Cost of living (monthly expenses) for one person by country:

🇨🇭 Switzerland: $3,900
🇳🇴 Norway: $3,200
🇮🇸 Iceland: $3,000
🇯🇵 Japan: $2,800
🇱🇺 Luxembourg: $2,700
🇩🇰 Denmark: $2,650
🇸🇬 Singapore: $2,600
🇮🇪 Ireland: $2,500
🇺🇸 United States: $2,450
🇭🇰 Hong Kong: $2,400
🇫🇮 Finland: $2,350
🇦🇪 UAE: $2,300
🇬🇧 UK: $2,250
🇸🇪 Sweden: $2,200
🇩🇪 Germany: $2,150
🇧🇪 Belgium: $2,100
🇫🇷 France: $2,050
🇳🇱 Netherlands: $2,000
🇨🇦 Canada: $1,950
🇦🇹 Austria: $1,900
🇦🇺 Australia: $1,850
🇳🇿 New Zealand: $1,800

Classes That SHOULD Be Mandatory in High School:

• Taxes
• Investing
• Real Estate
• Negotiating
• Basic coding
• Building credit
• Microsoft Excel
• Personal Finance
• Entrepreneurship
• Time Management
• Money Management

What would you add to the list?

Computer keyword shortcuts

Popular API Architecture Styles

1. gRPC: A high-performance, language-agnostic remote procedure call (RPC) framework for efficient communication between distributed systems, often used in microservices architectures.

2. SOAP: A protocol for exchanging structured information in the implementation of web services, known for its strict standards and XML-based message format.

3. GraphQL: A query language and runtime for APIs that allows clients to request only the data they need, reducing over-fetching and under-fetching of data.

4. Webhook: A mechanism for real-time communication where an application sends HTTP POST requests to a predefined URL to notify and trigger actions in another system.

5. REST: Representational State Transfer, an architectural style for designing networked applications, using standard HTTP methods (GET, POST, PUT, DELETE) to manipulate resources.

Many people still aren't fully utilizing the power of Telegram.

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Ai tools ✅

Javascript Mindmap ✅

LLM Project Ideas for Resume

1️⃣ AI Image Captioning

Train an LLM to generate accurate, context-aware image captions for better accessibility and engagement.

2️⃣ Large Text Analysis

Use LLMs to summarize and extract key insights from massive text documents in various industries.

3️⃣ AI Code Generation

Automate code snippet creation from natural language descriptions to boost developer productivity.

4️⃣ Text Completion

Fine-tune LLMs for smarter text predictions in chatbots and content tools, enhancing user interactions.

Top 21 skills to learn this year 👇

1. Artificial Intelligence and Machine Learning: Understanding AI algorithms and applications.
2. Data Science: Proficiency in tools like Python/ R, Jupyter Notebook, and GitHub, with the ability to apply data science algorithms to solve real-world problems.
3. Cybersecurity: Protecting data and systems from cyber threats.
4. Cloud Computing: Proficiency in platforms like AWS, Azure, and Google Cloud.
5. Blockchain Technology: Understanding blockchain architecture and applications beyond cryptocurrencies.
6. Digital Marketing: Expertise in SEO, social media, and online advertising.
7. Programming: Skills in languages such as Python, JavaScript, and Go.
8. UX/UI Design: Creating intuitive and effective user interfaces and experiences.
9. Consulting: Expertise in providing strategic advice, improving business processes, and implementing solutions to drive business growth.
10. Data Analysis and Visualization: Proficiency in tools like Excel, SQL, Tableau, and Power BI to analyze and present data effectively.
11. Business Analysis & Project Management: Using tools and methodologies like Agile and Scrum.
12. Remote Work Tools: Proficiency in tools for remote collaboration and productivity.
13. Financial Literacy: Understanding personal finance, investment, and cryptocurrencies.
14. Emotional Intelligence: Skills in empathy, communication, and relationship management.
15. Business Acumen: A deep understanding of how businesses operate, including strategic thinking, market analysis, and financial literacy.
16. Investment Banking: Knowledge of financial markets, valuation methods, mergers and acquisitions, and financial modeling.
17. Mobile App Development: Skills in developing apps for iOS and Android using Swift, Kotlin, or React Native.
18. Financial Management: Proficiency in financial planning, analysis, and tools like QuickBooks and SAP.
19. Web Development: Proficiency in front-end and back-end development using HTML, CSS, JavaScript, and frameworks like React, Angular, and Node.js.
20. Data Engineering: Skills in designing, building, and maintaining data pipelines and architectures using tools like Hadoop, Spark, and Kafka.
21. Soft Skills: Improving leadership, teamwork, and adaptability skills.

