#MachineLearning Systems โ Principles and Practices of Engineering Artificially Intelligent Systems: https://mlsysbook.ai/
open-source textbook focuses on how to design and implement AI systems effectively
open-source textbook focuses on how to design and implement AI systems effectively
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.iss.one/DataScienceMโ
Please open Telegram to view this post
VIEW IN TELEGRAM
โค5๐3
Forwarded from Python | Machine Learning | Coding | R
This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practicesโsuch as feature engineering or balancing response variablesโor discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
https://dafriedman97.github.io/mlbook/content/introduction.html
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.iss.one/CodeProgrammerโ
Please open Telegram to view this post
VIEW IN TELEGRAM
๐4โค2
Forwarded from Python | Machine Learning | Coding | R
"Introduction to Probability for Data Science"
One of the best books on #Probability. Available FREE.
Download the book:
probability4datascience.com/download.html
One of the best books on #Probability. Available FREE.
Download the book:
probability4datascience.com/download.html
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.iss.one/CodeProgrammerโ
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
๐7โค2
Forwarded from Python | Machine Learning | Coding | R
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.iss.one/addlist/0f6vfFbEMdAwODBk
Please open Telegram to view this post
VIEW IN TELEGRAM
@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.iss.one/addlist/0f6vfFbEMdAwODBk
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
๐9โค1
Forwarded from Python | Machine Learning | Coding | R
from SQL to pandas.pdf
1.3 MB
#DataScience #SQL #pandas #InterviewPrep #Python #DataAnalysis #CareerGrowth #TechTips #Analytics
Please open Telegram to view this post
VIEW IN TELEGRAM
๐7โค3๐ฅ1
Forwarded from Python | Machine Learning | Coding | R
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.iss.one/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
โค7๐4
This media is not supported in your browser
VIEW IN TELEGRAM
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.iss.one/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
โค9
Forwarded from Python | Machine Learning | Coding | R
#DataScience #SQL #Python #MachineLearning #Statistics #BusinessAnalytics #ProductCaseStudies #DataScienceProjects #InterviewPrep #LearnDataScience #YouTubeLearning #CodingInterview #MLInterview #SQLProjects #PythonForDataScience
Please open Telegram to view this post
VIEW IN TELEGRAM
โค6๐1
๐ฅ Trending Repository: data-engineer-handbook
๐ Description: This is a repo with links to everything you'd ever want to learn about data engineering
๐ Repository URL: https://github.com/DataExpert-io/data-engineer-handbook
๐ Readme: https://github.com/DataExpert-io/data-engineer-handbook#readme
๐ Statistics:
๐ Stars: 36.3K stars
๐ Watchers: 429
๐ด Forks: 7K forks
๐ป Programming Languages: Jupyter Notebook - Python - Makefile - Dockerfile - Shell
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: This is a repo with links to everything you'd ever want to learn about data engineering
๐ Repository URL: https://github.com/DataExpert-io/data-engineer-handbook
๐ Readme: https://github.com/DataExpert-io/data-engineer-handbook#readme
๐ Statistics:
๐ Stars: 36.3K stars
๐ Watchers: 429
๐ด Forks: 7K forks
๐ป Programming Languages: Jupyter Notebook - Python - Makefile - Dockerfile - Shell
๐ท๏ธ Related Topics:
#data #awesome #sql #bigdata #dataengineering #apachespark
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ฅ Trending Repository: leantime
๐ Description: Leantime is a goals focused project management system for non-project managers. Building with ADHD, Autism, and dyslexia in mind.
๐ Repository URL: https://github.com/Leantime/leantime
๐ Website: https://leantime.io
๐ Readme: https://github.com/Leantime/leantime#readme
๐ Statistics:
๐ Stars: 5.8K stars
๐ Watchers: 69
๐ด Forks: 671 forks
๐ป Programming Languages: PHP - JavaScript - CSS - Blade - Twig - HTML
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: Leantime is a goals focused project management system for non-project managers. Building with ADHD, Autism, and dyslexia in mind.
