Being a Generalist Data Scientist won't get you hired.
Here is how you can specialize 👇
Companies have specific problems that require certain skills to solve. If you do not know which path you want to follow. Start broad first, explore your options, then specialize.
To discover what you enjoy the most, try answering different questions for each DS role:
- 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Qs:
“How should we monitor model performance in production?”
- 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 / 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
Qs:
“How can we visualize customer segmentation to highlight key demographics?”
- 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
Qs:
“How can we use clustering to identify new customer segments for targeted marketing?”
- 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫
Qs:
“What novel architectures can we explore to improve model robustness?”
- 𝐌𝐋𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Qs:
“How can we automate the deployment of machine learning models to ensure continuous integration and delivery?”
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
Here is how you can specialize 👇
Companies have specific problems that require certain skills to solve. If you do not know which path you want to follow. Start broad first, explore your options, then specialize.
To discover what you enjoy the most, try answering different questions for each DS role:
- 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Qs:
“How should we monitor model performance in production?”
- 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 / 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
Qs:
“How can we visualize customer segmentation to highlight key demographics?”
- 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
Qs:
“How can we use clustering to identify new customer segments for targeted marketing?”
- 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫
Qs:
“What novel architectures can we explore to improve model robustness?”
- 𝐌𝐋𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
Qs:
“How can we automate the deployment of machine learning models to ensure continuous integration and delivery?”
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
❤4🕊1
ChatGPT 5 is finally out 🔥
ChatGPT 5 launch is now live & ChatGPT 5 FREE for Everyone!
Free users get PhD-level intelligence.
Plus = higher limits.
Pro = GPT-5 Pro.
Making this available to everyone is a big step forward.
Watch launch:
https://www.youtube.com/live/0Uu_VJeVVfo?si=h9ecIUIRDFsyb50A
ChatGPT 5 launch is now live & ChatGPT 5 FREE for Everyone!
Free users get PhD-level intelligence.
Plus = higher limits.
Pro = GPT-5 Pro.
Making this available to everyone is a big step forward.
Watch launch:
https://www.youtube.com/live/0Uu_VJeVVfo?si=h9ecIUIRDFsyb50A
YouTube
Introducing GPT-5
Sam Altman, Greg Brockman, Sebastien Bubeck, Mark Chen, Yann Dubois, Brian Fioca, Adi Ganesh, Oliver Godement, Saachi Jain, Christina Kaplan, Christina Kim, Elaine Ya Le, Felipe Millon, Michelle Pokrass, Jakub Pachocki, Max Schwarzer, Rennie Song, Ruochen…
❤3
Save this guide for later!
Here are 8 ChatGPT-4o prompts you must know to succeed in your business:
1. Lean Startup Methodology
Prompt:
2. Value Proposition Canvas
Prompt:
3. OKRs (Objectives and Key Results)
Prompt:
4. PEST Analysis
Prompt:
5. The Five Whys
Prompt:
6. Customer Journey Mapping
Prompt:
7. Business Model Canvas
Prompt:
8. Growth Hacking Strategies
Prompt:
OpenAI’s latest model, GPT-4o, is now available to all free users. This new AI model accepts any combination of text, audio, image, and video as input and generates any combination of text, audio, and image outputs. To make the most of GPT-4o’s capabilities, users can leverage prompts tailored to specific tasks and goals.
Here are 8 ChatGPT-4o prompts you must know to succeed in your business:
1. Lean Startup Methodology
Prompt:
ChatGPT, how can I apply the Lean Startup Methodology to quickly test and validate my [business idea/product]?2. Value Proposition Canvas
Prompt:
ChatGPT, help me create a Value Proposition Canvas for [your product/service] to better understand and meet customer needs.3. OKRs (Objectives and Key Results)
Prompt:
ChatGPT, guide me in setting up OKRs for [your business/project] to align team goals and drive performance.4. PEST Analysis
Prompt:
ChatGPT, conduct a PEST analysis for [your industry] to identify external factors affecting my business.5. The Five Whys
Prompt:
ChatGPT, use the Five Whys technique to identify the root cause of [specific problem] in my business.6. Customer Journey Mapping
Prompt:
ChatGPT, help me create a customer journey map for [your product/service] to improve user experience and satisfaction.7. Business Model Canvas
Prompt:
ChatGPT, guide me through filling out a Business Model Canvas for [your business] to clarify and refine my business model.8. Growth Hacking Strategies
Prompt:
ChatGPT, suggest some growth hacking strategies to rapidly expand my customer base for [your product/service].❤3👍1
🔴 How to MASTER a programming language using ChatGPT: 📌
1. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
2. What are some commonly asked interview questions about [lang]?
3. What are the advanced topics to learn in [lang]? Explain them to me with code examples.
4. Give me some practice questions along with solutions for [concept] in [lang].
5. What are some common mistakes that people make in [lang]?
6. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
7. How can I optimize the performance of my code in [lang]?
8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts?
