Hey guys!

I’ve been getting a lot of requests from you all asking for solid Data Analytics projects that can help you boost resume and build real skills.

So here you go —

These aren’t just “for practice,” they’re portfolio-worthy projects that show recruiters you’re ready for real-world work.

1. Sales Performance Dashboard

Tools: Excel / Power BI / Tableau
You’ll take raw sales data and turn it into a clean, interactive dashboard. Show key metrics like revenue, profit, top products, and regional trends.
Skills you build: Data cleaning, slicing & filtering, dashboard creation, business storytelling.

2. Customer Churn Analysis

Tools: Python (Pandas, Seaborn)

Work with a telecom or SaaS dataset to identify which customers are likely to leave and why.

Skills you build: Exploratory data analysis, visualization, correlation, and basic machine learning.


3. E-commerce Product Insights using SQL

Tools: SQL + Power BI

Analyze product categories, top-selling items, and revenue trends from a sample e-commerce dataset.

Skills you build: Joins, GROUP BY, aggregation, data modeling, and visual storytelling.


4. HR Analytics Dashboard

Tools: Excel / Power BI

Dive into employee data to find patterns in attrition, hiring trends, average salaries by department, etc.

Skills you build: Data summarization, calculated fields, visual formatting, DAX basics.


5. Movie Trends Analysis (Netflix or IMDb Dataset)

Tools: Python (Pandas, Matplotlib)

Explore trends across genres, ratings, and release years. Great for people who love entertainment and want to show creativity.

Skills you build: Data wrangling, time-series plots, filtering techniques.


6. Marketing Campaign Analysis

Tools: Excel / Power BI / SQL

Analyze data from a marketing campaign to measure ROI, conversion rates, and customer engagement. Identify which channels or strategies worked best and suggest improvements.

Skills you build: Data blending, KPI calculation, segmentation, and actionable insights.


7. Financial Expense Analysis & Budget Forecasting

Tools: Excel / Power BI / Python

Work on a company’s expense data to analyze spending patterns, categorize expenses, and create a forecasting model to predict future budgets.

Skills you build: Time series analysis, forecasting, budgeting, and financial storytelling.


Pick 2–3 projects. Don’t just show the final visuals — explain your process on LinkedIn or GitHub. That’s what sets you apart.

Like for more useful content ❤️

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!👨‍💻

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

Data Analytics Projects for Beginners 👇


Web Scraping
https://github.com/shreyaswankhede/IMDb-Web-Scraping-and-Sentiment-Analysis

Product Price Scraping and Analysis
https://github.com/CodesdaLu/Web-Scrapping

News Scraping
https://github.com/rohit-yadav/scraping-news-articles

Real Time Stock Price Scraping with Python
https://youtu.be/rONhdonaWUo?si=A3oDEVbLIAP78cCz

Zomato Analysis
https://youtu.be/fFi_TBw27is?si=E0iLd3J06YHfQkRk

IPL Analysis
https://github.com/Yashmenaria1/IPL-Data-Exploration

https://www.youtube.com/watch?v=ur-v0dv0Qtw

https://www.youtube.com/watch?v=ur-v0dv0Qtw

Football Data Analysis
https://youtu.be/yat7soj__4w?si=h5CLIvVFzzKm8IEP

Market Basket Analysis
https://youtu.be/Ne8Sbp2hJIk?si=ThEuvdOnRrpcVjOg

Customer Churn Prediction
https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction

Employee’s Performance for HR Analytics
https://www.kaggle.com/code/rajatraj0502/employee-s-performance-for-hr-analytics

Food Price Prediction
https://github.com/VectorInstitute/foodprice-forecasting

𝗦𝗤𝗟 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀😍

SQL is the backbone of data analytics. Whether you’re cleaning data, generating reports, or exploring trends—SQL helps you turn raw information into actionable insights.

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/43lI7CO

Use ChatGPT like a developer — not just a casual user✅️

⚡️ Stanford Released a Free Course on Language Modeling from Scratch

The university is currently teaching CS336: Language Modeling from Scratch - and uploading the full course to YouTube for everyone in real time.

Here’s why it’s a big deal:

• Anyone can learn to build their own language models from zero - completely free
• Full course: from architecture and tokenizers to RL training and scaling
• Explained step-by-step, beginner-friendly (even if you’re new to coding)
• Each lecture includes extra reading, assignments, and slides

📚 Course site: https://web.stanford.edu/class/cs336
▶️ YouTube playlist: Watch here

Sber500 is now accepting applications for its 6th batch — an international accelerator for tech startups in AI, DeepTech, FinTech, and beyond.

This fully online, 12-week program is designed for early-stage teams — whether you’ve got an MVP or a product ready to scale. Open to founders worldwide, with a special focus on BRICS countries. The participation is totally free!

