What those who mess with developers reap!

Steve Jobs: The Art of Starting Over

🍎 Jobs was fired from Apple, the company he co-founded, but returned to make it one of the most valuable brands in the world.

🔄 Setbacks are setups for comebacks.

t.me/techpsyche

🔍 Key Skills Every Developer Needs in 2025

- Problem-Solving: Coding is all about creative solutions. 
- Adaptability: Tech evolves fast—stay flexible! 
- Collaboration: Teamwork makes the dream work. 

✨ Master these and thrive in any tech field!

t.me/techpsyche

🎓 Cursor Now Free for Students

Cursor is offering 12 months of free access to its platform for students and high schoolers aged 16 and up.

🔓 How to Get It:
• Sign up on the platform
• Upload a photo of your student ID or school document
• High school IDs are accepted too, as long as you’re 16+
• Get instant access for a full year

💰 Already have a paid plan?
No worries — remaining funds will be refunded automatically to the original payment method.

⏱ Quick and Easy:
Approval usually takes just a few minutes, with almost no rejections according to user feedback.

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Compose Multiplatform 1.8.0 Released: Compose Multiplatform for iOS Is Stable and Production-Ready

A major milestone in the Kotlin Multiplatform journey: the release of Compose Multiplatform 1.8.0, which brings Compose for iOS to Stable. With this update, Kotlin Multiplatform becomes a complete solution for mobile development, enabling flexible code sharing across both business logic and UI without compromising app quality or losing control over platform-specific capabilities.

And finally, compose multiplatform has been stabilized alongside kotlin multiplatform. Now programmers can share both logic and UI between platforms.

https://www.jetbrains.com/compose-multiplatform/
_________________
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Power BI

Data Analyst vs Data Engineer vs Data Scientist ✅

Skills required to become a Data Analyst 👇

- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic scripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.


Skills required to become a Data Engineer: 👇

- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.


Skills required to become a Data Scientist: 👇

- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.

Bonus Skills Across All Roles:

- Data Visualization: Mastery in tools like Power BI and Tableau (https://t.me/PowerBI_analyst) to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.

Resources 👇👇

Data Analyst: (https://t.me/dataanalysisresourcestp)

Data Engineer: (https://t.me/datascienceresourcestp/39)

Data Scientist: (https://t.me/datascienceresourcestp)

Like this post for more content like this 👍♥️

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Hope it helps :)

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Educate yourself online for free:

1. 🤖 ChatGPT
2. 📺 YouTube
3. 🧠 Claude 3
4. 🌐 W3School
5. 🎓 Telegram
6. 📚 Udemy
7. 💻 FreeCodeCamp
8. 📖 MDN (Mozilla Developer Network)
9. 🎨 DataSimplifier
10. 🏛 Bard
11. 🚀 Udacity
12. 📈 LinkedIn Learning
13. 📘 MIT Open Courseware
14. 🏫 Stanford Online
15. 🥋 Perplexity AI
16. ♿️ Khan Academy

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ChatGPT Cheat Sheet

The Untold Truth About Junior Data Analyst Interviews (From Someone Who’s Seen It All)

Guys, let’s cut through the noise. Most companies aren’t testing how many fancy tools you know—they’re testing how you think! Here’s what you really need to focus on:

SQL Interview Round

WHAT YOU THINK THEY WANT:
“Write the most complex SQL queries!”

WHAT THEY ACTUALLY TEST:

Can you clean messy data?

Do you handle NULL values logically?

How do you deal with duplicates?

Can you explain what you did, step-by-step?

Do you verify your results?

REALISTIC QUESTIONS YOU’LL FACE:
1️⃣ Find duplicate orders in a sales table.
2️⃣ Calculate monthly revenue for the past year.
3️⃣ Identify the top 10 customers by revenue.

Excel Interview Round

WHAT YOU THINK THEY WANT:
“Show off crazy Excel skills with macros and VBA.”

WHAT THEY REALLY WANT TO SEE:

Your ability to use VLOOKUP/XLOOKUP.

Comfort with Pivot Tables for summarization.

Your knack for creating basic formulas for data cleaning.

A logical approach to tackling Excel problems.

REALISTIC TASKS:
✅ Merge two datasets using VLOOKUP.
✅ Summarize sales trends in a Pivot Table.
✅ Clean up inconsistent text fields (hello, TRIM function).

