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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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

Join https://t.me/techpsyche for More Resources:

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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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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.

Beginner's Guide to Cryptocurrency: https://t.me/techpsyche/703

Cryptocurrency Mining: https://t.me/techpsyche/663

Choosing the Right Crypto Exchange: https://t.me/techpsyche/774

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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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Power BI Learning Plan in 2025

|-- Week 1: Introduction to Power BI
| |-- Power BI Basics
| | |-- What is Power BI?
| | |-- Components of Power BI
| | |-- Power BI Desktop vs. Power BI Service
| |-- Setting up Power BI
| | |-- Installing Power BI Desktop
| | |-- Overview of the Interface
| | |-- Connecting to Data Sources
| |-- First Power BI Report
| | |-- Creating a Simple Report
| | |-- Basic Visualizations
|
|-- Week 2: Data Transformation and Modeling
| |-- Power Query Editor
| | |-- Importing and Shaping Data
| | |-- Applied Steps
| |-- Data Modeling
| | |-- Relationships
| | |-- Calculated Columns and Measures
| | |-- DAX Basics
| |-- Data Cleaning
| | |-- Handling Missing Data
| | |-- Data Types and Formatting
|
|-- Week 3: Advanced DAX and Data Modeling
| |-- Advanced DAX Functions
| | |-- Time Intelligence
| | |-- Iterators
| | |-- Filter Functions
| |-- Advanced Data Modeling
| | |-- Star and Snowflake Schemas
| | |-- Role-playing Dimensions
| |-- Performance Optimization
| | |-- Query Performance
| | |-- Model Performance
|
|-- Week 4: Visualizations and Reports
| |-- Advanced Visualizations
| | |-- Custom Visuals
| | |-- Conditional Formatting
| | |-- Interactive Elements
| |-- Report Design
| | |-- Designing for Clarity
| | |-- Using Themes
| | |-- Report Navigation
| |-- Power BI Service
| | |-- Publishing Reports
| | |-- Workspaces and Apps
| | |-- Sharing and Collaboration
|
|-- Week 5: Dashboards and Data Analysis
| |-- Creating Dashboards
| | |-- Pinning Visuals
| | |-- Dashboard Tiles
| | |-- Alerts
| |-- Data Analysis Techniques
| | |-- Drillthrough
| | |-- Bookmarks
| | |-- What-If Parameters
| |-- Advanced Analytics
| | |-- Quick Insights
| | |-- AI Visuals
|
|-- Week 6-8: Power BI and Other Tools
| |-- Power BI and Excel
| | |-- Excel Integration
| | |-- PowerPivot and PowerQuery
| | |-- Publishing from Excel
| |-- Power BI and R
| | |-- Using R Scripts in Power BI
| | |-- R Visuals
| |-- Power BI and Python
| | |-- Using Python Scripts
| | |-- Python Visuals
| |-- Power Automate and Power BI
| | |-- Automating Workflows
| | |-- Data Alerts and Actions
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing the Model
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- SQL Databases
| | |-- Azure Data Services
|
|-- Week 12: Post-Project Learning
| |-- Power BI Administration
| | |-- Data Governance
| | |-- Security
| | |-- Monitoring and Auditing
| |-- Power BI in the Cloud
| | |-- Power BI Premium
| | |-- Power BI Embedded
| |-- Continuing Education
| | |-- Advanced Power BI Topics
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udacity)
| |-- Books (The Definitive Guide to DAX, Microsoft Power BI Cookbook)
| |-- Power BI Blogs and Resources
| |-- GitHub Repositories
| |-- Power BI Communities (Microsoft Power BI Community, Reddit)

You can refer these Power BI Interview Resources to learn more:
https://t.me/dataanalysisresourcestp/27

Like this post if you want me to continue this Power BI series 👍♥️

Hope it helps :)

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Learn Data Science in 2025

𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚

Pareto's Law states that "that 80% of consequences come from 20% of the causes".

This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.

Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.

But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).

For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.

So, invest more time learning topics that provide immediate value now, not a year later.

𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡️

There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.

Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.

So, find a mentor who can teach you practical knowledge in data science.

𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️

If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.

Join https://t.me/datascienceresourcestp for more

ENJOY LEARNING 👍👍

WhatsApp Channel: https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

𝐈𝐧𝐬𝐩𝐢𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐢𝐧 𝐔𝐈 𝐝𝐞𝐬𝐢𝐠𝐧
since it allows designers to come up with new and unique concepts. It keeps designs fresh and relevant to current trends. Designers can create user-friendly and beautiful interfaces by taking inspiration from other designs.

𝐖𝐞𝐛𝐬𝐢𝐭𝐞𝐬 𝐟𝐨𝐫 𝐝𝐞𝐬𝐢𝐠𝐧 𝐢𝐧𝐬𝐩𝐢𝐫𝐚𝐭𝐢𝐨𝐧 ✨

𝐃𝐚𝐫𝐤 𝐃𝐞𝐬𝐢𝐠𝐧 : Dark Design uses dark backgrounds with light text, offering a modern look, reducing eye strain, and saving battery on OLED screens.

𝐌𝐨𝐛𝐛𝐢𝐧 : Mobbin is a design platform with a library of UI/UX patterns for trend exploration and inspiration.

𝐋𝐚𝐩𝐚 𝐍𝐢𝐧𝐣𝐚 : Lapa is a design inspiration platform featuring top landing page designs, Ul patterns, and web trends.

𝐋𝐚𝐧𝐝𝐛𝐨𝐨𝐤 : Land-book is a platform featuring curated website designs for creative inspiration. Explore layouts, typography, and trends.

Best Youtube Channels for UX/UI
https://t.me/designresourcestp/11

WhatsApp Channel:
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