9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively?
10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues?
11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]?
12. How can I effectively collaborate with other developers in [lang] on a project?
13. What are some common data structures and algorithms that I should be familiar with in [lang]?
Join for more: https://news.1rj.ru/str/AI_Best_Tools
1. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
2. What are some commonly asked interview questions about [lang]?
3. What are the advanced topics to learn in [lang]? Explain them to me with code examples.
4. Give me some practice questions along with solutions for [concept] in [lang].
5. What are some common mistakes that people make in [lang]?
6. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
7. How can I optimize the performance of my code in [lang]?
8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts?
9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively?
10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues?
11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]?
12. How can I effectively collaborate with other developers in [lang] on a project?
13. What are some common data structures and algorithms that I should be familiar with in [lang]?
Join for more: https://news.1rj.ru/str/AI_Best_Tools
❤3🔥1
Complete 3-months roadmap to learn Artificial Intelligence (AI) 👇👇
### Month 1: Fundamentals of AI and Python
Week 1: Introduction to AI
- Key Concepts: What is AI? Categories (Narrow AI, General AI, Super AI), Applications of AI.
- Reading: Research papers and articles on AI.
- Task: Watch introductory AI videos (e.g., Andrew Ng's "What is AI?" on Coursera).
Week 2: Python for AI
- Skills: Basics of Python programming (variables, loops, conditionals, functions, OOP).
- Resources: Python tutorials (W3Schools, Real Python).
- Task: Write simple Python noscripts.
Week 3: Libraries for AI
- Key Libraries: NumPy, Pandas, Matplotlib, Scikit-learn.
- Task: Install libraries and practice data manipulation and visualization.
- Resources: Documentation and tutorials on these libraries.
Week 4: Linear Algebra and Probability
- Key Topics: Matrices, Vectors, Eigenvalues, Probability theory.
- Resources: Khan Academy (Linear Algebra), MIT OCW.
- Task: Solve basic linear algebra problems and write Python functions to implement them.
---
### Month 2: Core AI Techniques & Machine Learning
Week 5: Machine Learning Basics
- Key Concepts: Supervised, Unsupervised learning, Model evaluation metrics.
- Algorithms: Linear Regression, Logistic Regression.
- Task: Build basic models using Scikit-learn.
- Resources: Coursera’s Machine Learning by Andrew Ng, Kaggle datasets.
Week 6: Decision Trees, Random Forests, and KNN
- Key Concepts: Decision Trees, Random Forests, K-Nearest Neighbors (KNN).
- Task: Implement these algorithms and analyze their performance.
- Resources: Hands-on Machine Learning with Scikit-learn.
Week 7: Neural Networks & Deep Learning
- Key Concepts: Artificial Neurons, Forward and Backpropagation, Activation Functions.
- Framework: TensorFlow, Keras.
- Task: Build a simple neural network for a classification problem.
- Resources: Fast.ai, Coursera Deep Learning Specialization by Andrew Ng.
Week 8: Convolutional Neural Networks (CNN)
- Key Concepts: Image classification, Convolution, Pooling.
- Task: Build a CNN using Keras/TensorFlow to classify images (e.g., CIFAR-10 dataset).
- Resources: CS231n Stanford Course, Fast.ai Computer Vision.
---
### Month 3: Advanced AI Techniques & Projects
Week 9: Natural Language Processing (NLP)
- Key Concepts: Tokenization, Embeddings, Sentiment Analysis.
- Task: Implement text classification using NLTK/Spacy or transformers.
- Resources: Hugging Face, Coursera NLP courses.
Week 10: Reinforcement Learning
- Key Concepts: Q-learning, Markov Decision Processes (MDP), Policy Gradients.
- Task: Solve a simple RL problem (e.g., OpenAI Gym).
- Resources: Sutton and Barto’s book on Reinforcement Learning, OpenAI Gym.
Week 11: AI Model Deployment
- Key Concepts: Model deployment using Flask/Streamlit, Model Serving.
- Task: Deploy a trained model using Flask API or Streamlit.
