Data Science Cheatsheet 💪
❤3
Machine Learning – Essential Concepts 🚀
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
❤2
🚀 Become an Agentic AI Builder — Free 12‑Week Certification by Ready Tensor
Ready Tensor’s Agentic AI Developer Certification is a free, project first 12‑week program designed to help you build and deploy real-world agentic AI systems. You'll complete three portfolio-ready projects using tools like LangChain, LangGraph, and vector databases, while deploying production-ready agents with FastAPI or Streamlit.
The course focuses on developing autonomous AI agents that can plan, reason, use memory, and act safely in complex environments. Certification is earned not by watching lectures, but by building — each project is reviewed against rigorous standards.
You can start anytime, and new cohorts begin monthly. Ideal for developers and engineers ready to go beyond chat prompts and start building true agentic systems.
👉 Apply now: https://www.readytensor.ai/agentic-ai-cert/
Ready Tensor’s Agentic AI Developer Certification is a free, project first 12‑week program designed to help you build and deploy real-world agentic AI systems. You'll complete three portfolio-ready projects using tools like LangChain, LangGraph, and vector databases, while deploying production-ready agents with FastAPI or Streamlit.
The course focuses on developing autonomous AI agents that can plan, reason, use memory, and act safely in complex environments. Certification is earned not by watching lectures, but by building — each project is reviewed against rigorous standards.
You can start anytime, and new cohorts begin monthly. Ideal for developers and engineers ready to go beyond chat prompts and start building true agentic systems.
👉 Apply now: https://www.readytensor.ai/agentic-ai-cert/
❤2
Jupyter Notebooks are essential for data analysts working with Python.
Here’s how to make the most of this great tool:
1. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗖𝗹𝗲𝗮𝗿 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲:
Break your notebook into logical sections using markdown headers. This helps you and your colleagues navigate the notebook easily and understand the flow of analysis. You could use headings (#, ##, ###) and bullet points to create a table of contents.
2. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀:
Add markdown cells to explain your methodology, code, and guidelines for the user. This Enhances the readability and makes your notebook a great reference for future projects. You might want to include links to relevant resources and detailed docs where necessary.
3. 𝗨𝘀𝗲 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗪𝗶𝗱𝗴𝗲𝘁𝘀:
Leverage ipywidgets to create interactive elements like sliders, dropdowns, and buttons. With those, you can make your analysis more dynamic and allow users to explore different scenarios without changing the code. Create widgets for parameter tuning and real-time data visualization.
𝟰. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝗠𝗼𝗱𝘂𝗹𝗮𝗿:
Write reusable functions and classes instead of long, monolithic code blocks. This will improve the code maintainability and efficiency of your notebook. You should store frequently used functions in separate Python noscripts and import them when needed.
5. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆:
Utilize libraries like Matplotlib, Seaborn, and Plotly for your data visualizations. These clear and insightful visuals will help you to communicate your findings. Make sure to customize your plots with labels, noscripts, and legends to make them more informative.
6. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Jupyter Notebooks are great for exploration, but they often lack systematic version control. Use tools like Git and nbdime to track changes, collaborate effectively, and ensure that your work is reproducible.
7. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Clean and secure your notebooks by removing sensitive information before sharing. This helps to prevent the leakage of private data. You should consider using environment variables for credentials.
Keeping these techniques in mind will help to transform your Jupyter Notebooks into great tools for analysis and communication.
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Here’s how to make the most of this great tool:
1. 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗖𝗹𝗲𝗮𝗿 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲:
Break your notebook into logical sections using markdown headers. This helps you and your colleagues navigate the notebook easily and understand the flow of analysis. You could use headings (#, ##, ###) and bullet points to create a table of contents.
2. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀:
Add markdown cells to explain your methodology, code, and guidelines for the user. This Enhances the readability and makes your notebook a great reference for future projects. You might want to include links to relevant resources and detailed docs where necessary.
3. 𝗨𝘀𝗲 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗪𝗶𝗱𝗴𝗲𝘁𝘀:
Leverage ipywidgets to create interactive elements like sliders, dropdowns, and buttons. With those, you can make your analysis more dynamic and allow users to explore different scenarios without changing the code. Create widgets for parameter tuning and real-time data visualization.
𝟰. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗖𝗹𝗲𝗮𝗻 𝗮𝗻𝗱 𝗠𝗼𝗱𝘂𝗹𝗮𝗿:
Write reusable functions and classes instead of long, monolithic code blocks. This will improve the code maintainability and efficiency of your notebook. You should store frequently used functions in separate Python noscripts and import them when needed.
5. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆:
Utilize libraries like Matplotlib, Seaborn, and Plotly for your data visualizations. These clear and insightful visuals will help you to communicate your findings. Make sure to customize your plots with labels, noscripts, and legends to make them more informative.
6. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Jupyter Notebooks are great for exploration, but they often lack systematic version control. Use tools like Git and nbdime to track changes, collaborate effectively, and ensure that your work is reproducible.
7. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗬𝗼𝘂𝗿 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀:
Clean and secure your notebooks by removing sensitive information before sharing. This helps to prevent the leakage of private data. You should consider using environment variables for credentials.