๐ Repository URL: https://github.com/Leantime/leantime
๐ Website: https://leantime.io
๐ Readme: https://github.com/Leantime/leantime#readme
๐ Statistics:
๐ Stars: 5.8K stars
๐ Watchers: 69
๐ด Forks: 671 forks
๐ป Programming Languages: PHP - JavaScript - CSS - Blade - Twig - HTML
๐ท๏ธ Related Topics:
#php #trello #jira #sql #agile #calendar #projects #project_management #kanban #scrum #lean #strategy #timesheets #asana #gantt #hacktoberfest #notion #retrospective #clickup #leantime
==================================
๐ง By: https://t.iss.one/DataScienceM
โค1
๐ฅ Trending Repository: budibase
๐ Description: Create business apps and automate workflows in minutes. Supports PostgreSQL, MySQL, MariaDB, MSSQL, MongoDB, Rest API, Docker, K8s, and more ๐ No code / Low code platform..
๐ Repository URL: https://github.com/Budibase/budibase
๐ Website: https://budibase.com
๐ Readme: https://github.com/Budibase/budibase#readme
๐ Statistics:
๐ Stars: 25.5K stars
๐ Watchers: 218
๐ด Forks: 1.8K forks
๐ป Programming Languages: TypeScript - Svelte - JavaScript - CSS - Shell - Handlebars
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: Create business apps and automate workflows in minutes. Supports PostgreSQL, MySQL, MariaDB, MSSQL, MongoDB, Rest API, Docker, K8s, and more ๐ No code / Low code platform..
๐ Repository URL: https://github.com/Budibase/budibase
๐ Website: https://budibase.com
๐ Readme: https://github.com/Budibase/budibase#readme
๐ Statistics:
๐ Stars: 25.5K stars
๐ Watchers: 218
๐ด Forks: 1.8K forks
๐ป Programming Languages: TypeScript - Svelte - JavaScript - CSS - Shell - Handlebars
๐ท๏ธ Related Topics:
#open_source #internal_tools #workflow_engine #crud_application #workflow_automation #low_code #no_code #rest_api_framework #crud_app #no_code_platform #data_apps #low_code_platform #ai_applications #data_application #workflow_apps #low_code_no_code #sql_gui #ai_app_builder #it_workflows
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ฅ Trending Repository: budibase
๐ Description: Create business apps and automate workflows in minutes. Supports PostgreSQL, MySQL, MariaDB, MSSQL, MongoDB, Rest API, Docker, K8s, and more ๐ No code / Low code platform..
๐ Repository URL: https://github.com/Budibase/budibase
๐ Website: https://budibase.com
๐ Readme: https://github.com/Budibase/budibase#readme
๐ Statistics:
๐ Stars: 25.9K stars
๐ Watchers: 218
๐ด Forks: 1.9K forks
๐ป Programming Languages: TypeScript - Svelte - JavaScript - CSS - Shell - Handlebars
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: Create business apps and automate workflows in minutes. Supports PostgreSQL, MySQL, MariaDB, MSSQL, MongoDB, Rest API, Docker, K8s, and more ๐ No code / Low code platform..
๐ Repository URL: https://github.com/Budibase/budibase
๐ Website: https://budibase.com
๐ Readme: https://github.com/Budibase/budibase#readme
๐ Statistics:
๐ Stars: 25.9K stars
๐ Watchers: 218
๐ด Forks: 1.9K forks
๐ป Programming Languages: TypeScript - Svelte - JavaScript - CSS - Shell - Handlebars
๐ท๏ธ Related Topics:
#open_source #internal_tools #workflow_engine #crud_application #workflow_automation #low_code #no_code #rest_api_framework #crud_app #no_code_platform #data_apps #low_code_platform #ai_applications #data_application #workflow_apps #low_code_no_code #sql_gui #ai_app_builder #it_workflows
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ฅ Trending Repository: leantime
๐ Description: Leantime is a goals focused project management system for non-project managers. Building with ADHD, Autism, and dyslexia in mind.
๐ Repository URL: https://github.com/Leantime/leantime
๐ Website: https://leantime.io
๐ Readme: https://github.com/Leantime/leantime#readme
๐ Statistics:
๐ Stars: 6.8K stars
๐ Watchers: 74
๐ด Forks: 715 forks
๐ป Programming Languages: PHP - JavaScript - CSS - Blade - Twig - HTML
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: Leantime is a goals focused project management system for non-project managers. Building with ADHD, Autism, and dyslexia in mind.