Business Case Analysis

WHAT YOU THINK THEY WANT:
“Build a mind-blowing dashboard or deliver complex models.”

WHAT THEY ACTUALLY EVALUATE:

Can you break down the problem into manageable parts?

Do you ask smart, relevant questions?

Is your analysis focused on business outcomes?

How clearly can you present your findings?

What You'll Definitely Face

1. The “Data Mess” Scenario
They’ll hand you a messy dataset with:

Missing data, duplicates, and weird formats.

No clear instructions.

They watch:
👉 How you approach the problem.
👉 If you spot inconsistencies.
👉 The steps you take to clean and structure data.

2. The “Explain Your Analysis” Challenge
They’ll say:
“Walk us through what you did and why.”

They’re looking for:

Clarity in communication.

Your thought process.

The connection between your work and the business context.

How to Stand Out in Interviews

1. Nail the Basics

SQL: Focus on joins, filtering, grouping, and aggregating.

Excel: Get comfortable with lookups, pivots, and cleaning techniques.

Data Cleaning: Practice handling real-world messy datasets.

2. Understand the Business

Research their industry and common metrics (e.g., sales, churn rate).

Know basic KPIs they might ask about.

Prepare thoughtful, strategic questions.

3. Practice Real Scenarios
🔹 Analyze trends: Monthly revenue, churn analysis.
🔹 Segment customers: Who are your top spenders?
🔹 Evaluate campaigns: Which marketing effort drove the best ROI?

Reality Check: What Really Matters

🌟 How you think through a problem.
🌟 How you communicate your insights.
🌟 How you connect your work to business goals.

🚫 What doesn’t matter?

Writing overly complex SQL.

Knowing every Excel formula.

Advanced machine learning knowledge (for most junior roles).

Pro Tip: Stay calm, ask questions, and show you’re eager to solve problems. Your mindset is just as important as your technical skills!

I know it's a very long post but it'll be worth the efforts I took even if it helps a single person. Give it a like if you want me to continue posting such detailed posts.

I have curated best top-notch Data Analytics Resources 👇👇
https://topmate.io/learning_resources/1456762

Like this post if you want me to post more useful content ❤️

Hope it helps :)

ENJOY LEARNING 👍👍

https://t.me/techpsyche

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Remote Data Engineer Job at Deel - EMEA

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1️⃣ What Is Crypto Arbitrage❓

Crypto arbitrage trading — it is a trading strategy, which consists in simultaneous buying and selling of cryptocurrency on different crypto exchanges in order to profit from the difference in its prices. Arbitrage crypto is based on the principle of inefficient markets, where a token or coin may trade cheaper on one exchange and more expensive on another. Using this difference, traders can quickly buy an asset on one market and sell it on another, capitalizing on the price difference.

2️⃣ How Does Crypto Arbitrage Trading Work?✅

— Market Monitoring:  A trader starts by monitoring cryptocurrency prices on various exchanges. Crypto prices can vary due to different levels of liquidity, trading volume, and other factors. Using specialized monitoring software or platforms, the trader finds arbitrage bundles.

— Buying cryptocurrency: The trader buys a cryptocurrency on an exchange where it trades at a lower price. The market’s liquidity must be considered to purchase the required amount of cryptocurrency without significantly affecting the price.

— Funds Transfer: The purchased cryptocurrency is transferred to another exchange, where it is traded at a higher price.

3️⃣ How to simplify the cryptocurrency arbitrage process?🔑

There are crypto experts and teams that quite realistically give working signals on crypto arbitrage for a % of your profit, on average it is 15-20%.


4️⃣ How much you can make. Real figures !💰💯

In the market it is considered that 3-4% from one round is the standard figures that can be earned. It turns out that with an investment of $1000, you earn $40 from one round, which takes 20-30 minutes.

For the most part, your earnings depend on the quality of the bundle, so it's best to enlist the help of experts in the field.

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Data Scientist vs Data Engineer vs Data Analyst

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These are the Top 5 Most Common SQL Questions for Data Engineering:

1. Total records after joining two tables on all types of joins
2. Rolling Sum and Nth salary based questions
3. Lag/Lead based questions e.g., consecutive months of increasing sales or YoY growth
4. Query to find employees who earn more than their managers
5. Removing duplicates from a table

Key Takeaways:
- Master window functions and joins
- Practice medium to hard SQL questions regularly

Getting good at SQL will pay off in the long run! 💪

Join our WhatsApp channel for More Data Engineering Resources:
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