- Resources: Heroku deployment guides, Streamlit documentation.
Week 12: AI Capstone Project
- Task: Create a full-fledged AI project (e.g., Image recognition app, Sentiment analysis, or Chatbot).
- Presentation: Prepare and document your project.
- Goal: Deploy your AI model and share it on GitHub/Portfolio.
### Tools and Platforms:
- Python IDE: Jupyter, PyCharm, or VSCode.
- Datasets: Kaggle, UCI Machine Learning Repository.
- Version Control: GitHub or GitLab for managing code.
Free Books and Courses to Learn Artificial Intelligence👇👇
Introduction to AI for Business Free Course
Top Platforms for Building Data Science Portfolio
Artificial Intelligence: Foundations of Computational Agents Free Book
Learn Basics about AI Free Udemy Course
Amazing AI Reverse Image Search
By following this roadmap, you’ll gain a strong understanding of AI concepts and practical skills in Python, machine learning, and neural networks.
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
### Month 1: Fundamentals of AI and Python
Week 1: Introduction to AI
- Key Concepts: What is AI? Categories (Narrow AI, General AI, Super AI), Applications of AI.
- Reading: Research papers and articles on AI.
- Task: Watch introductory AI videos (e.g., Andrew Ng's "What is AI?" on Coursera).
Week 2: Python for AI
- Skills: Basics of Python programming (variables, loops, conditionals, functions, OOP).
- Resources: Python tutorials (W3Schools, Real Python).
- Task: Write simple Python noscripts.
Week 3: Libraries for AI
- Key Libraries: NumPy, Pandas, Matplotlib, Scikit-learn.
- Task: Install libraries and practice data manipulation and visualization.
- Resources: Documentation and tutorials on these libraries.
Week 4: Linear Algebra and Probability
- Key Topics: Matrices, Vectors, Eigenvalues, Probability theory.
- Resources: Khan Academy (Linear Algebra), MIT OCW.
- Task: Solve basic linear algebra problems and write Python functions to implement them.
---
### Month 2: Core AI Techniques & Machine Learning
Week 5: Machine Learning Basics
- Key Concepts: Supervised, Unsupervised learning, Model evaluation metrics.
- Algorithms: Linear Regression, Logistic Regression.
- Task: Build basic models using Scikit-learn.
- Resources: Coursera’s Machine Learning by Andrew Ng, Kaggle datasets.
Week 6: Decision Trees, Random Forests, and KNN
- Key Concepts: Decision Trees, Random Forests, K-Nearest Neighbors (KNN).
- Task: Implement these algorithms and analyze their performance.
- Resources: Hands-on Machine Learning with Scikit-learn.
Week 7: Neural Networks & Deep Learning
- Key Concepts: Artificial Neurons, Forward and Backpropagation, Activation Functions.
- Framework: TensorFlow, Keras.
- Task: Build a simple neural network for a classification problem.
- Resources: Fast.ai, Coursera Deep Learning Specialization by Andrew Ng.
Week 8: Convolutional Neural Networks (CNN)
- Key Concepts: Image classification, Convolution, Pooling.
- Task: Build a CNN using Keras/TensorFlow to classify images (e.g., CIFAR-10 dataset).
- Resources: CS231n Stanford Course, Fast.ai Computer Vision.
---
### Month 3: Advanced AI Techniques & Projects
Week 9: Natural Language Processing (NLP)
- Key Concepts: Tokenization, Embeddings, Sentiment Analysis.
- Task: Implement text classification using NLTK/Spacy or transformers.
- Resources: Hugging Face, Coursera NLP courses.
Week 10: Reinforcement Learning
- Key Concepts: Q-learning, Markov Decision Processes (MDP), Policy Gradients.
- Task: Solve a simple RL problem (e.g., OpenAI Gym).
- Resources: Sutton and Barto’s book on Reinforcement Learning, OpenAI Gym.
Week 11: AI Model Deployment
- Key Concepts: Model deployment using Flask/Streamlit, Model Serving.
- Task: Deploy a trained model using Flask API or Streamlit.
- Resources: Heroku deployment guides, Streamlit documentation.
Week 12: AI Capstone Project
- Task: Create a full-fledged AI project (e.g., Image recognition app, Sentiment analysis, or Chatbot).
- Presentation: Prepare and document your project.
- Goal: Deploy your AI model and share it on GitHub/Portfolio.
### Tools and Platforms:
- Python IDE: Jupyter, PyCharm, or VSCode.
- Datasets: Kaggle, UCI Machine Learning Repository.