Keeping these techniques in mind will help to transform your Jupyter Notebooks into great tools for analysis and communication.
I have curated the best interview resources to crack Python Interviews 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this 👍❤️
❤3
Coding Project Ideas with AI 👇👇
1. Sentiment Analysis Tool: Develop a tool that uses AI to analyze the sentiment of text data, such as social media posts, customer reviews, or news articles. The tool could classify the sentiment as positive, negative, or neutral.
2. Image Recognition App: Create an app that uses AI image recognition algorithms to identify objects, scenes, or people in images. This could be useful for applications like automatic photo tagging or security surveillance.
3. Chatbot Development: Build a chatbot using AI natural language processing techniques to interact with users and provide information or assistance on a specific topic. You could integrate the chatbot into a website or messaging platform.
4. Recommendation System: Develop a recommendation system that uses AI algorithms to suggest products, movies, music, or other items based on user preferences and behavior. This could enhance the user experience on e-commerce platforms or streaming services.
5. Fraud Detection System: Create a fraud detection system that uses AI to analyze patterns and anomalies in financial transactions data. The system could help identify potentially fraudulent activities and prevent financial losses.
6. Health Monitoring App: Build an app that uses AI to monitor health data, such as heart rate, sleep patterns, or activity levels, and provide personalized recommendations for improving health and wellness.
7. Language Translation Tool: Develop a language translation tool that uses AI machine translation algorithms to translate text between different languages accurately and efficiently.
8. Autonomous Driving System: Work on a project to develop an autonomous driving system that uses AI computer vision and sensor data processing to navigate vehicles safely and efficiently on roads.
9. Personalized Content Generator: Create a tool that uses AI natural language generation techniques to generate personalized content, such as articles, emails, or marketing messages tailored to individual preferences.
10. Music Recommendation Engine: Build a music recommendation engine that uses AI algorithms to analyze music preferences and suggest playlists or songs based on user tastes and listening habits.
Join for more: https://news.1rj.ru/str/Programming_experts
ENJOY LEARNING 👍👍
1. Sentiment Analysis Tool: Develop a tool that uses AI to analyze the sentiment of text data, such as social media posts, customer reviews, or news articles. The tool could classify the sentiment as positive, negative, or neutral.
2. Image Recognition App: Create an app that uses AI image recognition algorithms to identify objects, scenes, or people in images. This could be useful for applications like automatic photo tagging or security surveillance.
3. Chatbot Development: Build a chatbot using AI natural language processing techniques to interact with users and provide information or assistance on a specific topic. You could integrate the chatbot into a website or messaging platform.
4. Recommendation System: Develop a recommendation system that uses AI algorithms to suggest products, movies, music, or other items based on user preferences and behavior. This could enhance the user experience on e-commerce platforms or streaming services.
5. Fraud Detection System: Create a fraud detection system that uses AI to analyze patterns and anomalies in financial transactions data. The system could help identify potentially fraudulent activities and prevent financial losses.
6. Health Monitoring App: Build an app that uses AI to monitor health data, such as heart rate, sleep patterns, or activity levels, and provide personalized recommendations for improving health and wellness.
7. Language Translation Tool: Develop a language translation tool that uses AI machine translation algorithms to translate text between different languages accurately and efficiently.
8. Autonomous Driving System: Work on a project to develop an autonomous driving system that uses AI computer vision and sensor data processing to navigate vehicles safely and efficiently on roads.
9. Personalized Content Generator: Create a tool that uses AI natural language generation techniques to generate personalized content, such as articles, emails, or marketing messages tailored to individual preferences.
10. Music Recommendation Engine: Build a music recommendation engine that uses AI algorithms to analyze music preferences and suggest playlists or songs based on user tastes and listening habits.
Join for more: https://news.1rj.ru/str/Programming_experts
ENJOY LEARNING 👍👍
❤1
Quick SQL functions cheat sheet for beginners
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, …): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, …): Returns the first non-null value.
Like for more free Cheatsheets ❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
#dataanalytics
Aggregate Functions
COUNT(*): Counts rows.
SUM(column): Total sum.
AVG(column): Average value.
MAX(column): Maximum value.
MIN(column): Minimum value.
String Functions
CONCAT(a, b, …): Concatenates strings.
SUBSTRING(s, start, length): Extracts part of a string.
UPPER(s) / LOWER(s): Converts string case.
TRIM(s): Removes leading/trailing spaces.
Date & Time Functions
CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.
EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).
DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.
Numeric Functions
ROUND(num, decimals): Rounds to a specified decimal.
CEIL(num) / FLOOR(num): Rounds up/down.
ABS(num): Absolute value.
MOD(a, b): Returns the remainder.
Control Flow Functions
CASE: Conditional logic.
COALESCE(val1, val2, …): Returns the first non-null value.
