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.

1️⃣ 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

2️⃣ 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

3️⃣ 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

4️⃣ 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

5️⃣ 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

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

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Introduction to Deep Learning
As we continue to push the boundaries of what's possible with artificial intelligence, I wanted to take a moment to share some insights on one of the most exciting fields in AI: Deep Learning.

Deep Learning is a subset of machine learning that uses neural networks to analyze and interpret data. These neural networks are designed to mimic the human brain, with layers of interconnected nodes (neurons) that process and transmit information.

What makes Deep Learning so powerful?

Ability to learn from large datasets: Deep Learning algorithms can learn from vast amounts of data, including images, speech, and text.
Improved accuracy: Deep Learning models can achieve state-of-the-art performance in tasks such as image recognition, natural language processing, and speech recognition.
Ability to generalize: Deep Learning models can generalize well to new, unseen data, making them highly effective in real-world applications.
Real-world applications of Deep Learning
Computer Vision: Self-driving cars, facial recognition, object detection
Natural Language Processing: Language translation, text summarization, sentiment analysis
Speech Recognition: Virtual assistants, voice-controlled devices.

#DeepLearning #AI #MachineLearning #NeuralNetworks #ArtificialIntelligence #DataScience #ComputerVision #NLP #SpeechRecognition #TechInnovation

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

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

😉 A list of the best YouTube videos
✅ To learn data science


1️⃣ SQL language


⬅️ Learning

💰 4-hour SQL course from zero to one hundred

💰 Window functions tutorial

⬅️ Projects

📎 Starting your first SQL project

💰 Data cleansing project

💰 Restaurant order analysis

⬅️ Interview

💰 How to crack the SQL interview?

➖➖➖

2️⃣ Python


⬅️ Learning

💰 12-hour Python for Data Science course

⬅️ Projects

💰 Python project for beginners

💰 Analyzing Corona Data with Python

⬅️ Interview

💰 Python interview golden tricks

💰 Python Interview Questions

➖➖➖

3️⃣ Statistics and machine learning


⬅️ Learning

💰 7-hour course in applied statistics

💰 Machine Learning Training Playlist

⬅️ Projects

💰 Practical ML Project

⬅️ Interview

💰 ML Interview Questions and Answers

💰 How to pass a statistics interview?

➖➖➖

4️⃣ Product and business case studies


⬅️ Learning

💰 Building strong product understanding

💰 Product Metric Definition

⬅️ Interview

💰 Case Study Analysis Framework

💰 How to shine in a business interview?

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

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

Topic: Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts

---

1. What is a Dataset?

• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.

---

2. Types of Datasets

• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).

• Unstructured Data: Images, text, audio, video.

• Semi-structured Data: JSON, XML files containing hierarchical data.

---

3. Common Dataset Formats

• CSV (Comma-Separated Values)

• Excel (.xls, .xlsx)

• JSON (JavaScript Object Notation)

• XML (eXtensible Markup Language)

• Images (JPEG, PNG, TIFF)

• Audio (WAV, MP3)

---

4. Loading Datasets in Python

• Use libraries like pandas for structured data:

import pandas as pd
df = pd.read_csv('data.csv')

• Use libraries like json for JSON files:

import json
with open('data.json') as f:
    data = json.load(f)

---

5. Basic Dataset Exploration

• Check shape and size:

print(df.shape)

• Preview data:

print(df.head())

• Check for missing values:

print(df.isnull().sum())

---

6. Summary

• Understanding dataset types is crucial before processing.

• Loading and exploring datasets helps identify cleaning and preprocessing needs.

---

Exercise

• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.

---

#DataScience #Datasets #DataLoading #Python #DataExploration

The rest of the parts 👇
https://t.iss.one/DataScienceM 🌟

𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝗳𝗼𝗿 𝗝𝗼𝗯 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀.

In DS or AI/ML interviews, you need to be able to explain models, debug them live, and design AI/ML systems from scratch. If you can’t demonstrate this during an interview, expect to hear, “We’ll get back to you.”

The attached person's name is Chip Huyen. Hopefully you know her; if not, then I can't help you here. She is probably one of the finest authors in the field of AI/ML.

She designed proper documentation/a book for common ML interview questions.

Target Audiences: ML engineer, a platform engineer, a research scientist, or you want to do ML but don’t yet know the differences among those titles.Check the comment section for links and repos.

📌 link:
https://huyenchip.com/ml-interviews-book/

#JobInterview #MachineLearning #AI #DataScience #MLEngineer #AIInterview #TechCareers #DeepLearning #AICommunity #MLSystems #CareerGrowth #AIJobs #ChipHuyen #InterviewPrep #DataScienceCommunit


https://t.iss.one/CodeProgrammer 🌟

👨🏻‍💻 This Python library helps you extract usable data for language models from complex files like tables, images, charts, or multi-page documents.

📝 The idea of Agentic Document Extraction is that unlike common methods like OCR that only read text, it can also understand the structure and relationships between different parts of the document. For example, it understands which title belongs to which table or image.


✅ Works with PDFs, images, and website links.

☑️ Can chunk and process very large documents (up to 1000 pages) by itself.

✔️ Outputs both JSON and Markdown formats.

☑️ Even specifies the exact location of each section on the page.

✔️ Supports parallel and batch processing.

pip install agentic-doc

┌ 🥵 Agentic Document Extraction
├ 🌎 Website
└ 🐱 GitHub Repos

🌐 #DataScience #DataScience
➖➖➖➖➖➖➖➖➖➖➖➖➖
https://t.iss.one/CodeProgrammer