- Version Control: GitHub or GitLab for managing code.
Free Books and Courses to Learn Artificial Intelligence👇👇
Introduction to AI for Business Free Course
Top Platforms for Building Data Science Portfolio
Artificial Intelligence: Foundations of Computational Agents Free Book
Learn Basics about AI Free Udemy Course
Amazing AI Reverse Image Search
By following this roadmap, you’ll gain a strong understanding of AI concepts and practical skills in Python, machine learning, and neural networks.
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
❤3🥰1
If you aspire to work in top product companies, here’s my advice:
👉 For SDE-1 or SWE positions, focus on:
✔️ Continuously upskilling and improving your abilities.
✔️ Developing strong problem-solving skills.
✔️Mastering DSA – trust me, you’ll be tested on it, so aim to excel.
Also, learn how to design scalable systems and understand how to build solutions that can handle growth in users and data.
👉 For higher-level roles (SDE-2 and SDE-3), focus on:
✔️ DSA + System Design (both LLD and HLD).
✔️ Building your leadership skills, as you’ll need to lead teams and projects.
🔸I know it’s challenging to do this while working full-time, but you’ll need to carve out time to consistently upskill yourself.
Remember, your learning plan should be sensible and well-organized.
Best Programming Resources: https://topmate.io/coding/886839
ENJOY LEARNING 👍👍
👉 For SDE-1 or SWE positions, focus on:
✔️ Continuously upskilling and improving your abilities.
✔️ Developing strong problem-solving skills.
✔️Mastering DSA – trust me, you’ll be tested on it, so aim to excel.
Also, learn how to design scalable systems and understand how to build solutions that can handle growth in users and data.
👉 For higher-level roles (SDE-2 and SDE-3), focus on:
✔️ DSA + System Design (both LLD and HLD).
✔️ Building your leadership skills, as you’ll need to lead teams and projects.
🔸I know it’s challenging to do this while working full-time, but you’ll need to carve out time to consistently upskill yourself.
Remember, your learning plan should be sensible and well-organized.
Best Programming Resources: https://topmate.io/coding/886839
ENJOY LEARNING 👍👍
❤2
For working professionals willing to pivot their careers to AI:
Here are the steps you can take right now:
1. Learn the basics of AI
==================
You need to understand the differences among various AI jargons (e.g., what is the difference between statistical ML vs. deep learning? What exactly is an LLM?) and when to use which to solve a given business problem. Many fast-paced courses can teach you all of this without having to learn coding. (Shameless plug: I have a course that I will add in the comments section below)
2. Build an AI project in your current work
==============================
Find a problem statement in your current work that can be solved using AI and will deliver some value. Work on this during your extra hours, then showcase it to your management to get official approval to make it a full-fledged project.
3. Collaborate with the AI team in your company for inner sourcing
================================================
Many companies have the concept of inner sourcing where, say, an AI team is too busy and has a list of tasks they have opened on their GitHub repository that others can work on. Use this as an opportunity to do some real AI work and build rapport with the AI team.
4. Attend AI conferences
==================
By attending AI conferences, you will not only learn but also build a network with AI professionals who will help you in your AI career journey.
5. Attend an AI bootcamp at a university or online learning company
=================================================
Artificial Intelligence
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
Like for more ❤️
All the best 👍👍
Here are the steps you can take right now:
1. Learn the basics of AI
==================
You need to understand the differences among various AI jargons (e.g., what is the difference between statistical ML vs. deep learning? What exactly is an LLM?) and when to use which to solve a given business problem. Many fast-paced courses can teach you all of this without having to learn coding. (Shameless plug: I have a course that I will add in the comments section below)
2. Build an AI project in your current work
==============================
Find a problem statement in your current work that can be solved using AI and will deliver some value. Work on this during your extra hours, then showcase it to your management to get official approval to make it a full-fledged project.
3. Collaborate with the AI team in your company for inner sourcing
================================================
Many companies have the concept of inner sourcing where, say, an AI team is too busy and has a list of tasks they have opened on their GitHub repository that others can work on. Use this as an opportunity to do some real AI work and build rapport with the AI team.
4. Attend AI conferences
==================
By attending AI conferences, you will not only learn but also build a network with AI professionals who will help you in your AI career journey.
5. Attend an AI bootcamp at a university or online learning company
=================================================
Artificial Intelligence
👉Telegram Link: https://news.1rj.ru/str/addlist/4q2PYC0pH_VjZDk5
Like for more ❤️
All the best 👍👍
❤2
CHATGPT TOOLS
GPT Store in ChaGPT
Browse hundreds of tools developers have created.
