🚀 PyTorch vs TensorFlow – Which Should YOU Choose?

If you’re starting in AI or planning to build real-world apps, this is the big question.

👉 PyTorch – simple, feels like Python, runs instantly. Perfect for learning, experiments, and research.
👉 TensorFlow – built by Google, comes with a full production toolkit (mobile, web, cloud). Perfect for apps at scale.

✨ Developer Experience: PyTorch is beginner-friendly. TensorFlow has improved with Keras but still leans towards production use.
📊 Research vs Production: 75% of research papers use PyTorch, but TensorFlow powers large-scale deployments.

💡 Think of it like this:
PyTorch = Notebook for experiments ✍️
TensorFlow = Office suite for real apps 🏢

So the choice is simple:

Learning & Research → PyTorch

Scaling & Deployment → TensorFlow

Amazon Interview Process for Data Scientist position

📍Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.

After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).

📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵:
In this round the interviewer tested my knowledge on different kinds of topics.

📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.

📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱-
This was a Python coding round, which I cleared successfully.

📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed.

📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.

So, here are my Tips if you’re targeting any Data Science role:
-> Never make up stuff & don’t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)

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

ENJOY LEARNING 👍👍

🚀 Walk-in Hiring Drive Alert! 🚀

AccioJob x Sceniuz are hiring for Data Analyst & Data Engineer roles!

* Graduation Year: Open to All
* Degree: BTech / BE / BCA / BSC / MTech /ME / MCA / MSC
* CTC: 3–6 LPA
* Offline Assesment at AccioJob partnered campus in Mumbai

👉🏻 Data Analyst: https://go.acciojob.com/47HSHh
👉🏻 Data Engineer: https://go.acciojob.com/PnRTK2

Greetings from PVR Cloud Tech!! 🌈

🚀 Kickstart Your Career in Azure Data Engineering – The Smart Way in 2025!

📌 Start Date: 15th September 2025

⏰ Time: 9 PM – 10 PM IST | Monday

🔹 Course Content:

https://drive.google.com/file/d/1YufWV0Ru6SyYt-oNf5Mi5H8mmeV_kfP-/view

📱 Join WhatsApp Group:

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Team PVR Cloud Tech :)
+91-9346060794

🚀 Step-by-Step Guide to Become a Data Engineer in 2025 🛠️📂

1️⃣ Start with Programming Basics 
Learn Python or Java — essential for scripting, automation & handling data.

2️⃣ Understand Databases 
Master SQL for querying, plus NoSQL (MongoDB, Cassandra) for unstructured data.

3️⃣ Learn Data Warehousing 
Get comfy with ETL, OLAP, Star/Snowflake schemas. Tools: Snowflake, Redshift, BigQuery.

4️⃣ Work with Big Data Tools 
Explore Hadoop, Spark, Kafka — key for large-scale data processing.

5️⃣ Get Hands-On with Cloud Platforms 
Focus on AWS, Azure, or GCP — master data services like S3, Lambda, Glue, BigQuery.

6️⃣ Practice Building Data Pipelines 
Use Apache Airflow, dbt, or Prefect to build and orchestrate workflows end-to-end.

7️⃣ Version Control & CI/CD 
Learn GitHub, Docker, Jenkins for collaboration and deployment.

8️⃣ Build a Strong Portfolio 
Show off pipeline projects, cloud workflows, and architecture diagrams.

9️⃣ Apply for Data Engineering Roles 
Look for titles like Data Engineer, ETL Developer, Cloud Data Engineer.

🔟 Keep Growing & Learning 
Dive into real-time streaming, data security, optimization, and advanced data modeling.

——————————

🔥 In 2025, top skills include cloud computing, big data, ETL, programming (Python/Java), and data warehousing. Focus where demand is highest!

💡 Start small, build projects, experiment on free cloud tiers, and stay updated with emerging tech.

💬 Tap ❤️ for more!

⌨️ QR code generation in Python

Top 5 Data Science Data Terms

Big Data 5V

Stop obsessing over Python and SQL skills.

Here are 5 non-technical skills that make exceptional data analysts:

- Business Acumen
Understand the industry you're in. Know your company's goals, challenges, and KPIs. Your analyses should drive business decisions, not just process data.

- Storytelling
Data without context is just noise. Learn to craft compelling narratives around your insights. Use analogies, visuals, and clear language to make complex data accessible.

- Stakeholder Management
Navigate office politics and build relationships. Know how to manage expectations, handle difficult personalities, and align your work with stakeholders' priorities.

- Problem-Solving
Develop ability for identifying the real problem behind the data request. Often, the question asked isn’t the one that truly needs solving. It’s your job as a data analyst to dig deeper, challenge assumptions, and uncover the actual business challenge.

Technical skills may get you started, but it’s the soft skills that truly advance your career. These are the skills that turn a good analyst into an essential part of the team.

The best data analysts aren't just number crunchers - they guide the strategy that drives the business forward.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