𝟮𝟱+ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 😍

Breaking into Data Analytics isn’t just about knowing the tools — it’s about answering the right questions with confidence🧑‍💻✨️

Whether you’re aiming for your first role or looking to level up your career, these real interview questions will test your skills📊📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3JumloI

Don’t just learn — prepare smart✅️

Resume not working? This might be the problem


I've seen hundreds of data analysts struggle to get a single interview, and I've also seen the resumes that some of my mentees made.

They all say the same thing (and that is the exact reason why they come up to me and say that they're not getting calls):

"I've learned Python.
I've got my SQL certification.
I've built dashboards in Tableau."

Most of you are focusing on the tools rather than the results.

Employers aren't looking for people who can build dashboards—they want to know what that dashboard does for the company. Does it save time? Boost efficiency? Cut costs? Improve sales?

No:
"Built a sales dashboard that improved efficiency."

Yes:
"Created a sales dashboard that reduced reporting time by 30%, using XYZ."

It's not enough to just say you did something.
Explain how you approached the problem, the decisions you made, and the outcomes you achieved. You also get extra points if you identify flaws in your work and how you solved them. That's a story.

And, in resumes, you must Tell your story, not show your grocery list.

Most people focus on what they did.
Most companies focus on what you can do.

I have curated top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊

𝐆𝐄 𝐀𝐞𝐫𝐨𝐬𝐩𝐚𝐜𝐞 𝐈𝐧𝐭𝐞𝐫𝐧𝐬𝐡𝐢𝐩, 𝟐𝟎𝟐𝟓!
Positio: Data Science Intern
Qualification: Bachelor’s/ Master’s Degree
Salary: ₹ 30,000 - ₹ 50,000 Per Month (Expected)
Batch: 2024/ 2025/ 2026/ 2027
Experienc: Freshers
Locatio: Bengaluru, India

📌Apply Now: https://careers.geaerospace.com/global/en/job/R5016107/DT-Data-Science-Intern

👉WhatsApp Channel: https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m

👉Telegram Link: https://t.iss.one/addlist/4q2PYC0pH_VjZDk5

All the best 👍👍

𝐄𝐚𝐫𝐧 𝐅𝐑𝐄𝐄 𝐎𝐫𝐚𝐜𝐥𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 — 𝐂𝐥𝐨𝐮𝐝, 𝐀𝐈 & 𝐃𝐚𝐭𝐚!😍

Oracle’s Race to Certification is here — your chance to earn globally recognized certifications for FREE!💥

💡 Choose from in-demand certifications in:
☁️ Cloud
🤖 AI
📊 Data
…and more!

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4lx2tin

⚡But hurry — spots are limited, and the clock is ticking!✅️

𝗛𝗼𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝗮𝘀𝘁 (𝗘𝘃𝗲𝗻 𝗜𝗳 𝗬𝗼𝘂'𝘃𝗲 𝗡𝗲𝘃𝗲𝗿 𝗖𝗼𝗱𝗲𝗱 𝗕𝗲𝗳𝗼𝗿𝗲!)🐍🚀

Python is everywhere—web dev, data science, automation, AI…
But where should YOU start if you're a beginner?

Don’t worry. Here’s a 6-step roadmap to master Python the smart way (no fluff, just action)👇

🔹 𝗦𝘁𝗲𝗽 𝟭: Learn the Basics (Don’t Skip This!)
✅ Variables, data types (int, float, string, bool)
✅ Loops (for, while), conditionals (if/else)
✅ Functions and user input
Start with:
Python.org Docs
YouTube: Programming with Mosh / CodeWithHarry
Platforms: W3Schools / SoloLearn / FreeCodeCamp
Spend a week here.

Practice > Theory.

🔹 𝗦𝘁𝗲𝗽 𝟮: Automate Boring Stuff (It’s Fun + Useful!)
✅ Rename files in bulk
✅ Auto-fill forms
✅ Web scraping with BeautifulSoup or Selenium
Read: “Automate the Boring Stuff with Python”
It’s beginner-friendly and practical!