๐ Repository URL: https://github.com/Leantime/leantime
๐ Website: https://leantime.io
๐ Readme: https://github.com/Leantime/leantime#readme
๐ Statistics:
๐ Stars: 6.8K stars
๐ Watchers: 74
๐ด Forks: 715 forks
๐ป Programming Languages: PHP - JavaScript - CSS - Blade - Twig - HTML
๐ท๏ธ Related Topics:
#php #trello #jira #sql #agile #calendar #projects #project_management #kanban #scrum #lean #strategy #timesheets #asana #gantt #hacktoberfest #notion #retrospective #clickup #leantime
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ฅ Trending Repository: chartdb
๐ Description: Database diagrams editor that allows you to visualize and design your DB with a single query.
๐ Repository URL: https://github.com/chartdb/chartdb
๐ Website: https://chartdb.io
๐ Readme: https://github.com/chartdb/chartdb#readme
๐ Statistics:
๐ Stars: 18.1K stars
๐ Watchers: 61
๐ด Forks: 968 forks
๐ป Programming Languages: TypeScript
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: Database diagrams editor that allows you to visualize and design your DB with a single query.
๐ Repository URL: https://github.com/chartdb/chartdb
๐ Website: https://chartdb.io
๐ Readme: https://github.com/chartdb/chartdb#readme
๐ Statistics:
๐ Stars: 18.1K stars
๐ Watchers: 61
๐ด Forks: 968 forks
๐ป Programming Languages: TypeScript
๐ท๏ธ Related Topics:
#react #visualization #mysql #editor #schema_migrations #typescript #sql #database #sqlite #postgresql #mariadb #db #mssql #erd #db_migration #react_flow #xyflow
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ฅ Trending Repository: WrenAI
๐ Description: โก๏ธ GenBI (Generative BI) queries any database in natural language, generates accurate SQL (Text-to-SQL), charts (Text-to-Chart), and AI-powered insights in seconds.
๐ Repository URL: https://github.com/Canner/WrenAI
๐ Website: https://getwren.ai/oss
๐ Readme: https://github.com/Canner/WrenAI#readme
๐ Statistics:
๐ Stars: 10.1K stars
๐ Watchers: 70
๐ด Forks: 1K forks
๐ป Programming Languages: TypeScript - Python - Go - JavaScript - Less - Dockerfile
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: โก๏ธ GenBI (Generative BI) queries any database in natural language, generates accurate SQL (Text-to-SQL), charts (Text-to-Chart), and AI-powered insights in seconds.
๐ Repository URL: https://github.com/Canner/WrenAI
๐ Website: https://getwren.ai/oss
๐ Readme: https://github.com/Canner/WrenAI#readme
๐ Statistics:
๐ Stars: 10.1K stars
๐ Watchers: 70
๐ด Forks: 1K forks
๐ป Programming Languages: TypeScript - Python - Go - JavaScript - Less - Dockerfile
๐ท๏ธ Related Topics:
#agent #bigquery #charts #sql #postgresql #bedrock #business_intelligence #openai #spreadsheets #vertex #genbi #text_to_sql #rag #text2sql #duckdb #llm #anthropic #sqlai #text_to_chart
==================================
๐ง By: https://t.iss.one/DataScienceM
Top 100 Data Analyst Interview Questions & Answers
#DataAnalysis #InterviewQuestions #SQL #Python #Statistics #CaseStudy #DataScience
Part 1: SQL Questions (Q1-30)
#1. What is the difference between
A:
โข
โข
โข
#2. Select all unique departments from the
A: Use the
#3. Find the top 5 highest-paid employees.
A: Use
#4. What is the difference between
A:
โข
โข
#5. What are the different types of SQL joins?
A:
โข
โข
โข
โข
โข
#6. Write a query to find the second-highest salary.
A: Use
#7. Find duplicate emails in a
A: Group by the email column and use
#8. What is a primary key vs. a foreign key?
A:
โข A Primary Key is a constraint that uniquely identifies each record in a table. It must contain unique values and cannot contain NULL values.
โข A Foreign Key is a key used to link two tables together. It is a field (or collection of fields) in one table that refers to the Primary Key in another table.
#9. Explain Window Functions. Give an example.
A: Window functions perform a calculation across a set of table rows that are somehow related to the current row. Unlike aggregate functions, they do not collapse rows.
#10. What is a CTE (Common Table Expression)?
