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๐ฉ๐ปโ๐ป I found a GitHub repo full of resources you need to succeed in Data Science and Machine Learning interviews!
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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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Master Machine Learning in Just 20 Days.1745724742524
30.8 MB
Title:
Master Machine Learning in Just 20 Days - Your Ultimate Guide! ๐ฅ
Description:
Struggling to break into Data Science or ace ML interviews at top product-based companies?
This 20-day roadmap covers ML basics to advanced topics like tuning, deep learning, and deployment with top resources and practice questions!
Whatโs Inside:
โ Supervised & Unsupervised Learning โ Regression, Classification, Clustering
โ Deep Learning & Neural Networks โ CNNs, RNNs, LSTMs
โ End-to-End ML Projects โ Data Preprocessing, Feature Engineering, Deployment
โ Model Optimization โ Hyperparameter Tuning, Ensemble Methods
โ Real-World ML Applications โ NLP, AutoML, Scalable ML Systems
#MachineLearning #DeepLearning #DataScience #ArtificialIntelligence #MLEngineering #CareerGrowth #MLRoadmap
By: t.me/HusseinSheikhoโ
๐ฏ BEST DATA SCIENCE CHANNELS ON TELEGRAM ๐
Master Machine Learning in Just 20 Days - Your Ultimate Guide! ๐ฅ
Description:
Struggling to break into Data Science or ace ML interviews at top product-based companies?
This 20-day roadmap covers ML basics to advanced topics like tuning, deep learning, and deployment with top resources and practice questions!
Whatโs Inside:
โ Supervised & Unsupervised Learning โ Regression, Classification, Clustering
โ Deep Learning & Neural Networks โ CNNs, RNNs, LSTMs
โ End-to-End ML Projects โ Data Preprocessing, Feature Engineering, Deployment
โ Model Optimization โ Hyperparameter Tuning, Ensemble Methods
โ Real-World ML Applications โ NLP, AutoML, Scalable ML Systems
#MachineLearning #DeepLearning #DataScience #ArtificialIntelligence #MLEngineering #CareerGrowth #MLRoadmap
By: t.me/HusseinSheikho
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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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from SQL to pandas.pdf
1.3 MB
#DataScience #SQL #pandas #InterviewPrep #Python #DataAnalysis #CareerGrowth #TechTips #Analytics
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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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