🚀𝗧𝗼𝗽 𝟯 𝗙𝗿𝗲𝗲 𝗚𝗼𝗼𝗴𝗹𝗲-𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟱😍

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𝗛𝘆𝗱𝗲𝗿𝗮𝗯𝗮𝗱 :- https://pdlink.in/4kFhjn3

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Here's a good list of cheat sheets for programmers (all free):

Data Science Cheatsheet

https://github.com/aaronwangy/Data-Science-Cheatsheet

SQL Cheatsheet

sqltutorial.org/sql-cheat-sheet

t.me/sqlspecialist/827

https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf

Java Programming Cheatsheet

https://introcs.cs.princeton.edu/java/11cheatsheet/

Javanoscript Cheatsheet

quickref.me/javanoscript.html

t.me/javanoscript_courses/532

Data Analytics Cheatsheets

https://dataanalytics.beehiiv.com/p/data

Python Cheat sheet

quickref.me/python.html

https://news.1rj.ru/str/pythondevelopersindia/314

GIT and Machine Learning Cheatsheet

https://news.1rj.ru/str/datasciencefun/714

HTML Cheatsheet

https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf

htmlcheatsheet.com

CSS Cheatsheet

htmlcheatsheet.com/css

jQuery Cheatsheet

t.me/webdevelopmentbook/90

Data Visualization
t.me/datasciencefun/698

Free entry to our WhatsApp channel

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ENJOY LEARNING👍👍

𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝟯𝟬-𝗗𝗮𝘆 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍

📊 If I had to restart my Data Science journey in 2025, this is where I’d begin✨️

Meet 30 Days of Data Science — a free and beginner-friendly GitHub repository that guides you through the core fundamentals of data science in just one month🧑‍🎓📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4mfNdXR

Simply bookmark the page, pick Day 1, and begin your journey✅️

Roadmap to Becoming a Python Developer 🚀

1. Basics 🌱
- Learn programming fundamentals and Python syntax.

2. Core Python 🧠
- Master data structures, functions, and OOP.

3. Advanced Python 📈
- Explore modules, file handling, and exceptions.

4. Web Development 🌐
- Use Django or Flask; build REST APIs.

5. Data Science 📊
- Learn NumPy, pandas, and Matplotlib.

6. Projects & Practice💡
- Build projects, contribute to open-source, join communities.

Like for more ❤️

ENJOY LEARNING 👍👍

🎓 𝐀𝐜𝐜𝐞𝐧𝐭𝐮𝐫𝐞 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 | 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗼𝘄 😍

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𝐋𝐢𝐧𝐤 👇:- 

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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).

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𝟳 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗘𝘃𝗲𝗿𝘆 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗦𝗵𝗼𝘂𝗹𝗱 𝗠𝗮𝘀𝘁𝗲𝗿😍

If you’re serious about becoming a data analyst, there’s no skipping SQL. It’s not just another technical skill — it’s the core language for data analytics.📊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/44S3Xi5

This guide covers 7 key SQL concepts that every beginner must learn✅️

🚀 𝗚𝗼𝗼𝗴𝗹𝗲 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 | 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗼𝘄 😍

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𝐋𝐢𝐧𝐤 👇:- 

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𝗦𝗤𝗟 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 📊

Whether you're writing daily queries or preparing for interviews, understanding these subtle SQL differences can make a big impact on both performance and accuracy.

🧠 Here’s a powerful visual that compares the most commonly misunderstood SQL concepts — side by side.

📌 𝗖𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘀𝗻𝗮𝗽𝘀𝗵𝗼𝘁:
🔹 RANK() vs DENSE_RANK()
🔹 HAVING vs WHERE
🔹 UNION vs UNION ALL
🔹 JOIN vs UNION
🔹 CTE vs TEMP TABLE
🔹 SUBQUERY vs CTE
🔹 ISNULL vs COALESCE
🔹 DELETE vs DROP
🔹 INTERSECT vs INNER JOIN
🔹 EXCEPT vs NOT IN

React ♥️ for detailed post with examples

7 Essential Data Science Techniques to Master 👇

Machine Learning for Predictive Modeling

Machine learning is the backbone of predictive analytics. Techniques like linear regression, decision trees, and random forests can help forecast outcomes based on historical data. Whether you're predicting customer churn, stock prices, or sales trends, understanding these models is key to making data-driven predictions.

Feature Engineering to Improve Model Performance

Raw data is rarely ready for analysis. Feature engineering involves creating new variables from your existing data that can improve the performance of your machine learning models. For example, you might transform timestamps into time features (hour, day, month) or create aggregated metrics like moving averages.

Clustering for Data Segmentation

Unsupervised learning techniques like K-Means or DBSCAN are great for grouping similar data points together without predefined labels. This is perfect for tasks like customer segmentation, market basket analysis, or anomaly detection, where patterns are hidden in your data that you need to uncover.

Time Series Forecasting

Predicting future events based on historical data is one of the most common tasks in data science. Time series forecasting methods like ARIMA, Exponential Smoothing, or Facebook Prophet allow you to capture seasonal trends, cycles, and long-term patterns in time-dependent data.

Natural Language Processing (NLP)

NLP techniques are used to analyze and extract insights from text data. Key applications include sentiment analysis, topic modeling, and named entity recognition (NER). NLP is particularly useful for analyzing customer feedback, reviews, or social media data.

Dimensionality Reduction with PCA

When working with high-dimensional data, reducing the number of variables without losing important information can improve the performance of machine learning models. Principal Component Analysis (PCA) is a popular technique to achieve this by projecting the data into a lower-dimensional space that captures the most variance.

Anomaly Detection for Identifying Outliers

Detecting unusual patterns or anomalies in data is essential for tasks like fraud detection, quality control, and system monitoring. Techniques like Isolation Forest, One-Class SVM, and Autoencoders are commonly used in data science to detect outliers in both supervised and unsupervised contexts.

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𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 & 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗧𝗼𝗽 𝗝𝗼𝗯𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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𝐋𝐢𝐧𝐤👇:-

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Skills for Data Scientists 👆

Python Project Ideas 💡