🔹 𝗦𝘁𝗲𝗽 𝟯: Build Mini Projects (Your Confidence Booster)
✅ Calculator app
✅ Dice roll simulator
✅ Password generator
✅ Number guessing game

These small projects teach logic, problem-solving, and syntax in action.

🔹 𝗦𝘁𝗲𝗽 𝟰: Dive Into Libraries (Python’s Superpower)
✅ Pandas and NumPy – for data
✅ Matplotlib – for visualizations
✅ Requests – for APIs
✅ Tkinter – for GUI apps
✅ Flask – for web apps

Libraries are what make Python powerful. Learn one at a time with a mini project.

🔹 𝗦𝘁𝗲𝗽 𝟱: Use Git + GitHub (Be a Real Dev)
✅ Track your code with Git
✅ Upload projects to GitHub
✅ Write clear README files
✅ Contribute to open source repos

Your GitHub profile = Your online CV. Keep it active!

🔹 𝗦𝘁𝗲𝗽 𝟲: Build a Capstone Project (Level-Up!)
✅ A weather dashboard (API + Flask)
✅ A personal expense tracker
✅ A web scraper that sends email alerts
✅ A basic portfolio website in Python + Flask

Pick something that solves a real problem—bonus if it helps you in daily life!

🎯 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 = 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗼𝗹𝘃𝗶𝗻𝗴

You don’t need to memorize code. Understand the logic.
Google is your best friend. Practice is your real teacher.

Python Resources: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a

ENJOY LEARNING 👍👍

𝐋𝐞𝐚𝐫𝐧 𝟔 𝐇𝐢𝐠𝐡-𝐈𝐧𝐜𝐨𝐦𝐞 𝐒𝐤𝐢𝐥𝐥𝐬 𝐟𝐨𝐫 𝐅𝐑𝐄𝐄 𝐰𝐢𝐭𝐡 𝐓𝐡𝐞𝐬𝐞 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥𝐬!😍

Want to future-proof your career? The best way to stay ahead is by mastering in-demand tech skills—and the best part? You don’t need to spend a dime!📊〽️

Here are 6 top YouTube channels that offer high-quality, expert-led courses in Graphic Design, DevOps, Data Science, Java, UI/UX, and more!🧑‍🎓✨️

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3XcIsnK

No more excuses—just pure learning and career growth!✅️

Company Name: Waymo
Role : ML Engineer
Batch : 2022/2021 and before passouts

Link: https://careers.withwaymo.com/jobs/ml-compiler-engineer-compute-bengaluru-karnataka-india

𝟒 𝐁𝐞𝐬𝐭 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 𝐭𝐨 𝐒𝐤𝐲𝐫𝐨𝐜𝐤𝐞𝐭 𝐘𝐨𝐮𝐫 𝐂𝐚𝐫𝐞𝐞𝐫😍

In today’s data-driven world, Power BI has become one of the most in-demand tools for businesses〽️📊

The best part? You don’t need to spend a fortune—there are free and affordable courses available online to get you started.💥🧑‍💻

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4mDvgDj

Start learning today and position yourself for success in 2025!✅️

Hiring AI Solution Architect And AI Technical Lead – Full Stack & Enterprise Architecture, Zealogics, fully remote

Job Title: Senior AI Solutions Architect – Generative AI & LLMs
Location: Fully Remote
Experience: 15+ years in enterprise AI architecture and software engineering

Required Skills & Technologies:
Programming: Python, .NET (C#), Node.js, React, Angular
Cloud Platforms: Azure (AI Foundry, OpenAI, DevOps), AWS (Bedrock, SageMaker), GCP
LLMs & GenAI: GPT-4,
AI & ML Tools: Hugging Face, TensorFlow, PyTorch, Keras
DevOps & CI/CD: Azure DevOps, GitHub Actions, Jenkins
Security & Identity: Azure AD, SAML 2.0, Microsoft Purview, Key Vault
Databases: SQL Server, PostgreSQL, Cosmos DB, MongoDB, Pinecone, Chroma
----------------------
Job Title: AI Technical Lead – Full Stack & Enterprise Architecture
📍 Location: India
🕒 Experience Required: 15+ Years
🧑‍💼 Employment Type: Full-Time, fully rmeote
🏢 Department: Technology / AI Solutions