🚀 What’s in it for you:
• Mentors from 17+ countries, including experts from Google, Amazon, Oracle
• Access to VCs, corporate partners, and pilot opportunities
• PR visibility in a fast-growing ecosystem
• Strategic entry into the Russian market

The top 25 teams will pitch live at Demo Day in Moscow to investors, corporates, and Sber leadership.

Yes, the application form is detailed — and that’s intentional. The more effort you put in now, the greater your chances of joining. Don’t rush it — this is your gateway to major opportunities.

📅 Deadline extended: June 9
Apply now → https://tinyurl.com/6wunzste

If you’re building something bold and ambitious — this is your moment. Join us!

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👇:-

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Perfect for beginners and aspiring pros✅️

🚀 Complete Roadmap to Become a Data Scientist in 5 Months

📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.

🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.

🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.

🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.

🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.

🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.

📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).

🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).

🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).

🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.

🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.

💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.

🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.

👨‍💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.

🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.

🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.

🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.

🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!

🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥

🎓 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👨‍🎓📌

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

A-Z of Data Science Part-1

A-Z of Data Science Part-2

Python Topics with Projects ✅

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 ✅️

Are you looking to become a machine learning engineer? 🤖
The algorithm brought you to the right place! 🚀

I created a free and comprehensive roadmap. Let’s go through this thread and explore what you need to know to become an expert machine learning engineer:

📚 Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, especially in linear algebra, probability, and statistics. Here’s what you need to focus on:

- Basic probability concepts 🎲
- Inferential statistics 📊
- Regression analysis 📈
- Experimental design & A/B testing 🔍
- Bayesian statistics 🔢
- Calculus 🧮
- Linear algebra 🔠

🐍 Python
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.

- Variables, data types, and basic operations ✏️
- Control flow statements (e.g., if-else, loops) 🔄
- Functions and modules 🔧
- Error handling and exceptions ❌
- Basic data structures (e.g., lists, dictionaries, tuples) 🗂️
- Object-oriented programming concepts 🧱
- Basic work with APIs 🌐
- Detailed data structures and algorithmic thinking 🧠

🧪 Machine Learning Prerequisites
- Exploratory Data Analysis (EDA) with NumPy and Pandas 🔍
- Data visualization techniques to visualize variables 📉
- Feature extraction & engineering 🛠️
- Encoding data (different types) 🔐

⚙️ Machine Learning Fundamentals
Use the scikit-learn library along with other Python libraries for:

- Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees 📊
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering 🧠
- Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients 🕹️

Solve two types of problems:
- Regression 📈
- Classification 🧩

🧠 Neural Networks
Neural networks are like computer brains that learn from examples 🧠, made up of layers of "neurons" that handle data. They learn without explicit instructions.

Types of Neural Networks:
- Feedforward Neural Networks: Simplest form, with straight connections and no loops 🔄
- Convolutional Neural Networks (CNNs): Great for images, learning visual patterns 🖼️
- Recurrent Neural Networks (RNNs): Good for sequences like text or time series 📚

In Python, use TensorFlow and Keras, as well as PyTorch for more complex neural network systems.

🕸️ Deep Learning
Deep learning is a subset of machine learning that can learn unsupervised from data that is unstructured or unlabeled.

- CNNs 🖼️
- RNNs 📝
- LSTMs ⏳

🚀 Machine Learning Project Deployment

Machine learning engineers should dive into MLOps and project deployment.

Here are the must-have skills:

- Version Control for Data and Models 🗃️
- Automated Testing and Continuous Integration (CI) 🔄
- Continuous Delivery and Deployment (CD) 🚚
- Monitoring and Logging 🖥️
- Experiment Tracking and Management 🧪
- Feature Stores 🗂️
- Data Pipeline and Workflow Orchestration 🛠️
- Infrastructure as Code (IaC) 🏗️
- Model Serving and APIs 🌐

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

ENJOY LEARNING 👍👍

𝗦𝗤𝗟 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Looking to master SQL for Data Analytics or prep for your dream tech job? 💼

These 3 Free SQL resources will help you go from beginner to job-ready—without spending a single rupee! 📊✨

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3TcvfsA

💥 Start learning today and build the skills top companies want!✅️

List of AI Project Ideas 👨🏻‍💻🤖 -

Beginner Projects

🔹 Sentiment Analyzer
🔹 Image Classifier
🔹 Spam Detection System
🔹 Face Detection
🔹 Chatbot (Rule-based)
🔹 Movie Recommendation System
🔹 Handwritten Digit Recognition
🔹 Speech-to-Text Converter
🔹 AI-Powered Calculator
🔹 AI Hangman Game

Intermediate Projects

🔸 AI Virtual Assistant
🔸 Fake News Detector
🔸 Music Genre Classification
🔸 AI Resume Screener
🔸 Style Transfer App
🔸 Real-Time Object Detection
🔸 Chatbot with Memory
🔸 Autocorrect Tool
🔸 Face Recognition Attendance System
🔸 AI Sudoku Solver