TopGPT
Directory of GPTs
Awesome GPT4
A collection of demos, use cases
ChatGPT for Google
Browser extension that allows ChatGPT to work alongside Google.
GPTGo
Free ChatGPT and search engine
GPT Store in ChaGPT
Browse hundreds of tools developers have created.
TopGPT
Directory of GPTs
Awesome GPT4
A collection of demos, use cases
ChatGPT for Google
Browser extension that allows ChatGPT to work alongside Google.
GPTGo
Free ChatGPT and search engine
Which AI is better — with a wrapper or without?
I liked a slide from the recent presentation Sequoia Capital AI Ascent 2025.
Big vendors after the launch of ChatGPT insisted that niche and industrial startups consuming AGI intelligence are unpromising.
They disdainfully called them AI wrappers, believing that they have no protection against competitors.
A couple of years later, we observe record growth precisely in such companies as Cursor ($300 million ARR), Loveable, Windsurf (bought by OpenAI for $3 billion), and thousands of new industrial startups — in finance, insurance, e-commerce, legal, accounting, healthcare, and other sectors.
Meanwhile, AI tokens are becoming the fastest depreciating technology and currency in history.
According to Sam Altman himself, over time the cost of AI will equal the cost of energy.
Giants have started giving access to their most powerful AI models to seize leadership in the new technological era.
I have already written about a unique market moment: when, being an expert in any subject area, you combine your knowledge with the growing capabilities of AI — and get a tool that solves real human problems for which clients are ready to pay immediately.
Now these very AI wrappers have turned into serious businesses.
The obvious challenge for entrepreneurs is that AI skills and specialists are increasing in value much faster than other segments of the IT market.
Startups and mature companies with subject matter experts already face difficulties attracting strong AI engineers, but at the same time, according to the results of the first internal hackathons, I see a rapid increase in the number of those who caught the trend and felt in AI a breath of fresh air in the enterprise world.
In the coming years, young founders and enthusiasts who live by technology, see trends ahead, and tirelessly experiment by creating new products will win rapidly.
Time to act!
I liked a slide from the recent presentation Sequoia Capital AI Ascent 2025.
Big vendors after the launch of ChatGPT insisted that niche and industrial startups consuming AGI intelligence are unpromising.
They disdainfully called them AI wrappers, believing that they have no protection against competitors.
A couple of years later, we observe record growth precisely in such companies as Cursor ($300 million ARR), Loveable, Windsurf (bought by OpenAI for $3 billion), and thousands of new industrial startups — in finance, insurance, e-commerce, legal, accounting, healthcare, and other sectors.
Meanwhile, AI tokens are becoming the fastest depreciating technology and currency in history.
According to Sam Altman himself, over time the cost of AI will equal the cost of energy.
Giants have started giving access to their most powerful AI models to seize leadership in the new technological era.
I have already written about a unique market moment: when, being an expert in any subject area, you combine your knowledge with the growing capabilities of AI — and get a tool that solves real human problems for which clients are ready to pay immediately.
Now these very AI wrappers have turned into serious businesses.
The obvious challenge for entrepreneurs is that AI skills and specialists are increasing in value much faster than other segments of the IT market.
Startups and mature companies with subject matter experts already face difficulties attracting strong AI engineers, but at the same time, according to the results of the first internal hackathons, I see a rapid increase in the number of those who caught the trend and felt in AI a breath of fresh air in the enterprise world.
In the coming years, young founders and enthusiasts who live by technology, see trends ahead, and tirelessly experiment by creating new products will win rapidly.
Time to act!
❤2🔥1
Power BI Interview Questions for Entry-Level Data Analysts (Easy-Medium Difficulty) 📊
