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Join a live hands-on session by GeeksforGeeks & Salesforce for working professionals

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Registeration link:👇
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𝟱 𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Explore AI, machine learning, and cloud computing — straight from Google and FREE

1. 🌐Google AI for Anyone
2. 💻Google AI for JavaScript Developers
3. ☁️ Cloud Computing Fundamentals (Google Cloud)
4. 🔍 Data, ML & AI in Google Cloud
5. 📊 Smart Analytics, ML & AI on Google Cloud

𝐋𝐢𝐧𝐤 👇:-

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Enroll for FREE & Get Certified 🎓

Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:

👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.

👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.

👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.

👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.

👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.

👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.

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

ENJOY LEARNING 👍👍

MUST ADD these 5 POWER Bl projects to your resume to get hired

Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger

📌Customer Churn Analysis
🔗 https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input

📌Credit Card Fraud
🔗 https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

📌Movie Sales Analysis
🔗https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data

📌Airline Sector
🔗https://www.kaggle.com/datasets/yuanyuwendymu/airline-

📌Financial Data Analysis
🔗https://www.kaggle.com/datasets/qks1%7Cver/financial-data-

Simple guide

1. Data Utilization:
- Initiate the process by using the provided datasets for a comprehensive analysis.

2. Domain Research:
- Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis.

3. Dashboard Blueprint:
- Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality.

4. Data Handling:
- Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations.

5. Question Formulation:
- Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data.

6. Platform Integration:
- Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility.

7. LinkedIn Visibility:
- Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections.

Join for more: https://t.iss.one/DataPortfolio

Hope this helps you :)

Call for papers on AI to AI Journey* conference journal has started!
Prize for the best scientific paper - 1 million roubles!

Selected papers will be published in the scientific journal Doklady Mathematics.

📖 The journal:
•  Indexed in the largest bibliographic databases of scientific citations
•  Accessible to an international audience and published in the world’s digital libraries

Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference.
Prize for the best article - 1 million roubles!

More detailed information can be found in the Selection Rules -> AI Journey

*AI Journey - a major online conference in the field of AI technologies

𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Want to master web development? These free certification courses will help you build real-world full-stack skills:

✅ Web Design 🎨
✅ JavaScript ⚡ 
✅ Front-End Libraries 📚
✅ Back-End & APIs 🌐 
✅ Databases 💾 

💡 Start learning today and build your career for FREE! 🚀

𝐋𝐢𝐧𝐤 👇:-

https://pdlink.in/4bqbQwB

Enroll for FREE & Get Certified 🎓

📊 Power BI / Tableau Dashboard Inspiration

🚀 Want to Build Stunning Dashboards? Try This!

Creating an interactive and insightful dashboard is a key skill for any Data Analyst. Here’s a simple Power BI / Tableau dashboard idea to practice!

📝 Project Idea: Sales Performance Dashboard

📌 Dataset: Use free datasets from Kaggle or Sample Superstore (Tableau)

📌 Key Visuals to Include:
✅ Total Sales, Profit, and Orders (KPI Cards)
✅ Sales Trend Over Time (Line Chart)
✅ Top 5 Best-Selling Products (Bar Chart)
✅ Sales by Region & Category (Map & Pie Chart)
✅ Customer Segmentation (Filters & Slicers)

💡 Pro Tips:
🔹 Use conditional formatting to highlight trends 📊
🔹 Add slicers to make the dashboard interactive 🔍
🔹 Keep colors consistent for better readability 🎨

📌 Bonus Challenge: Can you create a drill-through feature to view details by region?

Join @dataportfolio to find free data analytics projects

Like this post for more content like this ♥️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Datasets Guide 📚

A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.

Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.

Link: https://docs.unsloth.ai/basics/datasets-guide

𝗟𝗲𝗮𝗿𝗻 𝗡𝗲𝘄 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗮𝗿𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀!😍

Looking to upgrade your skills in Data Science, Programming, AI, Business, and more? 📚💡

This platform offers FREE online courses that help you gain job-ready expertise and earn certificates to showcase your achievements! ✅

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/41Nulbr

Don’t miss out! Start exploring today📌

The Data Science skill no one talks about...

Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
    1. a dataset, and
    2. a clearly defined metric to optimize for, e.g. accuracy

But it doesn’t.

It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.

Let’s go through an example.

Example

Imagine you are a data scientist at Uber. And your product lead tells you:

    👩‍💼: “We want to decrease user churn by 5% this quarter”

We say that a user churns when she decides to stop using Uber.

But why?

There are different reasons why a user would stop using Uber. For example:

   1.  “Lyft is offering better prices for that geo” (pricing problem)
   2. “Car waiting times are too long” (supply problem)
   3. “The Android version of the app is very slow” (client-app performance problem)

You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.

Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?

This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.

You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.

For example…

Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)

One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:

    The A group. No user in this group will receive any discount.

    The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.

You could add more groups (e.g. C, D, E…) to test different pricing points.

In a nutshell

    1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem

Websites to find Free Project Datasets 👆

𝗧𝗼𝗽 𝟰 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 😍

These FREE resources are all you need to go from beginner to confident analyst! 💻📊

✅ Hands-on projects
✅ Beginner to advanced lessons
✅ Resume-worthy skills

𝗟𝗶𝗻𝗸:-👇

https://pdlink.in/4jkQaW1

Learn today, level up tomorrow. Let’s go!✅

Sharing 20+ Diverse Datasets📊 for Data Science and Analytics practice!