Advanced Projects

🔺 AI Stock Predictor
🔺 AI Writer (GPT-based)
🔺 AI-powered Resume Builder
🔺 Deepfake Generator
🔺 AI Lawyer Assistant
🔺 AI-Powered Medical Diagnosis
🔺 AI-based Game Bot
🔺 Custom Voice Cloning
🔺 Multi-modal AI App
🔺 AI Research Paper Summarizer

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

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𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

𝗦𝗤𝗟:- https://pdlink.in/3TcvfsA

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3Hfpwjc

𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3ZyQpFd

𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3Hnx3wh

𝗗𝗲𝘃𝗢𝗽𝘀 :- https://pdlink.in/4jyxBwS

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Enroll for FREE & Get Certified 🎓

Data Analysis is not just SQL.
Data Analysis is not just PowerBI/Tableau.
Data Analysis is not just Python.
Data Analysis is not just Excel.


𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐢𝐬 𝐚𝐛𝐨𝐮𝐭:

✅𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲: It's about uncovering the stories hidden within the data.

✅𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐚𝐤𝐢𝐧𝐠: It's about informing business decisions with data-driven insights.

✅ 𝐓𝐫𝐞𝐧𝐝 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: It's about identifying trends and patterns to forecast future outcomes.

✅ 𝐏𝐫𝐨𝐛𝐥𝐞𝐦-𝐒𝐨𝐥𝐯𝐢𝐧𝐠: It's about addressing business challenges with data-backed solutions.

✅ 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠: It's about evaluating data with an analytical mindset to ensure accurate and reliable conclusions.

✅ 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭: It's about iterating and refining processes for better outcomes.

Tools like Power BI, Tableau, Excel, and Python are just that—tools. The real value lies in how we use them to transform data into actionable insights.

𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗧𝗮𝗸𝗲 𝗢𝗻𝗹𝗶𝗻𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

🎓No MIT Admission? No Problem — Learn from MIT for Free!🔥

MIT is known for world-class education—but you don’t need to walk its halls to access its knowledge📚📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4jBNtP2

These courses offer industry-relevant skills & completion certificates at no cost✅️

✨The STAR method is a powerful technique used to answer behavioral interview questions effectively.

It helps structure responses by focusing on Situation, Task, Action, and Result. For analytics professionals, using the STAR method ensures that you demonstrate your problem-solving abilities, technical skills, and business acumen in a clear and concise way.

Here’s how the STAR method works, tailored for an analytics interview:

📍 1. Situation
Describe the context or challenge you faced. For analysts, this might be related to data challenges, business processes, or system inefficiencies. Be specific about the setting, whether it was a project, a recurring task, or a special initiative.

Example: “At my previous role as a data analyst at XYZ Company, we were experiencing a high churn rate among our subscription customers. This was a critical issue because it directly impacted revenue.”*

📍 2. Task
Explain the responsibilities you had or the goals you needed to achieve in that situation. In analytics, this usually revolves around diagnosing the problem, designing experiments, or conducting data analysis.

Example: “I was tasked with identifying the factors contributing to customer churn and providing actionable insights to the marketing team to help them improve retention.”*

📍 3. Action
Detail the specific actions you took to address the problem. Be sure to mention any tools, software, or methodologies you used (e.g., SQL, Python, data #visualization tools, #statistical #models). This is your opportunity to showcase your technical expertise and approach to problem-solving.

Example: “I collected and analyzed customer data using #SQL to extract key trends. I then used #Python for data cleaning and statistical analysis, focusing on engagement metrics, product usage patterns, and customer feedback. I also collaborated with the marketing and product teams to understand business priorities.”*

📍 4. Result
Highlight the outcome of your actions, especially any measurable impact. Quantify your results if possible, as this demonstrates your effectiveness as an analyst. Show how your analysis directly influenced business decisions or outcomes.

Example: “As a result of my analysis, we discovered that customers were disengaging due to a lack of certain product features. My insights led to a targeted marketing campaign and product improvements, reducing churn by 15% over the next quarter.”*

Example STAR Answer for an Analytics Interview Question:
Question: *"Tell me about a time you used data to solve a business problem."*

Answer (STAR format): 
🔻*S*: “At my previous company, our sales team was struggling with inconsistent performance, and management wasn’t sure which factors were driving the variance.” 
🔻*T*: “I was assigned the task of conducting a detailed analysis to identify key drivers of sales performance and propose data-driven recommendations.” 
🔻*A*: “I began by collecting sales data over the past year and segmented it by region, product line, and sales representative. I then used Python for #statistical #analysis and developed a regression model to determine the key factors influencing sales outcomes. I also visualized the data using #Tableau to present the findings to non-technical stakeholders.” 
🔻*R*: “The analysis revealed that product mix and regional seasonality were significant contributors to the variability. Based on my findings, the company adjusted their sales strategy, leading to a 20% increase in sales efficiency in the next quarter.”

Hope this helps you 😊