𝟰 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗙𝗿𝗲𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁, 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗔𝗜/𝗠𝗟 & 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 😍

Learn Tech the Smart Way: Step-by-Step Roadmaps for Beginners🚀

Learning tech doesn’t have to be overwhelming—especially when you have a roadmap to guide you!📊📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/45wfx2V

Enjoy Learning ✅️

100 Days Data Science Challenge 👆

𝟴 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗠𝗜𝗧 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱😍

🎓 Learn Data Science for Free from the World’s Best Universities🚀

Top institutions like Harvard, MIT, and Stanford are offering world-class data science courses online — and they’re 100% free. 🎯📍

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Hfpwjc

All The Best 👍

Free Datasets to work on Power BI + SQL projects 👇👇

1. AdventureWorks Sample Database:
- Link: [AdventureWorks Sample Database](https://docs.microsoft.com/en-us/sql/samples/adventureworks-install-configure?view=sql-server-ver15)
- Description: A sample database provided by Microsoft, containing sales, products, customers, and other related data.

2. Online Retail Dataset:
- Link: [UCI Machine Learning Repository - Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/online+retail)
- Description: Transactional data from an online retail store, suitable for customer segmentation and sales analysis.

3. Supermarket Sales Dataset:
- Link: [Supermarket Sales Dataset](https://www.kaggle.com/aungpyaeap/supermarket-sales)
- Description: Sales data from a supermarket, useful for inventory management and sales performance analysis.

4. Yahoo Finance (Historical Stock Data):
- Link: [Yahoo Finance](https://finance.yahoo.com/)
- Description: Historical stock data for various companies, suitable for financial analysis and visualization.

5. Human Resources Analytics: Employee Attrition and Performance:
- Link: [Kaggle HR Analytics Dataset](https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)
- Description: Employee data including demographics, performance, and attrition information, suitable for employee performance analysis.

Bonus Open Sources Resources: https://t.iss.one/DataPortfolio/16

These datasets are freely available for practicing Power BI and SQL skills. You can download them from the provided links and import them into your SQL database management system (e.g., MySQL, SQL Server, PostgreSQL) for hands-on ☺️💪

𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯 𝗠𝗼𝗻𝘁𝗵𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗿𝗲𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗼𝗮𝗱𝗺𝗮𝗽😍

🎯 Want to Master Data Science in Just 3 Months?📊

Feeling overwhelmed by the sheer volume of resources and don’t know where to start? You’re not alone🚀

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/43uHPrX

This FREE GitHub roadmap is a game-changer for anyone✅️

👉The Ultimate Guide to the Pandas Library for Data Science in Python
👇👇

https://www.freecodecamp.org/news/the-ultimate-guide-to-the-pandas-library-for-data-science-in-python/amp/

A Visual Intro to NumPy and Data Representation
.
Link : 👇👇
https://jalammar.github.io/visual-numpy/

Matplotlib Cheatsheet 👇👇

https://github.com/rougier/matplotlib-cheatsheet

SQL Cheatsheet 👇👇

https://websitesetup.org/sql-cheat-sheet/

𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀😍

𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇

S&P Global :- https://pdlink.in/3ZddwVz

IBM :- https://pdlink.in/4kDmMKE

TVS Credit :- https://pdlink.in/4mI0JVc

Sutherland :- https://pdlink.in/4mGYBgg

Other Jobs :- https://pdlink.in/44qEIDu

Apply before the link expires 💫

If you're a software engineer in your 20s, beware of this habit, it can kill your growth faster than anything else.

► Fake learning.

It feels productive, but it's not.

Let me give you a great example:

You wake up fired up.
Open YouTube, start a system design video.
An hour goes by. You nod, you get it (or so you think).
You switch to a course on Spring Boot. Build a to-do app.
Then read a blog on Kafka. Scroll through a thread on Redis.
By evening, you feel like you’ve had a productive day.

But two weeks later?

You can’t recall a single implementation detail.
You haven’t written a line of code around those topics.
You just consumed, but never applied.

That’s fake learning.

It’s learning without doing.
It gives you the illusion of growth, while keeping you stuck.

📌 Here’s how to fix it:

Watch fewer tutorials. Build more things.
Learn with a goal: “I’ll use this to build X.”

After every video, write your own summary.
Recode it from scratch.

Start documenting what you really understood vs. what felt easy.

Real growth happens when you struggle.
When you break things. When you debug.

Passive learning is comfortable.
But discomfort is where the actual skills are built.

Your 20s are for laying that solid technical foundation.
Don’t waste them just “watching smart.”

Build. Ship. Reflect.
That’s how you grow.

Coding Projects:👇
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502

ENJOY LEARNING 👍👍

𝟰 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Want to Boost Your Resume with In-Demand Python Skills?👨‍💻

In today’s tech-driven world, Python is one of the most in-demand programming languages across data science, software development, and machine learning📊📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Hnx3wh

Enjoy Learning ✅️

Want to become a Data Scientist?

Here’s a quick roadmap with essential concepts:

1. Mathematics & Statistics

Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.

Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.

Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.


2. Programming

Python or R: Choose a primary programming language for data science.

Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.

R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.


SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.


3. Data Wrangling & Preprocessing

Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.


4. Data Visualization

Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.


5. Machine Learning

Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.


6. Advanced Machine Learning & Deep Learning

Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.


7. Natural Language Processing (NLP)

Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.


8. Big Data Tools (Optional)

Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.