Like for more free Cheatsheets ❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
#dataanalytics
❤3
Top 5 Important Languages for Data Science 🧑💻📊
1. Python - 50% 🐍
2. R - 20% 📉
3. SQL - 15% 🗄️
4. Java - 7% ☕
5. Julia - 5% 🚀
6. Matlab - 3% 🧮
1. Python - 50% 🐍
2. R - 20% 📉
3. SQL - 15% 🗄️
4. Java - 7% ☕
5. Julia - 5% 🚀
6. Matlab - 3% 🧮
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🔰 How to become a data scientist in 2025?
👨🏻💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
➖➖➖➖➖
🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
➖➖➖➖➖
🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
➖➖➖➖➖
🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
➖➖➖➖➖
🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
➖➖➖➖➖
🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
➖➖➖➖➖
🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
➖➖➖➖➖
🔢 Step 8: Stay up to date and network
✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 Site: AI Research at Google
👨🏻💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
➖➖➖➖➖
🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
➖➖➖➖➖
🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
➖➖➖➖➖
🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
➖➖➖➖➖
🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
➖➖➖➖➖
🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
➖➖➖➖➖
🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
➖➖➖➖➖
🔢 Step 8: Stay up to date and network
✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 Site: AI Research at Google
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data
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This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
1. Supervised Learning
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.
Some common supervised learning algorithms include:
➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.
2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.
Some popular unsupervised learning algorithms include:
➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.
3. Semi-Supervised Learning
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.
Common semi-supervised learning algorithms include:
➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.
4. Reinforcement Learning
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.
Popular reinforcement learning algorithms include:
➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.
1. Supervised Learning
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.
Some common supervised learning algorithms include:
➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.
2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.
Some popular unsupervised learning algorithms include:
➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.
3. Semi-Supervised Learning
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.
Common semi-supervised learning algorithms include:
➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.
4. Reinforcement Learning
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.
Popular reinforcement learning algorithms include:
➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.
❤3
Goldman Sachs senior data analyst interview asked questions
SQL
1 find avg of salaries department wise from table
2 Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'.
3 newest joinee for every department (solved using lead lag)
POWER BI
1. What does Filter context in DAX mean?
2. Explain how to implement Row-Level Security (RLS) in Power BI.
3. Describe different types of filters in Power BI.
4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX.
5. How do you calculate the total sales for a specific product using DAX?
PYTHON
1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated.
3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.
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SQL
1 find avg of salaries department wise from table
2 Write a SQL query to see employee name and manager name using a self-join on 'employees' table with columns 'emp_id', 'name', and 'manager_id'.
3 newest joinee for every department (solved using lead lag)
POWER BI
1. What does Filter context in DAX mean?
2. Explain how to implement Row-Level Security (RLS) in Power BI.
3. Describe different types of filters in Power BI.
4. Explain the difference between 'ALL' and 'ALLSELECTED' in DAX.
5. How do you calculate the total sales for a specific product using DAX?
PYTHON
1. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.
2. Find unique values in a list of assorted numbers and print the count of how many times each value is repeated.
3. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Hope this helps you 😊
❤7
🟢 7 valuable resources that you can use to prepare for data science interviews!
🟢 One of the most important factors to get data science jobs in the best companies is success in job interviews.
🗂 I have put here 7 valuable resources that helped me a lot while preparing for data science interviews. I hope these resources can help you succeed in data science interviews
1️⃣ machine learning
📕 Link: Machine Learning
2️⃣ Python programming language
📕 Link: Python Programming Language
3️⃣ SQL programming language
📕 Link: SQL Programming Language
4️⃣ R programming language
📕 Link: R Programming Language
5️⃣ Pandas library
📕 Link: Pandas Python Library
6️⃣ NumPy library
📕 Link: NumPy Python Library
7️⃣ Matplotlib library
📕 Link: Matplotlib Python Library
Enjoy 👍
🟢 One of the most important factors to get data science jobs in the best companies is success in job interviews.
🗂 I have put here 7 valuable resources that helped me a lot while preparing for data science interviews. I hope these resources can help you succeed in data science interviews
1️⃣ machine learning
📕 Link: Machine Learning
2️⃣ Python programming language
📕 Link: Python Programming Language
3️⃣ SQL programming language
📕 Link: SQL Programming Language
4️⃣ R programming language
📕 Link: R Programming Language
5️⃣ Pandas library
📕 Link: Pandas Python Library
6️⃣ NumPy library
📕 Link: NumPy Python Library
7️⃣ Matplotlib library
📕 Link: Matplotlib Python Library
Enjoy 👍
❤6
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𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
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Master the most in-demand AI skill in today’s job market: building autonomous AI systems.
In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
👉 Apply now: https://go.readytensor.ai/cert-552-agentic-ai-certification
www.readytensor.ai
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Learn to build chatbots, AI assistants, and multi-agent systems with Ready Tensor's free, self-paced, and beginner-friendly Agentic AI Developer Certification. View the full program guide and how to get certified.
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Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence pinned «🚀🔥 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 Master the most in-demand AI skill in today’s job market: building autonomous AI systems. In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻…»
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🚀🔥 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.
In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
👉 Apply now: https://go.readytensor.ai/cert-552-agentic-ai-certification
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.
In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.
Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.
👉 Apply now: https://go.readytensor.ai/cert-552-agentic-ai-certification
www.readytensor.ai
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