A: A CTE is a temporary, named result set that you can reference within a
#DataAnalysis #InterviewQuestions #SQL #Python #Statistics #CaseStudy #DataScience
Part 1: SQL Questions (Q1-30)
#1. What is the difference between
DELETE, TRUNCATE, and DROP?A:
โข
DELETE is a DML command that removes rows from a table based on a WHERE clause. It is slower as it logs each row deletion and can be rolled back.โข
TRUNCATE is a DDL command that quickly removes all rows from a table. It is faster, cannot be rolled back, and resets table identity.โข
DROP is a DDL command that removes the entire table, including its structure, data, and indexes.#2. Select all unique departments from the
employees table.A: Use the
DISTINCT keyword.SELECT DISTINCT department
FROM employees;
#3. Find the top 5 highest-paid employees.
A: Use
ORDER BY and LIMIT.SELECT name, salary
FROM employees
ORDER BY salary DESC
LIMIT 5;
#4. What is the difference between
WHERE and HAVING?A:
โข
WHERE is used to filter records before any groupings are made (i.e., it operates on individual rows).โข
HAVING is used to filter groups after aggregations (GROUP BY) have been performed.-- Find departments with more than 10 employees
SELECT department, COUNT(employee_id)
FROM employees
GROUP BY department
HAVING COUNT(employee_id) > 10;
#5. What are the different types of SQL joins?
A:
โข
(INNER) JOIN: Returns records that have matching values in both tables.โข
LEFT (OUTER) JOIN: Returns all records from the left table, and the matched records from the right table.โข
RIGHT (OUTER) JOIN: Returns all records from the right table, and the matched records from the left table.โข
FULL (OUTER) JOIN: Returns all records when there is a match in either the left or right table.โข
SELF JOIN: A regular join, but the table is joined with itself.#6. Write a query to find the second-highest salary.
A: Use
OFFSET or a subquery.-- Method 1: Using OFFSET
SELECT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 1;
-- Method 2: Using a Subquery
SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
#7. Find duplicate emails in a
customers table.A: Group by the email column and use
HAVING to find groups with a count greater than 1.SELECT email, COUNT(email)
FROM customers
GROUP BY email
HAVING COUNT(email) > 1;
#8. What is a primary key vs. a foreign key?
A:
โข A Primary Key is a constraint that uniquely identifies each record in a table. It must contain unique values and cannot contain NULL values.
โข A Foreign Key is a key used to link two tables together. It is a field (or collection of fields) in one table that refers to the Primary Key in another table.
#9. Explain Window Functions. Give an example.
A: Window functions perform a calculation across a set of table rows that are somehow related to the current row. Unlike aggregate functions, they do not collapse rows.
-- Rank employees by salary within each department
SELECT
name,
department,
salary,
RANK() OVER (PARTITION BY department ORDER BY salary DESC) as dept_rank
FROM employees;
#10. What is a CTE (Common Table Expression)?
A: A CTE is a temporary, named result set that you can reference within a
SELECT, INSERT, UPDATE, or DELETE statement. It helps improve readability and break down complex queries.โข (Time: 90s) Simpson's Paradox occurs when:
a) A model performs well on training data but poorly on test data.
b) Two variables appear to be correlated, but the correlation is caused by a third variable.
c) A trend appears in several different groups of data but disappears or reverses when these groups are combined.
d) The mean, median, and mode of a distribution are all the same.
โข (Time: 75s) When presenting your findings to non-technical stakeholders, you should focus on:
a) The complexity of your statistical models and the p-values.
b) The story the data tells, the business implications, and actionable recommendations.
c) The exact Python code and SQL queries you used.
d) Every single chart and table you produced during EDA.
โข (Time: 75s) A survey about job satisfaction is only sent out via a corporate email newsletter. The results may suffer from what kind of bias?
a) Survivorship bias
b) Selection bias
c) Recall bias
d) Observer bias
โข (Time: 90s) For which of the following machine learning algorithms is feature scaling (e.g., normalization or standardization) most critical?
a) Decision Trees and Random Forests.
b) K-Nearest Neighbors (KNN) and Support Vector Machines (SVM).
c) Naive Bayes.
d) All algorithms require feature scaling to the same degree.
โข (Time: 90s) A Root Cause Analysis for a business problem primarily aims to:
a) Identify all correlations related to the problem.
b) Assign blame to the responsible team.
c) Build a model to predict when the problem will happen again.
d) Move beyond symptoms to find the fundamental underlying cause of the problem.
โข (Time: 75s) A "funnel analysis" is typically used to:
a) Segment customers into different value tiers.
b) Understand and optimize a multi-step user journey, identifying where users drop off.
c) Forecast future sales.
d) Perform A/B tests on a website homepage.
