10 great Python packages for Data Science not known to many:
1️⃣ CleanLab
Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.
2️⃣ LazyPredict
A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.
3️⃣ Lux
A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.
4️⃣ PyForest
A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.
5️⃣ PivotTableJS
PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥
6️⃣ Drawdata
Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.
7️⃣ black
The Uncompromising Code Formatter
8️⃣ PyCaret
An open-source, low-code machine learning library in Python that automates the machine learning workflow.
9️⃣ PyTorch-Lightning by LightningAI
Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.
🔟 Streamlit
A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment.
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Like if you need similar content 😄👍
1️⃣ CleanLab
Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.
2️⃣ LazyPredict
A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.
3️⃣ Lux
A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.
4️⃣ PyForest
A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.
5️⃣ PivotTableJS
PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥
6️⃣ Drawdata
Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.
7️⃣ black
The Uncompromising Code Formatter
8️⃣ PyCaret
An open-source, low-code machine learning library in Python that automates the machine learning workflow.
9️⃣ PyTorch-Lightning by LightningAI
Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.
🔟 Streamlit
A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment.
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Like if you need similar content 😄👍
❤2👍2
AI & ML Project Ideas
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🔥 Top SQL Projects for Data Analytics 🚀
If you're preparing for a Data Analyst role or looking to level up your SQL skills, working on real-world projects is the best way to learn!
Here are some must-do SQL projects to strengthen your portfolio. 👇
🟢 Beginner-Friendly SQL Projects (Great for Learning Basics)
✅ Employee Database Management – Build and query HR data 📊
✅ Library Book Tracking – Create a database for book loans and returns
✅ Student Grading System – Analyze student performance data
✅ Retail Point-of-Sale System – Work with sales and transactions 💰
✅ Hotel Booking System – Manage customer bookings and check-ins 🏨
🟡 Intermediate SQL Projects (For Stronger Querying & Analysis)
⚡ E-commerce Order Management – Analyze order trends & customer data 🛒
⚡ Sales Performance Analysis – Work with revenue, profit margins & KPIs 📈
⚡ Inventory Control System – Optimize stock tracking 📦
⚡ Real Estate Listings – Manage and analyze property data 🏡
⚡ Movie Rating System – Analyze user reviews & trends 🎬
🔵 Advanced SQL Projects (For Business-Level Analytics)
🔹 Social Media Analytics – Track user engagement & content trends
🔹 Insurance Claim Management – Fraud detection & risk assessment
🔹 Customer Feedback Analysis – Perform sentiment analysis on reviews ⭐
🔹 Freelance Job Platform – Match freelancers with project opportunities
🔹 Pharmacy Inventory System – Optimize stock levels & prenoscriptions
🔴 Expert-Level SQL Projects (For Data-Driven Decision Making)
🔥 Music Streaming Analysis – Study user behavior & song trends 🎶
🔥 Healthcare Prenoscription Tracking – Identify patterns in medicine usage
🔥 Employee Shift Scheduling – Optimize workforce efficiency ⏳
🔥 Warehouse Stock Control – Manage supply chain data efficiently
🔥 Online Auction System – Analyze bidding patterns & sales performance 🛍️
🔗 Pro Tip: If you're applying for Data Analyst roles, pick 3-4 projects, clean the data, and create interactive dashboards using Power BI/Tableau to showcase insights!
React with ♥️ if you want detailed explanation of each project
Share with credits: 👇 https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
If you're preparing for a Data Analyst role or looking to level up your SQL skills, working on real-world projects is the best way to learn!
Here are some must-do SQL projects to strengthen your portfolio. 👇
🟢 Beginner-Friendly SQL Projects (Great for Learning Basics)
✅ Employee Database Management – Build and query HR data 📊
✅ Library Book Tracking – Create a database for book loans and returns
✅ Student Grading System – Analyze student performance data
✅ Retail Point-of-Sale System – Work with sales and transactions 💰
✅ Hotel Booking System – Manage customer bookings and check-ins 🏨
🟡 Intermediate SQL Projects (For Stronger Querying & Analysis)
⚡ E-commerce Order Management – Analyze order trends & customer data 🛒
⚡ Sales Performance Analysis – Work with revenue, profit margins & KPIs 📈
⚡ Inventory Control System – Optimize stock tracking 📦
⚡ Real Estate Listings – Manage and analyze property data 🏡
⚡ Movie Rating System – Analyze user reviews & trends 🎬
🔵 Advanced SQL Projects (For Business-Level Analytics)
🔹 Social Media Analytics – Track user engagement & content trends
🔹 Insurance Claim Management – Fraud detection & risk assessment
🔹 Customer Feedback Analysis – Perform sentiment analysis on reviews ⭐
🔹 Freelance Job Platform – Match freelancers with project opportunities
🔹 Pharmacy Inventory System – Optimize stock levels & prenoscriptions
🔴 Expert-Level SQL Projects (For Data-Driven Decision Making)
🔥 Music Streaming Analysis – Study user behavior & song trends 🎶
🔥 Healthcare Prenoscription Tracking – Identify patterns in medicine usage
🔥 Employee Shift Scheduling – Optimize workforce efficiency ⏳
🔥 Warehouse Stock Control – Manage supply chain data efficiently
🔥 Online Auction System – Analyze bidding patterns & sales performance 🛍️
🔗 Pro Tip: If you're applying for Data Analyst roles, pick 3-4 projects, clean the data, and create interactive dashboards using Power BI/Tableau to showcase insights!