1. What is Power BI, and how does it fit into the data analysis workflow?
2. Difference between Power BI Desktop and Power BI Service?
3. How to import data into Power BI? What are the various data sources supported?
4. Explain the process of transforming data in Power BI. Which tools or features would you use for data cleaning?
5. What is data modeling in Power BI, and why is it important?
6. How would you create relationships between different tables in Power BI?
7. Explain cardinality and its significance?
8. Describe the steps to create a basic report/dashboard in Power BI?
9. What are best practices for creating effective visualizations in Power BI?
10. What is DAX, and why is it used in Power BI?
11. DAX formulas to calculate a new measure or column?
12. How does data refresh work in Power BI? What options are available for scheduling data refreshes?
13. Process of publishing a Power BI report to the Power BI service?
14. If a Power BI report is loading slowly, what steps would you take to identify and rectify the issue?
15. How do you optimize Power BI reports for better performance?
I have curated the best interview resources to crack Power BI Interviews 👇👇
https://topmate.io/analyst/866125
Hope you'll like it
Like this post if you need more resources like this 👍❤️
1. What is Power BI, and how does it fit into the data analysis workflow?
2. Difference between Power BI Desktop and Power BI Service?
3. How to import data into Power BI? What are the various data sources supported?
4. Explain the process of transforming data in Power BI. Which tools or features would you use for data cleaning?
5. What is data modeling in Power BI, and why is it important?
6. How would you create relationships between different tables in Power BI?
7. Explain cardinality and its significance?
8. Describe the steps to create a basic report/dashboard in Power BI?
9. What are best practices for creating effective visualizations in Power BI?
10. What is DAX, and why is it used in Power BI?
11. DAX formulas to calculate a new measure or column?
12. How does data refresh work in Power BI? What options are available for scheduling data refreshes?
13. Process of publishing a Power BI report to the Power BI service?
14. If a Power BI report is loading slowly, what steps would you take to identify and rectify the issue?
15. How do you optimize Power BI reports for better performance?
I have curated the best interview resources to crack Power BI Interviews 👇👇
https://topmate.io/analyst/866125
Hope you'll like it
Like this post if you need more resources like this 👍❤️
❤3
Frontend Development Interview Questions
Beginner Level
1. What are semantic HTML tags?
2. Difference between id and class in HTML?
3. What is the Box Model in CSS?
4. Difference between margin and padding?
5. What is a responsive web design?
6. What is the use of the <meta viewport> tag?
7. Difference between inline, block, and inline-block elements?
8. What is the difference between == and === in JavaScript?
9. What are arrow functions in JavaScript?
10. What is DOM and how is it used?
Intermediate Level
1. What are pseudo-classes and pseudo-elements in CSS?
2. How do media queries work in responsive design?
3. Difference between relative, absolute, fixed, and sticky positioning?
4. What is the event loop in JavaScript?
5. Explain closures in JavaScript with an example.
6. What are Promises and how do you handle errors with .catch()?
7. What is a higher-order function?
8. What is the difference between localStorage and sessionStorage?
9. How does this keyword work in different contexts?
10. What is JSX in React?
Advanced Level
1. How does the virtual DOM work in React?
2. What are controlled vs uncontrolled components in React?
3. What is useMemo and when should you use it?
4. How do you optimize a large React app for performance?
5. What are React lifecycle methods (class-based) and their hook equivalents?
6. How does Redux work and when should you use it?
7. What is code splitting and why is it useful?
8. How do you secure a frontend app from XSS attacks?
9. Explain the concept of Server-Side Rendering (SSR) vs Client-Side Rendering (CSR).
10. What are Web Components and how do they work?
React ❤️ for the detailed answers
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Beginner Level
1. What are semantic HTML tags?
2. Difference between id and class in HTML?
3. What is the Box Model in CSS?
4. Difference between margin and padding?
5. What is a responsive web design?
6. What is the use of the <meta viewport> tag?
7. Difference between inline, block, and inline-block elements?
8. What is the difference between == and === in JavaScript?
9. What are arrow functions in JavaScript?
10. What is DOM and how is it used?
Intermediate Level
1. What are pseudo-classes and pseudo-elements in CSS?
2. How do media queries work in responsive design?
3. Difference between relative, absolute, fixed, and sticky positioning?
4. What is the event loop in JavaScript?
5. Explain closures in JavaScript with an example.
6. What are Promises and how do you handle errors with .catch()?
7. What is a higher-order function?
8. What is the difference between localStorage and sessionStorage?
9. How does this keyword work in different contexts?
10. What is JSX in React?
Advanced Level
1. How does the virtual DOM work in React?
2. What are controlled vs uncontrolled components in React?
3. What is useMemo and when should you use it?
4. How do you optimize a large React app for performance?
5. What are React lifecycle methods (class-based) and their hook equivalents?
6. How does Redux work and when should you use it?
7. What is code splitting and why is it useful?
8. How do you secure a frontend app from XSS attacks?
9. Explain the concept of Server-Side Rendering (SSR) vs Client-Side Rendering (CSR).
10. What are Web Components and how do they work?
React ❤️ for the detailed answers
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Anthropic is putting a limit on a Claude AI feature because people are using it '24/7'
Anthropic has introduced weekly rate limits for its Claude Code feature—used within paid Pro and Max plans—following reports that a small fraction of users have been running Claude “continuously in the background, 24/7,” which resulted in one individual accruing tens of thousands in usage on a $200/month tier and placing unexpected strain on infrastructure.
