✨ 7 Must-Try Prompts for Claude 4.5

1️⃣ Build a Mini App

Prompt: “Write a simple budgeting app in Python that lets me input expenses, categories, and shows a weekly summary.”

2️⃣ Travel Planning with Multi-Step Reasoning

Prompt: “Plan a 7-day European itinerary with train travel only, balancing cost, culture, and family-friendly activities.”

3️⃣ Debugging Marathon

Prompt: “Here’s a broken code snippet [paste code]. Debug it, explain what was wrong, and suggest two alternative fixes.”

4️⃣ Real-World Instructions

Prompt: “Explain how to set up a home Wi-Fi mesh network with three routers, step by step, including diagrams in ASCII.”

5️⃣ Creative Storytelling

Prompt: “Pretend you’re a film director. Pitch me a 3-scene short film about humans teaching AI how to dance.”

6️⃣ Math Under Pressure

Prompt: “Solve this: A factory produces 120 widgets in 4 hours with 6 machines. How many machines are needed to produce 900 widgets in 10 hours?”

7️⃣ Computer-Use Challenge

Prompt: “Act as if you’re navigating a desktop. Open a folder, create a file called draft.txt, add the line ‘Hello Claude 4.5’ and show me the file tree.”

Complete Roadmap to learn Excel in 2025 👇👇

1. Basic Excel Skills:
   - Familiarize yourself with Excel's interface and navigation.
   - Learn basic formulas (SUM, AVERAGE, COUNT, etc.).
   - Understand cell referencing (absolute vs. relative).

2. Data Entry and Formatting:
   - Practice entering and formatting data efficiently.
   - Explore cell formatting options for a clean and organized dataset.

3. Advanced Formulas:
   - Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.
   - Learn logical functions (IF, AND, OR).
   - Understand array formulas for complex calculations.

4. Pivot Tables:
   - Gain proficiency in creating Pivot Tables for data summarization.
   - Learn to customize and format Pivot Tables effectively.

5. Data Cleaning:
   - Acquire skills in cleaning and transforming data.
   - Explore text-to-columns, remove duplicates, and data validation.

6. Charts and Graphs:
   - Learn to create various charts (bar, line, pie) for data visualization.
   - Understand chart formatting and customization.

7. Dashboard Creation:
   - Combine charts and tables to build basic dashboards.
   - Explore dynamic dashboards using Excel features.

8. Macros and VBA:
   - Dive into basic automation using Excel macros.
   - Learn Visual Basic for Applications (VBA) for more advanced automation.

9. Power Query:
   - Introduce yourself to Power Query for enhanced data manipulation.
   - Learn to import, transform, and load data efficiently.

10. Advanced Excel Techniques:
   - Explore advanced features like Goal Seek, Solver, and Scenario Manager.
   - Master the use of data tables for sensitivity analysis.

11. Real-world Projects:
   - Apply your skills to real-world projects or datasets.
   - Practice solving analytical problems using Excel.
Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel.

5️⃣ Free resources to practice Excel

https://www.w3schools.com/EXCEL/index.php

https://learn.microsoft.com/en-gb/training/paths/modern-analytics/

https://t.iss.one/excel_analyst/52

https://excel-practice-online.com/

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ENJOY LEARNING 👍👍

You don't need to spend a single penny to learn AI Agents

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DeepLearning AI has done a great job in making these courses

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2. Practical Multi AI Agents
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3. Serverless Agentic Workflows
- https://lnkd.in/dENcD795 
 
4. AI Agents in LangGraph
- https://lnkd.in/dJbGHaV2 
 
5. AI Agentic Design Patterns
- https://lnkd.in/dzDAA-J4

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Data Analytics isn't rocket science. It's just a different language.

Here's a beginner's guide to the world of data analytics:

1) Understand the fundamentals:
- Mathematics
- Statistics
- Technology

2) Learn the tools:
- SQL
- Python
- Excel (yes, it's still relevant!)

3) Understand the data:
- What do you want to measure?
- How are you measuring it?
- What metrics are important to you?

4) Data Visualization:
- A picture is worth a thousand words

5) Practice:
- There's no better way to learn than to do it yourself.

Data Analytics is a valuable skill that can help you make better decisions, understand your audience better, and ultimately grow your business.

It's never too late to start learning!