9. Data Science Workflows & Pipelines (Optional)

ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).


10. Model Validation & Tuning

Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.


11. Time Series Analysis

Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.


12. Experimentation & A/B Testing

Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.

ENJOY LEARNING 👍👍

#datascience

𝗠𝗮𝘀𝘁𝗲𝗿 𝟲 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍

Want to boost your career with highly sought-after tech skills? These 6 YouTube channels will help you learn from scratch!👨‍💻

No need for expensive courses—start learning for FREE today!🚀

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Ddxd7P

Don’t miss this opportunity—start learning today and take your skills to the next level!✅️

Exploratory Data Analysis ( EDA)

𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 𝗼𝗻 𝗚𝗶𝘁𝗛𝘂𝗯 𝗘𝘃𝗲𝗿𝘆 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗕𝗼𝗼𝗸𝗺𝗮𝗿𝗸😍

🧠Master Data Science Faster with This Free GitHub Cheat Sheet🚀

Whether you’re starting your data science journey or preparing for job interviews, having the right revision tool can make all the difference🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4klQmF3

Must-have resource for students and professionals✅️

𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭:

df = pd.read_csv('your_dataset.csv')

𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧:

1- View the first few rows:
df.head()

2- Summary of the dataset:
df.info()

3- Statistical summary:
df.describe()

𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬:

1- Identify missing values:
df.isnull().sum()

2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()

𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:

1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()

2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()

3- Pair plots:
sns.pairplot(df)
plt.show()

4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()

𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
Count plots for categorical features:

plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()

Python Interview Q&A: https://topmate.io/coding/898340

Like for more ❤️

ENJOY LEARNING 👍👍

𝟱 𝗠𝘂𝘀𝘁-𝗙𝗼𝗹𝗹𝗼𝘄 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗳𝗼𝗿 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Want to Become a Data Scientist in 2025? Start Here!🎯

If you’re serious about becoming a Data Scientist in 2025, the learning doesn’t have to be expensive — or boring!🚀

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4kfBR5q

Perfect for beginners and aspiring pros✅️

👨‍💻 𝟓 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐤𝐢𝐥𝐥𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐍𝐞𝐞𝐝𝐬 𝐢𝐧 𝐚𝐧 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 📊

🔸𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 & 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
You need to understand two main types of machine learning: supervised learning (used for predicting outcomes, like whether a customer will buy a product) and unsupervised learning (used to find patterns, like grouping customers based on buying behavior).

🔸𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
This is about turning raw data into useful information for your model. Knowing how to clean data, fill missing values, and create new features will improve the model's performance.

🔸𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬
It’s important to know how to check if a model is working well. Use simple measures like accuracy (how often the model is right), precision, and recall to assess your model’s performance.

🔸𝐅𝐚𝐦𝐢𝐥𝐢𝐚𝐫𝐢𝐭𝐲 𝐰𝐢𝐭𝐡 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬
Get to know basic machine learning algorithms like Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). These are often used for solving real-world problems and can help you choose the best approach.

🔸𝐃𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬
Once you’ve built a model, it’s important to know how to use it in the real world. Learn how to deploy models so they can be used by others in your organization and continue to make decisions automatically.

🔍 𝐏𝐫𝐨 𝐓𝐢𝐩: Keep practicing by working on real projects or using online platforms to improve these skills!

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

Like if you need similar content 😄👍

Hope this helps you 😊

#ai #datascience

🎓 𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱, 𝗠𝗜𝗧 & 𝗚𝗼𝗼𝗴𝗹𝗲😍

Why pay thousands when you can access world-class Computer Science courses for free? 🌐

Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills👨‍🎓📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3ZyQpFd

Perfect for students, self-learners, and career switchers✅️

Coding isn't easy!

It’s the art of turning ideas into functional, impactful software that shapes the world around us.

To truly excel in coding, focus on these key areas:

0. Understanding the Basics: Learn the syntax, variables, loops, and conditionals in your chosen programming language. These are the building blocks of coding.


1. Mastering Data Structures and Algorithms: These are the backbone of efficient, scalable, and optimized code.


2. Learning Debugging Techniques: Understand how to identify and fix errors in your code using tools and logical thinking.


3. Writing Clean Code: Follow best practices like commenting, indentation, and naming conventions to make your code readable and maintainable.


4. Building Real-World Projects: Hands-on experience is essential. Apply what you learn by building applications, games, or automation scripts.


5. Collaborating with Git: Master version control to work effectively in teams and manage your codebase.


6. Exploring Frameworks and Libraries: Learn to use tools that simplify coding and add functionality to your projects.


7. Understanding Problem-Solving: Focus on logical thinking and breaking down problems into smaller, manageable parts.


8. Adapting to New Technologies: Stay curious and keep learning new languages, paradigms, and tools as they emerge.


9. Practicing Consistently: Coding is a skill that improves with regular practice and perseverance.

💡 Embrace the process, learn from your mistakes, and keep pushing your limits to grow as a developer.

Best Programming Resources: https://topmate.io/coding/886839

ENJOY LEARNING 👍👍

𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗼𝗻 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁 𝗚𝘂𝗶𝗱𝗲😍

🎥 YouTube is the ultimate free classroom—and this is your Data Analytics syllabus in one post!👨‍💻

From Python and SQL to Power BI, Machine Learning, and Data Science, these carefully curated playlists will take you from complete beginner to job-ready✨️📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4jzVggc

Enjoy Learning ✅️