Starting August 28, 2025, the limits will cap weekly access (e.g. 240–480 hours of Sonnet 4 and 24–40 hours of Opus 4 for Max 20× subscribers), affecting under 5% of users and offering over‑limit users the option to purchase extra usage for continuity—but the move has drawn developer backlash, as some workflows are already being interrupted and critics argue the caps were poorly communicated
Anthropic has introduced weekly rate limits for its Claude Code feature—used within paid Pro and Max plans—following reports that a small fraction of users have been running Claude “continuously in the background, 24/7,” which resulted in one individual accruing tens of thousands in usage on a $200/month tier and placing unexpected strain on infrastructure.
Starting August 28, 2025, the limits will cap weekly access (e.g. 240–480 hours of Sonnet 4 and 24–40 hours of Opus 4 for Max 20× subscribers), affecting under 5% of users and offering over‑limit users the option to purchase extra usage for continuity—but the move has drawn developer backlash, as some workflows are already being interrupted and critics argue the caps were poorly communicated
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Data science is a multidisciplinary field that combines techniques from statistics, computer science, and domain-specific knowledge to extract insights and knowledge from data. Here are some essential concepts in data science:
1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
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1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
Join for more: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
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Quickly learn & retain new skills!
Try these top 10 ChatGPT prompts:
⏳ Explain Using Analogies
Prompt: "Explain the concept of (concept/theory) using an analogy or comparison to something familiar. Make it relatable and easy to understand with clear examples."
⏳ Develop a Quick Reference Guide
Prompt: "Create a quick reference guide for (skill/subject) that includes essential formulas, key points, and concise summaries for easy review."
⏳ Design Interactive Learning Activities
Prompt: "Suggest interactive activities or exercises that will help me actively engage with and better understand (topic/skill). Include step-by-step instructions and expected outcomes."
⏳ Simulate Real-World Applications
Prompt: "Provide real-world scenarios or applications where I can apply (concept/skill) to reinforce my learning. Include detailed examples and practical exercises."
⏳ Identify Common Mistakes
Prompt: "List common mistakes or pitfalls to avoid when learning (skill/subject) and provide strategies for overcoming them. Include tips for maintaining good practices."
⏳ Create a Daily Practice Routine
Prompt: "Design a daily practice routine to help me steadily improve in (skill/subject) over time. Include specific exercises, time allocations, and progression milestones."
⏳ Use Mnemonics for Memory
Prompt: "Create mnemonic devices to help me remember important information related to (topic/subject). Explain how each mnemonic works and provide examples."
⏳ Set Achievable Micro-Goals
Prompt: "Break down (skill/subject) into small, achievable goals that will keep me motivated and on track. Include a timeline and criteria for measuring success."
⏳ Leverage Peer Learning
Prompt: "Suggest ways to find or create a study group or learning community to practice and discuss (skill/subject) with peers. Include strategies for effective group learning."
⏳ Apply Spaced Repetition Technique
Prompt: "Set up a spaced repetition schedule to review and retain information for (topic/subject). Include intervals, review methods, and tips for long-term retention."
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All the best 👍 👍
Try these top 10 ChatGPT prompts:
⏳ Explain Using Analogies
Prompt: "Explain the concept of (concept/theory) using an analogy or comparison to something familiar. Make it relatable and easy to understand with clear examples."
⏳ Develop a Quick Reference Guide
Prompt: "Create a quick reference guide for (skill/subject) that includes essential formulas, key points, and concise summaries for easy review."
⏳ Design Interactive Learning Activities
Prompt: "Suggest interactive activities or exercises that will help me actively engage with and better understand (topic/skill). Include step-by-step instructions and expected outcomes."
⏳ Simulate Real-World Applications
Prompt: "Provide real-world scenarios or applications where I can apply (concept/skill) to reinforce my learning. Include detailed examples and practical exercises."
⏳ Identify Common Mistakes
Prompt: "List common mistakes or pitfalls to avoid when learning (skill/subject) and provide strategies for overcoming them. Include tips for maintaining good practices."
⏳ Create a Daily Practice Routine
Prompt: "Design a daily practice routine to help me steadily improve in (skill/subject) over time. Include specific exercises, time allocations, and progression milestones."