โข (Time: 75s) Tracking the engagement metrics of users grouped by their sign-up month is an example of:
a) Funnel Analysis
b) Regression Analysis
c) Cohort Analysis
d) Time-Series Forecasting
โข (Time: 90s) A retail company wants to increase customer lifetime value (CLV). A data-driven first step would be to:
a) Redesign the company logo.
b) Increase the price of all products.
c) Perform customer segmentation (e.g., using RFM analysis) to understand the behavior of different customer groups and tailor strategies accordingly.
d) Switch to a new database provider.
#DataAnalysis #Certification #Exam #Advanced #SQL #Pandas #Statistics #MachineLearning
โโโโโโโโโโโโโโโ
By: @DataScienceM โจ
a) A model performs well on training data but poorly on test data.
b) Two variables appear to be correlated, but the correlation is caused by a third variable.
c) A trend appears in several different groups of data but disappears or reverses when these groups are combined.
d) The mean, median, and mode of a distribution are all the same.
โข (Time: 75s) When presenting your findings to non-technical stakeholders, you should focus on:
a) The complexity of your statistical models and the p-values.
b) The story the data tells, the business implications, and actionable recommendations.
c) The exact Python code and SQL queries you used.
d) Every single chart and table you produced during EDA.
โข (Time: 75s) A survey about job satisfaction is only sent out via a corporate email newsletter. The results may suffer from what kind of bias?
a) Survivorship bias
b) Selection bias
c) Recall bias
d) Observer bias
โข (Time: 90s) For which of the following machine learning algorithms is feature scaling (e.g., normalization or standardization) most critical?
a) Decision Trees and Random Forests.
b) K-Nearest Neighbors (KNN) and Support Vector Machines (SVM).
c) Naive Bayes.
d) All algorithms require feature scaling to the same degree.
โข (Time: 90s) A Root Cause Analysis for a business problem primarily aims to:
a) Identify all correlations related to the problem.
b) Assign blame to the responsible team.
c) Build a model to predict when the problem will happen again.
d) Move beyond symptoms to find the fundamental underlying cause of the problem.
โข (Time: 75s) A "funnel analysis" is typically used to:
a) Segment customers into different value tiers.
b) Understand and optimize a multi-step user journey, identifying where users drop off.
c) Forecast future sales.
d) Perform A/B tests on a website homepage.
โข (Time: 75s) Tracking the engagement metrics of users grouped by their sign-up month is an example of:
a) Funnel Analysis
b) Regression Analysis
c) Cohort Analysis
d) Time-Series Forecasting
โข (Time: 90s) A retail company wants to increase customer lifetime value (CLV). A data-driven first step would be to:
a) Redesign the company logo.
b) Increase the price of all products.
c) Perform customer segmentation (e.g., using RFM analysis) to understand the behavior of different customer groups and tailor strategies accordingly.
d) Switch to a new database provider.
#DataAnalysis #Certification #Exam #Advanced #SQL #Pandas #Statistics #MachineLearning
โโโโโโโโโโโโโโโ
By: @DataScienceM โจ
โค2๐ฅ1
๐ Multi-Agent SQL Assistant, Part 2: Building a RAG Manager
๐ Category: AI APPLICATIONS
๐ Date: 2025-11-06 | โฑ๏ธ Read time: 21 min read
Explore building a multi-agent SQL assistant in this hands-on guide to creating a RAG Manager. Part 2 of this series provides a practical comparison of multiple Retrieval-Augmented Generation strategies, weighing traditional keyword search against modern vector-based approaches using FAISS and Chroma. Learn how to select and implement the most effective retrieval method to enhance your AI assistant's performance and accuracy when interacting with databases.
#RAG #SQL #AI #VectorSearch #LLM
๐ Category: AI APPLICATIONS
๐ Date: 2025-11-06 | โฑ๏ธ Read time: 21 min read
Explore building a multi-agent SQL assistant in this hands-on guide to creating a RAG Manager. Part 2 of this series provides a practical comparison of multiple Retrieval-Augmented Generation strategies, weighing traditional keyword search against modern vector-based approaches using FAISS and Chroma. Learn how to select and implement the most effective retrieval method to enhance your AI assistant's performance and accuracy when interacting with databases.
#RAG #SQL #AI #VectorSearch #LLM
โค1