✅SQL Checklist for Data Analysts 📀🧠

1. SQL Basics
⦁ SELECT, WHERE, ORDER BY
⦁ DISTINCT, LIMIT, BETWEEN, IN
⦁ Aliasing (AS)

2. Filtering & Aggregation
⦁ GROUP BY & HAVING
⦁ COUNT(), SUM(), AVG(), MIN(), MAX()
⦁ NULL handling with COALESCE, IS NULL

3. Joins
⦁ INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
⦁ Joining multiple tables
⦁ Self Joins

4. Subqueries & CTEs
⦁ Subqueries in SELECT, WHERE, FROM
⦁ WITH clause (Common Table Expressions)
⦁ Nested subqueries

5. Window Functions
⦁ ROW_NUMBER(), RANK(), DENSE_RANK()
⦁ LEAD(), LAG()
⦁ PARTITION BY & ORDER BY within OVER()

6. Data Manipulation
⦁ INSERT, UPDATE, DELETE
⦁ CREATE TABLE, ALTER TABLE
⦁ Constraints: PRIMARY KEY, FOREIGN KEY, NOT NULL

7. Optimization Techniques
⦁ Indexes
⦁ Query performance tips
⦁ EXPLAIN plans

8. Real-World Scenarios
⦁ Writing complex queries for reports
⦁ Customer, sales, and product data
⦁ Time-based analysis (e.g., monthly trends)

9. Tools & Practice Platforms
⦁ MySQL, PostgreSQL, SQL Server
⦁ DB Fiddle, Mode Analytics, LeetCode (SQL), StrataScratch

10. Portfolio & Projects
⦁ Showcase queries on GitHub
⦁ Analyze public datasets (e.g., ecommerce, finance)
⦁ Document business insights

SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

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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 👍👍

Free Resources to learn Python Programming
👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

🤖 Artificial Intelligence Project Ideas ✅

🟢 Beginner Level
⦁ Spam Email Classifier (train on labeled emails with Naive Bayes—super practical for real apps!)
⦁ Handwritten Digit Recognition (MNIST) (classic CNN starter using TensorFlow)
⦁ Rock-Paper-Scissors AI Game (add random choices or simple ML to beat players)
⦁ Chatbot using Rule-Based Logic (pattern matching for basic Q&A)
⦁ AI Tic-Tac-Toe Game (minimax algorithm for unbeatable play)

🟡 Intermediate Level
⦁ Face Detection & Emotion Recognition (OpenCV + pre-trained models for facial analysis)
⦁ Voice Assistant with Speech Recognition (integrate SpeechRecognition lib for commands)
⦁ Language Translator (using NLP models) (Hugging Face transformers for quick translations)
⦁ AI-Powered Resume Screener (NLP to parse and score resumes)
⦁ Smart Virtual Keyboard (predictive typing) (build next-word prediction with basic RNNs)

🔴 Advanced Level
⦁ Self-Learning Game Agent (Reinforcement Learning) (Q-learning for games like CartPole)
⦁ AI Stock Trading Bot (time-series forecasting with LSTM)
⦁ Deepfake Video Generator (Ethical Use Only) (GANs like StyleGAN—handle responsibly)
⦁ Autonomous Car Simulation (OpenCV + RL) (pathfinding in virtual environments)
⦁ Medical Diagnosis using Deep Learning (X-ray/CT analysis) (CNNs on datasets like ChestX-ray)

💬 Double Tap ❤️ for more! 💡🧠

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🔟 Free useful resources to learn Machine Learning

👉 Google

https://developers.google.com/machine-learning/crash-course

👉 Leetcode

https://leetcode.com/explore/featured/card/machine-learning-101

👉 Hackerrank

https://www.hackerrank.com/domains/ai/machine-learning

👉 Hands-on Machine Learning

https://t.iss.one/datasciencefun/424

👉 FreeCodeCamp

https://www.freecodecamp.org/learn/machine-learning-with-python/

👉 Machine learning projects

https://t.iss.one/datasciencefun/392

👉 Kaggle

https://www.kaggle.com/learn/intro-to-machine-learning

https://www.kaggle.com/learn/intermediate-machine-learning

👉 Geeksforgeeks

https://www.geeksforgeeks.org/machine-learning/

👉 Create ML Models

https://docs.microsoft.com/en-us/learn/paths/create-machine-learn-models/

👉 Machine Learning Test Cheat Sheet

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

Join @free4unow_backup for more free resources

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