SQL Interview Question for #DataScience:

A company has provided sales data containing information about customer purchases, as shown in the table below.

Your task is to:

Calculate Total Revenue
Calculate Total Sales by Product
Find Top Customers by Revenue

Solve it using SQL

4 websites to practice SQL

1. Dataford - https://www.dataford.io
2. Interview Query - https://www.interviewquery.com/questions
3. LeetCode - https://leetcode.com/
4. HackerRank - https://www.hackerrank.com/

#datascience

⚠️ O'Reilly Media, one of the most reputable publishers in the fields of programming, data mining, and AI, has made 10 data science books available to those interested in this field for free .

✔️ To use the online and PDF versions of these books, you can use the following links:👇

0⃣ Python Data Science Handbook
┌ Online
└ PDF

1⃣ Python for Data Analysis book
┌ Online
└ PDF

🔢 Fundamentals of Data Visualization book
┌ Online
└ PDF

🔢 R for Data Science book
┌ Online
└ PDF

🔢 Deep Learning for Coders book
┌ Online
└ PDF

🔢 DS at the Command Line book
┌ Online
└ PDF

🔢 Hands-On Data Visualization Book
┌ Online
└ PDF

🔢 Think Stats book
┌ Online
└ PDF

🔢 Think Bayes book
┌ Online
└ PDF

🔢 Kafka, The Definitive Guide
┌ Online
└ PDF

#DataScience #Python #DataAnalysis #DataVisualization #RProgramming #DeepLearning #CommandLine #HandsOnLearning #Statistics #Bayesian #Kafka #MachineLearning #AI #Programming #FreeBooks ✅

🔥 Data Science Roadmap 2025

Step 1: 🐍 Python Basics
Step 2: 📊 Data Analysis (Pandas, NumPy)
Step 3: 📈 Data Visualization (Matplotlib, Seaborn)
Step 4: 🤖 Machine Learning (Scikit-learn)
Step 5: � Deep Learning (TensorFlow/PyTorch)
Step 6: 🗃️ SQL & Big Data (Spark)
Step 7: 🚀 Deploy Models (Flask, FastAPI)
Step 8: 📢 Showcase Projects
Step 9: 💼 Land a Job!

🔓 Pro Tip: Compete on Kaggle

#datascience

🔥 Data Science Roadmap 2025

Step 1: 🐍 Python Basics
Step 2: 📊 Data Analysis (Pandas, NumPy)
Step 3: 📈 Data Visualization (Matplotlib, Seaborn)
Step 4: 🤖 Machine Learning (Scikit-learn)
Step 5: � Deep Learning (TensorFlow/PyTorch)
Step 6: 🗃️ SQL & Big Data (Spark)
Step 7: 🚀 Deploy Models (Flask, FastAPI)
Step 8: 📢 Showcase Projects
Step 9: 💼 Land a Job!

🔓 Pro Tip: Compete on Kaggle

#datascience

Artificial Intelligence isn't easy!

It’s the cutting-edge field that enables machines to think, learn, and act like humans.

To truly master Artificial Intelligence, focus on these key areas:

0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.


1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.


2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.


3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.


4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).


5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.


6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.


7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.


8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.


9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms.



Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.

💡 Embrace the journey of learning and building systems that can reason, understand, and adapt.

⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems!

Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

#ai #datascience

Breaking into Data Science doesn’t need to be complicated.

If you’re just starting out,

Here’s how to simplify your approach:

Avoid:
🚫 Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once.
🚫 Spending months on theoretical concepts without hands-on practice.
🚫 Overloading your resume with keywords instead of impactful projects.
🚫 Believing you need a Ph.D. to break into the field.

Instead:

✅ Start with Python or R—focus on mastering one language first.
✅ Learn how to work with structured data (Excel or SQL) - this is your bread and butter.
✅ Dive into a simple machine learning model (like linear regression) to understand the basics.
✅ Solve real-world problems with open datasets and share them in a portfolio.
✅ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.

Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Like if you need similar content 😄👍

Hope this helps you 😊

#ai #datascience

Data Science Cheat sheet 2.0

A helpful 5-page data science cheatsheet to assist with exam reviews, interview prep, and anything in-between. It covers over a semester of introductory machine learning, and is based on MIT's Machine Learning courses 6.867 and 15.072. The reader should have at least a basic understanding of statistics and linear algebra, though beginners may find this resource helpful as well.

Creator: Aaron Wang
Stars ⭐️: 4.5k
Forked By: 645
https://github.com/aaronwangy/Data-Science-Cheatsheet

#datascience
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