📌 🚀 How to Build a Personal Brand as a Data Analyst

Want to stand out in the competitive job market? Build your personal brand using these strategies:

✅ 1. Share Your Work Publicly – Post SQL/Python projects on LinkedIn, Medium, or GitHub.

✅ 2. Engage with Data Communities – Follow & contribute to Kaggle, DataCamp, or Analytics Vidhya.

✅ 3. Write About Data – Share blog posts on real-world data insights & case studies.

✅ 4. Present at Meetups/Webinars – Gain visibility & network with industry experts.

✅ 5. Optimize LinkedIn & GitHub – Highlight your skills, certifications, and projects.


💡 Start with one personal branding activity this week.

Q: How do you import data from various sources (Excel, SQL Server, CSV) into Power BI?

A: Here’s how to handle multi-source imports in Power BI Desktop:

1. Excel:

° Go to Home > Get Data > Excel

° Select your file & sheets or tables



2. CSV:

° Choose Get Data > Text/CSV

° Browse and load the file



3. SQL Server:

° Select Get Data > SQL Server

° Enter server/database name

° Use a query or select tables directly



4. Combine Sources:

° Use Power Query to transform, merge, or append tables

° Create relationships in the Model view


Pro Tip:
Use consistent data types and naming to make transformations smoother across sources!

ChatGPT Prompt to learn any skill
👇👇
I am seeking to become an expert professional in [Making ChatGPT prompts perfectly]. I would like ChatGPT to provide me with a complete course on this subject, following the principles of Pareto principle and simulating the complexity, structure, duration, and quality of the information found in a college degree program at a prestigious university. The course should cover the following aspects: Course Duration: The course should be structured as a comprehensive program, spanning a duration equivalent to a full-time college degree program, typically four years. Curriculum Structure: The curriculum should be well-organized and divided into semesters or modules, progressing from beginner to advanced levels of proficiency. Each semester/module should have a logical flow and build upon the previous knowledge. Relevant and Accurate Information: The course should provide all the necessary and up-to-date information required to master the skill or knowledge area. It should cover both theoretical concepts and practical applications. Projects and Assignments: The course should include a series of hands-on projects and assignments that allow me to apply the knowledge gained. These projects should range in complexity, starting from basic exercises and gradually advancing to more challenging real-world applications. Learning Resources: ChatGPT should share a variety of learning resources, including textbooks, research papers, online tutorials, video lectures, practice exams, and any other relevant materials that can enhance the learning experience. Expert Guidance: ChatGPT should provide expert guidance throughout the course, answering questions, providing clarifications, and offering additional insights to deepen understanding. I understand that ChatGPT's responses will be generated based on the information it has been trained on and the knowledge it has up until September 2021. However, I expect the course to be as complete and accurate as possible within these limitations. Please provide the course syllabus, including a breakdown of topics to be covered in each semester/module, recommended learning resources, and any other relevant information

(Tap on above text to copy)

🚀 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 👍👍