1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview

2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand

3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction

4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data

5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction

6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris

7. Titanic Dataset: https://www.kaggle.com/c/titanic

8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality

9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data

11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud

13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows

14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new

15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting

16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19

17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness

18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata

19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams

20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140

21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer


💻🔍 Don't miss out on these valuable resources for advancing your data science journey!

𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁😍

✅ Beginner-friendly
✅ Straight from Microsoft
✅ And yes… a badge for that resume flex

Perfect for beginners, job seekers, & Working Professionals

𝐋𝐢𝐧𝐤 👇:-

https://pdlink.in/4iq8QlM

Enroll for FREE & Get Certified 🎓

Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:

👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.

👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.

👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.

👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.

👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.

👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.

By following these tips, you can be well-prepared for your next data science interview. Good luck!

𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲? 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗪𝗶𝗹𝗹 𝗛𝗲𝗹𝗽 𝗬𝗼𝘂 𝗚𝗲𝘁 𝗧𝗵𝗲𝗿𝗲😍

Dreaming of working at Google but not sure where to even begin?📍

Start with these FREE insider resources—from building a resume that stands out to mastering the Google interview process. 🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/441GCKF

Because if someone else can do it, so can you. Why not you? Why not now?✅️

Mathematics for Machine Learning

📚 Book

𝗡𝗼 𝗗𝗲𝗴𝗿𝗲𝗲? 𝗡𝗼 𝗣𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗖𝗮𝗻 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗝𝗼𝗯😍

Dreaming of a career in data but don’t have a degree? You don’t need one. What you do need are the right skills🔗

These 4 free/affordable certifications can get you there. 💻✨

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4ioaJ2p

Let’s get you certified and hired!✅️

Here are 10 project ideas to work on for Data Analytics

1. Customer Churn Prediction: Predict customer churn for subscription-based services. Skills: EDA, classification models. Tools: Python, Scikit-Learn.
2. Retail Sales Forecasting: Forecast sales using historical data. Skills: Time series analysis. Tools: Python, Statsmodels.
3. Sentiment Analysis: Analyze sentiments in product reviews or tweets. Skills: Text processing, NLP. Tools: Python, NLTK.
4. Loan Approval Prediction: Predict loan approvals based on credit risk. Skills: Classification models. Tools: Python, Scikit-Learn.
5. COVID-19 Data Analysis: Explore and visualize COVID-19 trends. Skills: EDA, visualization. Tools: Python, Tableau.
6. Traffic Accident Analysis: Discover patterns in traffic accidents. Skills: Clustering, heatmaps. Tools: Python, Folium.
7. Movie Recommendation System: Build a recommendation system using user ratings. Skills: Collaborative filtering. Tools: Python, Scikit-Learn.
8. E-commerce Analysis: Analyze top-performing products in e-commerce. Skills: EDA, association rules. Tools: Python, Apriori.
9. Stock Market Analysis: Analyze stock trends using historical data. Skills: Moving averages, sentiment analysis. Tools: Python, Matplotlib.
10. Employee Attrition Analysis: Predict employee turnover. Skills: Classification models, HR analytics. Tools: Python, Scikit-Learn.

And this is how you can work on

Here’s a compact list of free resources for working on data analytics projects:

1. Datasets
• Kaggle Datasets: Wide range of datasets and community discussions.
• UCI Machine Learning Repository: Great for educational datasets.
• Data.gov: U.S. government datasets (e.g., traffic, COVID-19).
2. Learning Platforms
• YouTube: Channels like Data School and freeCodeCamp for tutorials.
• 365DataScience: Data Science & AI Related Courses
3. Tools
• Google Colab: Free Jupyter Notebooks for Python coding.
• Tableau Public & Power BI Desktop: Free data visualization tools.
4. Project Resources
• Kaggle Notebooks & GitHub: Code examples and project walk-throughs.
• Data Analytics on Medium: Project guides and tutorials.

ENJOY LEARNING ✅️✅️

#datascienceprojects

𝟱 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗵𝗮𝘁’𝗹𝗹 𝗠𝗮𝗸𝗲 𝗦𝗤𝗟 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 𝗖𝗹𝗶𝗰𝗸.😍

SQL seems tough, right? 😩

These 5 FREE SQL resources will take you from beginner to advanced without boring theory dumps or confusion.📊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3GtntaC

Master it with ease. 💡

Python Roadmap: 🗺

📂 Basics
 ∟📂 Data Types & Variables
 ∟📂 Operators & Expressions
 ∟📂 Control Flow (if, loops)
  ∟📂 Functions & Modules
   ∟📂 File Handling
    ∟📂 OOP (Classes & Objects)
     ∟📂 Exception Handling
      
∟📂 Advanced Topics (Decorators, Generators)
 ∟📂 Libraries (NumPy, Pandas, Matplotlib)
 ∟📂 Web Scraping / API Integration
 ∟📂 Frameworks (Flask/Django)
  ∟📂 Automation & Scripting
   ∟📂 Projects
    ∟ ✅ Apply For Job

Like if you need a detailed explanation step-by-step ❤️