⏳ Use Mnemonics for Memory
Prompt: "Create mnemonic devices to help me remember important information related to (topic/subject). Explain how each mnemonic works and provide examples."
⏳ Set Achievable Micro-Goals
Prompt: "Break down (skill/subject) into small, achievable goals that will keep me motivated and on track. Include a timeline and criteria for measuring success."
⏳ Leverage Peer Learning
Prompt: "Suggest ways to find or create a study group or learning community to practice and discuss (skill/subject) with peers. Include strategies for effective group learning."
⏳ Apply Spaced Repetition Technique
Prompt: "Set up a spaced repetition schedule to review and retain information for (topic/subject). Include intervals, review methods, and tips for long-term retention."
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://news.1rj.ru/str/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍 👍
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How to be a Prompt Engineer 101
The shortest and most comprehensive guide
1. start with an explanation
Make a denoscription and character situation at the beginning of the Prompt
Error example:
Please help me read the following code:
{your input here}
Correct example:
2. Prompt to describe the situation
In the prompt, it is necessary to describe the context, result, length, format and style as much as possible
Error example:
Write a short story for kids
Correct example:
3. gives output in the format
If you are doing data analysis, please give the input template of the format
Error example:
Extract house pricing data from the following text.
Text: """
{your text containing pricing data}
"""
Correct example:
4. Add some example questions and answers
Sometimes adding some question and answer examples can make GPT more intelligent
Correct example:
The question and answer example is also a standard template example in fine-tune
5. Simplify the sentence and clarify the purpose
Keep your words as short as possible and don't say useless content
Error example:
ChatGPT, write a sales page for my company selling sand in the desert, please write only a few sentences, nothing long and complex
Correct example:
6. Good at using introductory words
Error example:
Write a Python function that plots my net worth over 10 years for different inputs on the initial investment and a given ROI
Correct example:
The shortest and most comprehensive guide
1. start with an explanation
Make a denoscription and character situation at the beginning of the Prompt
Error example:
Please help me read the following code:
{your input here}
Correct example:
Now let's play the role, you are a senior information security engineer, I will give you a piece of code, please help me read the code and point out where there may be security vulnerable.
Text: """
{your input here}
"""2. Prompt to describe the situation
In the prompt, it is necessary to describe the context, result, length, format and style as much as possible
Error example:
Write a short story for kids
Correct example:
Write a funny soccer story for kids that teaches the kid that persistence is the key for success in the style of Rowling.3. gives output in the format
If you are doing data analysis, please give the input template of the format
Error example:
Extract house pricing data from the following text.
Text: """
{your text containing pricing data}
"""
Correct example:
Extract house pricing data from the following text.
Desired format: """
House 1 | $1,000,000 | 100 sqm
House 2 | $500,000 | 90 sqm
... (and so on)
"""
Text: """
{your text containing pricing data}
"""4. Add some example questions and answers
Sometimes adding some question and answer examples can make GPT more intelligent
Correct example:
Extract brand names from the texts below.
Text 1: Finxter and YouTube are tech companies. Google is too.
Brand names 2: Finxter, YouTube, Google
###
Text 2: If you like tech, you'll love Finxter!
Brand names 2: Finxter
###
Text 3: {your text here}Brand names 3:The question and answer example is also a standard template example in fine-tune
5. Simplify the sentence and clarify the purpose
Keep your words as short as possible and don't say useless content
Error example:
ChatGPT, write a sales page for my company selling sand in the desert, please write only a few sentences, nothing long and complex
Correct example:
Write a 5-sentence sales page, sell sand in the desert.6. Good at using introductory words
Error example:
Write a Python function that plots my net worth over 10 years for different inputs on the initial investment and a given ROI
Correct example:
# Python function that plots net worth over 10
# years for different inputs on the initial
# investment and a given ROI
import matplotlib
def plot_net_worth(initial, roi):❤4👍1
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Ideogram Character is a powerful tool to create studio-quality AI headshots in seconds. Perfect for LinkedIn and social profiles.
Quick tutorial + master prompt below:
Professional headshot portrait. Person is [expression], wearing [wardrobe]. Background is [setting]. Lighting is [atmosphere].
Quick tutorial + master prompt below:
Professional headshot portrait. Person is [expression], wearing [wardrobe]. Background is [setting]. Lighting is [atmosphere].
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9. Git and GitHub ➝
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10. Blockchain ➝
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12. Node JS ➝
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13. English Speaking ➝
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