Python | Machine Learning | Coding

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A complete package for success in data science and machine learning interviews!

👩🏻‍💻 I found a GitHub repo full of resources you need to succeed in Data Science and Machine Learning interviews!

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What do you find in it?

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Practical cheat sheets: Important tips gathered in one place.

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Cool books: resources worth your time!

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Frequently Asked Interview Questions: Topics that are asked in most interviews and that you are likely to encounter.

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Portfolio projects: To make your resume stronger.

✅ In short, a complete package for preparing for data science interviews, without the confusion!

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Here is the repost link: 👇

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Cracking the data science interview

#DataScience #MachineLearning #InterviewPrep #CareerGrowth #TechResources #GitHubRepo #CheatSheets #PortfolioProjects #InterviewQuestions #DataScientists #SuccessTips #TechCareer #CodingLife #LearnAndGrow #InterviewReady

https://t.me/CodeProgrammer

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Python | Machine Learning | Coding | R

80 Python Interview Questions.pdf

410.4 KB

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80 Python Interview Questions with Answers & Code!

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Why this resource?
- Covers frequently asked questions in Python interviews

📄 Each question comes with detailed answers and ready-to-use code snippets, making it perfect for beginners and experienced developers alike. Whether you're preparing for a job interview or leveling up your Python skills, this guide has you covered! 👀

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Don’t miss out! Save this, share it, and start preparing today! 💼

#Python #DataScience #Programming #InterviewPrep #Coding #PythonInterview #TechInterview #DataScientist #PythonProgramming #LearnPython #CodeNewbie #CareerGrowth #TechJobs #PythonCode #PythonTips

https://t.me/CodeProgrammer

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Python | Machine Learning | Coding | R

Top 100+ questions%0A %22Google Data Science Interview%22.pdf

16.7 MB

💯 Top 100+ Google Data Science Interview Questions

🌟 Essential Prep Guide for Aspiring Candidates

Google is known for its rigorous data science interview process, which typically follows a hybrid format. Candidates are expected to demonstrate strong programming skills, solid knowledge in statistics and machine learning, and a keen ability to approach problems from a product-oriented perspective.

To succeed, one must be proficient in several critical areas: statistics and probability, SQL and Python programming, product sense, and case study-based analytics.

This curated list features over 100 of the most commonly asked and important questions in Google data science interviews. It serves as a comprehensive resource to help candidates prepare effectively and confidently for the challenge ahead.

#DataScience #GoogleInterview #InterviewPrep #MachineLearning #SQL #Statistics #ProductAnalytics #Python #CareerGrowth

https://t.me/addlist/0f6vfFbEMdAwODBk

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Python | Machine Learning | Coding | R

@CodeProgrammer Matplotlib.pdf

4.3 MB

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Mastering Matplotlib in 20 Days

The Complete Visual Guide for Data Enthusiasts

Matplotlib is a powerful Python library for data visualization, essential not only for acing job interviews but also for building a solid foundation in analytical thinking and data storytelling.

This step-by-step tutorial guide walks learners through everything from the basics to advanced techniques in Matplotlib. It also includes a curated collection of the most frequently asked Matplotlib-related interview questions, making it an ideal resource for both beginners and experienced professionals.

#Matplotlib #DataVisualization #Python #DataScience #InterviewPrep #Analytics #TechCareer #LearnToCode

https://t.me/addlist/0f6vfFbEMdAwODBk

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Python | Machine Learning | Coding | R

from SQL to pandas.pdf

1.3 MB

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"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

✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

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9.79K viewsedited 06:20

Python | Machine Learning | Coding | R

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Preparing for Data Science Interviews

👨🏻‍💻 I've been collecting a variety of data science interview questions for different positions for a few weeks now.

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I covered everything, from basic to advanced:

Common Data Science and ML Questions (34 questions)

Regression (22 questions)

Classification (39 questions)

SVM algorithms, decision tree

Simple Bayes and statistical discussions and...

🚨 This list is regularly updated and categorized so that you can easily prepare for the interview step by step.👇

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Interview Questions
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GitHub-Repos

#DataScience #InterviewPrep #MLInterviews #DataScientist #MachineLearning #TechCareers #DSInterviewQuestions #GitHubResources #CareerInDataScience #CodingInterview

✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

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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

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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.

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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

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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

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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

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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

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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

🔜 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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