Required Skills & Qualifications
15+ years of experience in software development, with deep expertise in Full Stack technologies (e.g., .NET, React/Angular, Node.js, Python).
Proven experience in architecting enterprise-grade applications and AI integrations.
Hands-on experience with cloud platforms (Azure preferred), microservices, and containerization.
Strong understanding of AI/ML concepts, APIs, and deployment strategies.
Excellent leadership, communication, and stakeholder management skills.
Experience in mentoring teams and driving technical excellence.
Familiarity with compliance standards (e.g., PCI DSS, FedRAMP) is a plus.

If interested, please share your CV with following details to [email protected]:

Full Legal Name
Current Location
Permanent Location
Contact
E-Mail
LinkedIn
Notice
Current CTC
Expected CTC

Hexaware conducting Walkin Drive for AI Engineer and Lead Data Scientist (GenAI)-Hyderabad Location-24th Aug 2025(Sunday)

Interested candidates share your CV at [email protected]

Open Positions:
AI Engineer
Lead Data Scientist (GenAI)

AI Engineer Experience- 3+years
Lead Data Scientist (GenAI) Experience- 7+years
Notice Period- 15 days/30days Max (who serving Notice Period)
Walkin Drive Location- Hyderabad
Date of drive- 24th Aug 2025(Sunday)

Must have Experience:
LLM, Advance RAG, NLP, transformer model, LangChain
Technical Skill:
1. Strong Experience in Data Scientist (GENAI)
2. Proficiency with Generative AI models like GANs, VAEs, and transformers
3. Expertise with cloud platforms (AWS, Azure, Google Cloud) for deploying AI models
4. Strong Python Fast API experience, SDA based implementations for all the APIs
5. Knowledge of Agentic AI concepts and applications

EXL is looking for a Senior Neo4j Developer to join our growing data engineering team!

🧠 Experience Required:
✔️ 10+ years overall in software/data engineering
✔️ 4+ years of hands-on experience with Neo4j
✔️ Strong background in Python and PySpark
✔️ Experience in graph modeling, Cypher queries, and big data pipelines

🌐 Location: Open to all EXL locations [Hybrid]

Join us to build cutting-edge graph-based solutions that solve real-world business problems.

📩 Interested or know someone who might be a great fit? Let’s connect!
Share your resume at [email protected]

𝟳 𝗠𝘂𝘀𝘁-𝗛𝗮𝘃𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Want to land a career in data analytics? 📊💥

It’s not about stacking degrees anymore—it’s about mastering in-demand skills that make you stand out in a competitive job market🧑‍💻📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Uxh5TR

Start small, practice every day, and add these skills to your portfolio✅️

Machine learning powers so many things around us – from recommendation systems to self-driving cars!

But understanding the different types of algorithms can be tricky.

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.

ENJOY LEARNING 👍👍

Data Scientist – Fraud Risk🚀

📍 Hyderabad | Gurgaon | Bangalore

Do you have a passion for fighting fraud with data & machine learning? 💡

We’re looking for Data Scientists / Sr. Data Scientists who love solving complex problems and want to make an impact in the world of Fraud Risk & Analytics.

✨ What You’ll Work On
🔹 Build & deploy advanced ML models to detect and prevent Payment Fraud
🔹 Dive deep into SQL + Python + PySpark to analyze large datasets
🔹 Spot hidden fraud patterns & create smarter prevention strategies
🔹 Collaborate with cross-functional teams to continuously improve detection systems

👩‍💻 What We’re Looking For
✔️ 2.5–5 years’ experience in SQL + ML (Classification & Regression Models)
✔️ Strong skills in Excel, SQL, PySpark & Python
✔️ Hands-on experience in fraud detection models (a big plus!)
✔️ Immediate joiners (or <30 days’ notice) ONLY