React with ♥️ if you want detailed explanation of each project
Share with credits: 👇 https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤4
🤖 AI/ML Roadmap
1️⃣ Math & Stats 🧮🔢: Learn Linear Algebra, Probability, and Calculus.
2️⃣ Programming 🐍💻: Master Python, NumPy, Pandas, and Matplotlib.
3️⃣ Machine Learning 📈🤖: Study Supervised & Unsupervised Learning, and Model Evaluation.
4️⃣ Deep Learning 🔥🧠: Understand Neural Networks, CNNs, RNNs, and Transformers.
5️⃣ Specializations 🎓🔬: Choose from NLP, Computer Vision, or Reinforcement Learning.
6️⃣ Big Data & Cloud ☁️📡: Work with SQL, NoSQL, AWS, and GCP.
7️⃣ MLOps & Deployment 🚀🛠️: Learn Flask, Docker, and Kubernetes.
8️⃣ Ethics & Safety ⚖️🛡️: Understand Bias, Fairness, and Explainability.
9️⃣ Research & Practice 📜🔍: Read Papers and Build Projects.
🔟 Projects 📂🚀: Compete in Kaggle and contribute to Open-Source.
React ❤️ for more
#ai
1️⃣ Math & Stats 🧮🔢: Learn Linear Algebra, Probability, and Calculus.
2️⃣ Programming 🐍💻: Master Python, NumPy, Pandas, and Matplotlib.
3️⃣ Machine Learning 📈🤖: Study Supervised & Unsupervised Learning, and Model Evaluation.
4️⃣ Deep Learning 🔥🧠: Understand Neural Networks, CNNs, RNNs, and Transformers.
5️⃣ Specializations 🎓🔬: Choose from NLP, Computer Vision, or Reinforcement Learning.
6️⃣ Big Data & Cloud ☁️📡: Work with SQL, NoSQL, AWS, and GCP.
7️⃣ MLOps & Deployment 🚀🛠️: Learn Flask, Docker, and Kubernetes.
8️⃣ Ethics & Safety ⚖️🛡️: Understand Bias, Fairness, and Explainability.
9️⃣ Research & Practice 📜🔍: Read Papers and Build Projects.
🔟 Projects 📂🚀: Compete in Kaggle and contribute to Open-Source.
React ❤️ for more
#ai
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©How fresher can get a job as a data scientist?©
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
Job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
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Machine learning is a subset of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. In machine learning, computers are trained on large datasets to identify patterns, relationships, and trends without being explicitly programmed to do so.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided along with the input data. Unsupervised learning involves training the algorithm on unlabeled data, allowing it to identify patterns and relationships on its own. Reinforcement learning involves training an algorithm to make decisions by rewarding or punishing it based on its actions.
Machine learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, predictive analytics, and more. These algorithms can be trained using various techniques such as neural networks, decision trees, support vector machines, and clustering algorithms.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided along with the input data. Unsupervised learning involves training the algorithm on unlabeled data, allowing it to identify patterns and relationships on its own. Reinforcement learning involves training an algorithm to make decisions by rewarding or punishing it based on its actions.
Machine learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, predictive analytics, and more. These algorithms can be trained using various techniques such as neural networks, decision trees, support vector machines, and clustering algorithms.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
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Source codes for data science projects 👇👇
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
𝗘𝗡𝗝𝗢𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚👍👍
1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro
2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python
3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/
4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/
5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/
6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/
7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/
𝗘𝗡𝗝𝗢𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚👍👍
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In a data science project, using multiple scalers can be beneficial when dealing with features that have different scales or distributions. Scaling is important in machine learning to ensure that all features contribute equally to the model training process and to prevent certain features from dominating others.
Here are some scenarios where using multiple scalers can be helpful in a data science project:
1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.
2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.
3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.
4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.
5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.
When using multiple scalers in a data science project, it's important to evaluate the impact of scaling on the model performance through cross-validation or other evaluation methods. Try experimenting with different scaling techniques to you find the optimal approach for your specific dataset and machine learning model.
Here are some scenarios where using multiple scalers can be helpful in a data science project:
1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.
2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.
3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.
4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.
5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.
When using multiple scalers in a data science project, it's important to evaluate the impact of scaling on the model performance through cross-validation or other evaluation methods. Try experimenting with different scaling techniques to you find the optimal approach for your specific dataset and machine learning model.
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Guys, Big Announcement!
We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!
I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Here’s what you’ll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile noscript)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic — Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs — Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract noscripts from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer noscript)
- Final Project (Choose, build, and polish your app!)
React with ❤️ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!
I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Here’s what you’ll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile noscript)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic — Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs — Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract noscripts from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer noscript)
- Final Project (Choose, build, and polish your app!)
React with ❤️ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
❤4
Top Platforms for Building Data Science Portfolio
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
7 Websites to Learn Data Science for FREE🧑💻
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
7 Websites to Learn Data Science for FREE🧑💻
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
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2206.13446.pdf
3 MB
Book: 📚Exercises in Machine Learning
Authors: Michael U. Gutmann
year: 2024
pages: 211
Authors: Michael U. Gutmann
year: 2024
pages: 211
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