📊 Data Science Summarized: The Core Pillars of Success! 🚀

✅ 1️⃣ Statistics:
The backbone of data analysis and decision-making.
Used for hypothesis testing, distributions, and drawing actionable insights.

✅ 2️⃣ Mathematics:
Critical for building models and understanding algorithms.
Focus on:
Linear Algebra
Calculus
Probability & Statistics

✅ 3️⃣ Python:
The most widely used language in data science.
Essential libraries include:
Pandas
NumPy
Scikit-Learn
TensorFlow

✅ 4️⃣ Machine Learning:
Use algorithms to uncover patterns and make predictions.
Key types:
Regression
Classification
Clustering

✅ 5️⃣ Domain Knowledge:
Context matters.
Understand your industry to build relevant, useful, and accurate models.

Greetings from PVR Cloud Tech!! 🌈

We will be starting Full Stack Data Engineering on 19th July 2025, from 10:00 AM to 12:00 PM IST (Saturday).

These sessions are exclusively designed for beginners entering the software industry and individuals transitioning from non-IT to IT backgrounds. Data engineers are the backbone of modern businesses.

✅ Course Content :

https://drive.google.com/file/d/1yejI95UAC5DdD2X83Qiu14pnfpUVX6_l/view?usp=sharing

🔥 Interested candidates, please fill out the form below and join the WhatsApp Group.

https://forms.gle/B2JD2ZUvpwfUtPZN6

https://chat.whatsapp.com/Cdr0oDSoaGZIyoIAkmlOAa

https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n

Please share these details with your friends as these sessions may help them transform their careers, and you will be a part of it by providing information.

Thanks,
Team,PVR Cloud Tech
+91-9346060794

𝟲 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍

Want to break into Data Science & Analytics but don’t want to spend on expensive courses?👨‍💻

Start here — with 100% FREE courses from Cisco, IBM, Google & LinkedIn, all with certificates you can showcase on LinkedIn or your resume!📚📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Ix2oxd

This list will set you up with real-world, job-ready skills✅️

𝗖𝗿𝗮𝗰𝗸 𝗙𝗔𝗔𝗡𝗚 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍

If you’re serious about cracking top tech interviews — from FAANG to startups — this is the roadmap you can’t afford to miss🎊

Thousands have used it to land roles at Google, Amazon, Microsoft, and more — completely free🤩📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3TJlpyW

Your dream job might just start here.✅️

Here’s a detailed breakdown of critical roles and their associated responsibilities:


🔘 Data Engineer: Tailored for Data Enthusiasts

1. Data Ingestion: Acquire proficiency in data handling techniques.
2. Data Validation: Master the art of data quality assurance.
3. Data Cleansing: Learn advanced data cleaning methodologies.
4. Data Standardisation: Grasp the principles of data formatting.
5. Data Curation: Efficiently organise and manage datasets.

🔘 Data Scientist: Suited for Analytical Minds

6. Feature Extraction: Hone your skills in identifying data patterns.
7. Feature Selection: Master techniques for efficient feature selection.
8. Model Exploration: Dive into the realm of model selection methodologies.

🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts

9. Coding Proficiency: Develop robust programming skills.
10. Model Training: Understand the intricacies of model training.
11. Model Validation: Explore various model validation techniques.
12. Model Evaluation: Master the art of evaluating model performance.
13. Model Refinement: Refine and improve candidate models.
14. Model Selection: Learn to choose the most suitable model for a given task.

🔘 ML Engineer: Tailored for Deployment Enthusiasts

15. Model Packaging: Acquire knowledge of essential packaging techniques.
16. Model Registration: Master the process of model tracking and registration.
17. Model Containerisation: Understand the principles of containerisation.
18. Model Deployment: Explore strategies for effective model deployment.

These roles encompass diverse facets of Data and ML, catering to various interests and skill sets. Delve into these domains, identify your passions, and customise your learning journey accordingly.

𝟰 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

Want to break into data science in 2025—without spending a single rupee?💰👨‍💻

You’re in luck! Microsoft is offering powerful, beginner-friendly resources that teach you everything from Python fundamentals to AI and data analytics—for free🤩✔️

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/42vCIrb

Level up your career in the booming field of data✅️

𝟰 𝗠𝘂𝘀𝘁-𝗪𝗮𝘁𝗰𝗵 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍

If you’re starting your data analytics journey, these 4 YouTube courses are pure gold — and the best part? 💻🤩

They’re completely free💥💯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/44DvNP1

Each course can help you build the right foundation for a successful tech career✅️

𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤

What it is: A powerful open-source platform designed to automate deploying, scaling, and operating application containers.

𝐂𝐥𝐮𝐬𝐭𝐞𝐫 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭:
- Organizes containers into groups for easier management.
- Automates tasks like scaling and load balancing.

𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫 𝐑𝐮𝐧𝐭𝐢𝐦𝐞:
- Software responsible for launching and managing containers.
- Ensures containers run efficiently and securely.

𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲:
- Implements measures to protect against unauthorized access and malicious activities.
- Includes features like role-based access control and encryption.

𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲:
- Tools to monitor system health, performance, and resource usage.
- Helps identify and troubleshoot issues quickly.

𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐢𝐧𝐠:
- Manages network communication between containers and external systems.
- Ensures connectivity and security between different parts of the system.

𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬:
- Handles tasks related to the underlying infrastructure, such as provisioning and scaling.
- Automates repetitive tasks to streamline operations and improve efficiency.

- 𝐊𝐞𝐲 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬:
- Cluster Management: Handles grouping and managing multiple containers.
- Container Runtime: Software that runs containers and manages their lifecycle.
- Security: Implements measures to protect containers and the overall system.
- Monitoring & Observability: Tools to track and understand system behavior and performance.
- Networking: Manages communication between containers and external networks.
- Infrastructure Operations: Handles tasks like provisioning, scaling, and maintaining the underlying infrastructure.

𝟲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗧𝗼𝗽 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 😍

A power-packed selection of 100% free, certified courses from top institutions:

- Data Analytics – Cisco
- Digital Marketing – Google
- Python for AI – IBM/edX
- SQL & Databases – Stanford
- Generative AI – Google Cloud
- Machine Learning – Harvard

𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 
 
https://pdlink.in/3FcwrZK
 
Master in‑demand tech skills with these 6 certified, top-tier free courses

Join Biggest Telegram channel for data analysts
👇👇
https://t.me/sqlspecialist

🚀 𝟳 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 + 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 😍

Gain globally recognized skills with Microsoft x LinkedIn Career Essentials – completely FREE!

🎯 Top Certifications:
🔹 Generative AI
🔹 Data Analysis
🔹 Software Development
🔹 Project Management
🔹 Business Analysis
🔹 System Administration
🔹 Administrative Assistance

📚 100% Free | Self-Paced | Industry-Aligned

𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 
 
https://pdlink.in/46TZP2h
 
💼 Perfect for students, freshers & working professionals

Netflix Analytics Engineer Interview Question (SQL) 🚀
---

### Scenario Overview
Netflix wants to analyze user engagement with their platform. Imagine you have a table called netflix_data with the following columns:
- user_id: Unique identifier for each user
- subscription_plan: Type of subscription (e.g., Basic, Standard, Premium)
- genre: Genre of the content the user watched (e.g., Drama, Comedy, Action)
- timestamp: Date and time when the user watched a show
- watch_duration: Length of time (in minutes) a user spent watching
- country: User’s country

The main objective is to figure out how to get insights into user behavior, such as which genres are most popular or how watch duration varies across subscription plans.

---

### Typical Interview Question

> “Using the netflix_data table, find the top 3 genres by average watch duration in each subscription plan, and return both the genre and the average watch duration.”

This question tests your ability to:
1. Filter or group data by subscription plan.
2. Calculate average watch duration within each group.
3. Sort results to find the “top 3” within each group.
4. Handle tie situations or edge cases (e.g., if there are fewer than 3 genres).

---

### Step-by-Step Approach

1. Group and Aggregate
Use the GROUP BY clause to group by subscription_plan and genre. Then, use an aggregate function like AVG(watch_duration) to get the average watch time for each combination.

2. Rank Genres
You can utilize a window function—commonly ROW_NUMBER() or RANK()—to assign a ranking to each genre within its subscription plan, based on the average watch duration. For example:

   AVG(watch_duration) OVER (PARTITION BY subscription_plan ORDER BY AVG(watch_duration) DESC)
   

(Note that in many SQL dialects, you’ll need a subquery because you can’t directly apply an aggregate in the ORDER BY of a window function.)

3. Select Top 3
After ranking rows in each partition (i.e., subscription plan), pick only the top 3 by watch duration. This could look like:

   SELECT subscription_plan,
          genre,
          avg_watch_duration
   FROM (
       SELECT subscription_plan,
              genre,
              AVG(watch_duration) AS avg_watch_duration,
              ROW_NUMBER() OVER (
                  PARTITION BY subscription_plan 
                  ORDER BY AVG(watch_duration) DESC
              ) AS rn
       FROM netflix_data
       GROUP BY subscription_plan, genre
   ) ranked
   WHERE rn <= 3;
   

4. Validate Results
- Make sure each subscription plan returns up to 3 genres.
- Check for potential ties. Depending on the question, you might use RANK() or DENSE_RANK() to handle ties differently.
- Confirm the data type and units for watch_duration (minutes, seconds, etc.).

---

### Key Takeaways
- Window Functions: Essential for ranking or partitioning data.
- Aggregations & Grouping: A foundational concept for Analytics Engineers.
- Data Validation: Always confirm you’re interpreting columns (like watch_duration) correctly.

By mastering these techniques, you’ll be better prepared for SQL interview questions that delve into real-world scenarios—especially at a data-driven company like Netflix.

𝗧𝗶𝗿𝗲𝗱 𝗼𝗳 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗳𝗶𝗻𝗱 𝗴𝗼𝗼𝗱 𝗔𝗜/𝗠𝗟 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲?😍

Stop wasting hours searching — here’s a GOLDMINE 💎

✅ 500+ Real-World Projects with Code
✅ Covers NLP, Computer Vision, Deep Learning, ML Pipelines
✅ Beginner to Advanced Levels
✅ Resume-Worthy, Interview-Ready!

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/45gTMU8

✨Save this. Share this. Start building.✅️