📩 Ready to fight fraud with us?
Share your resume at [email protected]

📌SONY is hiring for Machine Learning Role
Experience: 0 - 2 years
Apply here: https://www.linkedin.com/jobs/view/4290651291/

Godrej Capital is hiring Data Scientist 🚀

Experience : 2 Years
Location : Mumbai

Apply link : Check out this job at Godrej Capital: https://www.linkedin.com/jobs/view/4292342820

Step-by-Step Roadmap to Learn Data Science in 2025:

Step 1: Understand the Role
A data scientist in 2025 is expected to:

Analyze data to extract insights

Build predictive models using ML

Communicate findings to stakeholders

Work with large datasets in cloud environments


Step 2: Master the Prerequisite Skills

A. Programming

Learn Python (must-have): Focus on pandas, numpy, matplotlib, seaborn, scikit-learn

R (optional but helpful for statistical analysis)

SQL: Strong command over data extraction and transformation


B. Math & Stats

Probability, Descriptive & Inferential Statistics

Linear Algebra & Calculus (only what's necessary for ML)

Hypothesis testing


Step 3: Learn Data Handling

Data Cleaning, Preprocessing

Exploratory Data Analysis (EDA)

Feature Engineering

Tools: Python (pandas), Excel, SQL


Step 4: Master Machine Learning

Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, XGBoost

Unsupervised Learning: K-Means, Hierarchical Clustering, PCA

Deep Learning (optional): Use TensorFlow or PyTorch

Evaluation Metrics: Accuracy, AUC, Confusion Matrix, RMSE


Step 5: Learn Data Visualization & Storytelling

Python (matplotlib, seaborn, plotly)

Power BI / Tableau

Communicating insights clearly is as important as modeling


Step 6: Use Real Datasets & Projects

Work on projects using Kaggle, UCI, or public APIs

Examples:

Customer churn prediction

Sales forecasting

Sentiment analysis

Fraud detection



Step 7: Understand Cloud & MLOps (2025+ Skills)

Cloud: AWS (S3, EC2, SageMaker), GCP, or Azure

MLOps: Model deployment (Flask, FastAPI), CI/CD for ML, Docker basics


Step 8: Build Portfolio & Resume

Create GitHub repos with well-documented code

Post projects and blogs on Medium or LinkedIn

Prepare a data science-specific resume


Step 9: Apply Smartly

Focus on job roles like: Data Scientist, ML Engineer, Data Analyst → DS

Use platforms like LinkedIn, Glassdoor, Hirect, AngelList, etc.

Practice data science interviews: case studies, ML concepts, SQL + Python coding


Step 10: Keep Learning & Updating

Follow top newsletters: Data Elixir, Towards Data Science

Read papers (arXiv, Google Scholar) on trending topics: LLMs, AutoML, Explainable AI

Upskill with certifications (Google Data Cert, Coursera, DataCamp, Udemy)

Free Resources to learn Data Science

Kaggle Courses: https://www.kaggle.com/learn

CS50 AI by Harvard: https://cs50.harvard.edu/ai/

Fast.ai: https://course.fast.ai/

Google ML Crash Course: https://developers.google.com/machine-learning/crash-course

Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998

Data Science Books: https://t.iss.one/datalemur

React ❤️ for more

🚀🔥 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-610-agentic-ai-certification

React ❤️ for more free resources

We're Hiring - Computer Vision Engineers & Al
Connect
We're expanding our team and looking for skilled
professionals to join us in building intelligent,
real-world solutions
Requirements:
2+ years of hands-on experience in Al/ML or
Computer Vision roles
Strong proficiency in Python
Solid experience with:
Open Cv
Pytourch
Image Classification
YOLO and object detection
Transfer Learning
Machine Learning frameworks and pipelines
Location: Mumbai (On-site)
Working Days: Monday to Friday (Weekends Off)
Votice Period: Immediate to 15 days
We offer a collaborative, innovation-driven work
culture where your contributions directly shape
impactful Al solutions
Send your resume to [email protected]