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Essentials for Acing any Data Analytics Interviews-

SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation

2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements

3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE

Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages

2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate

3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly

Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting

2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek

3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards

Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema

2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX

3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes

Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution

Channels that you MUST follow in 2024:

✅ @getjobss - Jobs and Internship Opportunities

✅ @englishlearnerspro - improve your English

✅ @datasciencefun - Learn Data Science and Machibe Learning

✅ @crackingthecodinginterview - boost your coding knowledge

✅ @sqlspecialist - Data Analysts Community

✅ @programming_guide - Coding Books

✅ @udemy_free_courses_with_certi - Free Udemy Courses with Certificate

𝐔𝐬𝐢𝐧𝐠 𝐁𝐢𝐠-𝐎 𝐢𝐧 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 𝐚𝐧𝐝 𝐄𝐯𝐞𝐫𝐲𝐝𝐚𝐲 𝐋𝐢𝐟𝐞.

Big-O notation is a mathematical notation that is used to describe the performance or complexity of an algorithm, specifically how long an algorithm takes to run as the input size grows.

Understanding Big-O notation is essential for software engineers, as it allows them to analyze and compare the efficiency of different algorithms and make informed decisions about which one to use in a given situation.

Here are famous Big-O notations with examples.

Programming languages and their uses in Ethical hacking :

Programming languages are essential tools for ethical hackers. They are used for tasks such as vulnerability testing, penetration testing, and developing exploits. Here are some programming languages that are commonly used in ethical hacking and their specific uses:

Python: Python is a high-level programming language that is easy to learn and widely used in the field of cybersecurity. It is used for tasks such as penetration testing, reverse engineering, and scripting. Python has a large community of developers who create and maintain libraries that can be used for security purposes, such as Scapy for packet manipulation, PyCrypto for encryption and decryption, and BeautifulSoup for web scraping.

Ruby: Ruby is another high-level programming language that is popular in the cybersecurity community. It is used for developing exploits and automating tasks. Metasploit, one of the most widely used penetration testing tools, is written in Ruby.

C/C++: C and C++ are low-level programming languages that are used for writing exploits and developing rootkits. They are also used for reverse engineering and vulnerability testing. Many of the tools used in ethical hacking, such as Nmap, Wireshark, and Tcpdump, are written in C/C++.

JavaScript: JavaScript is a popular scripting language that is used for web application security testing. It is used for tasks such as cross-site scripting (XSS) and cross-site request forgery (CSRF) testing. Many web-based security tools, such as Burp Suite, are written in JavaScript.

Bash: Bash is a shell scripting language that is used for automating tasks and creating custom scripts. It is commonly used for tasks such as password cracking and network scanning.

SQL: SQL is a database programming language that is used for exploiting and testing SQL injection vulnerabilities in web applications.

In addition to these languages, there are many other programming languages that can be used in ethical hacking, such as Perl, PHP, and Java. The choice of programming language will depend on the specific task at hand and the preference of the individual ethical hacker.

TYPES OF INTELLIGENCE

4 types of Intelligence:

1) Intelligence Quotient (IQ)
2) Emotional Quotient (EQ)
3) Social Quotient (SQ)
4) Adversity Quotient (AQ)

1. Intelligence Quotient (IQ): this is the measure of your level of comprehension. You need IQ to solve maths, memorize things, and recall lessons.

2. Emotional Quotient (EQ): this is the measure of your ability to maintain peace with others, keep to time, be responsible, be honest, respect boundaries, be humble, genuine and considerate.
3. Social Quotient (SQ): this is the measure of your ability to build a network of friends and maintain it over a long period of time.

People that have higher EQ and SQ tend to go further in life than those with a high IQ but low EQ and SQ. Most schools capitalize on improving IQ levels while EQ and SQ are played down.

Develop their IQ, as well as their EQ, SQ and AQ. They should become multifaceted human beings able to do things independently of their parents.
4. The Adversity Quotient (AQ): The measure of your ability to go through a rough patch in life, and come out of it without losing your mind.

When faced with troubles, AQ determines who will give up, who will abandon their family, and who will consider suicide.

Parents please expose your children to other areas of life than just Academics. They should adore manual labour (never use work as a form of punishment), Sports and Arts.