep #LearnDataScience #YouTubeLearning #CodingInterview #MLInterview #SQLProjects #PythonForDataScience



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