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๐ฉ๐ปโ๐ป I found a GitHub repo full of resources you need to succeed in Data Science and Machine Learning interviews!
#DataScience #MachineLearning #InterviewPrep #CareerGrowth #TechResources #GitHubRepo #CheatSheets #PortfolioProjects #InterviewQuestions #DataScientists #SuccessTips #TechCareer #CodingLife #LearnAndGrow #InterviewReady
https://t.me/CodeProgrammer
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80 Python Interview Questions.pdf
410.4 KB
- Covers frequently asked questions in Python interviews
#Python #DataScience #Programming #InterviewPrep #Coding #PythonInterview #TechInterview #DataScientist #PythonProgramming #LearnPython #CodeNewbie #CareerGrowth #TechJobs #PythonCode #PythonTips
https://t.me/CodeProgrammer
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Top 100+ questions%0A %22Google Data Science Interview%22.pdf
16.7 MB
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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@CodeProgrammer Matplotlib.pdf
4.3 MB
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๏ปฟ
https://t.me/addlist/0f6vfFbEMdAwODBk
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from SQL to pandas.pdf
1.3 MB
#DataScience #SQL #pandas #InterviewPrep #Python #DataAnalysis #CareerGrowth #TechTips #Analytics
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๐จ๐ปโ๐ป I've been collecting a variety of data science interview questions for different positions for a few weeks now.
Common Data Science and ML Questions (34 questions)
Regression (22 questions)
Classification (39 questions)
SVM algorithms, decision tree
Simple Bayes and statistical discussions and...
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#DataScience #InterviewPrep #MLInterviews #DataScientist #MachineLearning #TechCareers #DSInterviewQuestions #GitHubResources #CareerInDataScience #CodingInterview
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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๐ฌ๐ผ๐๐ฟ_๐๐ฎ๐๐ฎ_๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ_๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐_๐ฆ๐๐๐ฑ๐_๐ฃ๐น๐ฎ๐ป.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
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ 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๏ธโฃ 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๏ธโฃ 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๏ธโฃ 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๏ธโฃ 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๏ธโฃ 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
๐ All links are available in the GitHub repository:
https://lnkd.in/djcgcKRT
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
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
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
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
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
https://lnkd.in/djcgcKRT
#DataScience #InterviewPrep #MachineLearning #SQL #Python #Statistics #Probability #CodingInterview #AIBootcamp #DeepLearning #LLMs #ComputerVision #GitHubResources #CareerInDataScience
โ๏ธ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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