16 Websites to Find Remote International Jobs

1. LinkedIn - linkedin.com
2. Indeed - indeed.com
3. Glassdoor - glassdoor.com
4. FlexJobs - flexjobs.com
5. Remote.co - remote.co
6. Upwork - upwork.com
7. Freelancer - freelancer.com
8. Fiverr - fiverr.com
9. Guru - guru.com
10. Toptal - toptal.com
11. AngelList - angel.co
12. SimplyHired - simplyhired.com
13. Remotive - remotive.com
14. Hired - hired.com
15. CloudPeeps - cloudpeeps.com
16. TaskRabbit - taskrabbit.com

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10 awesome frontend development YouTube channels:

1. Traversy Media 🚀
2. The Net Ninja 🥷
3. Dev Ed 🎨
4. Academind 📚
5. Fireship 🔥
6. Codevolution 💻
7. DesignCourse 🎨
8. Florin Pop 🧑‍💻
9. Web Dev Simplified 🌐
10. Kevin Powell 🎥

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JavaScript Array Methods

What Python Can Do

Road to API Expertise

https://x.com/tech_psyche/status/1847893803807486180

*Here's a good list of cheat sheets for programmers (all free):*

Data Science Cheatsheet

https://github.com/aaronwangy/Data-Science-Cheatsheet

SQL Cheatsheet

sqltutorial.org/sql-cheat-sheet

https://bit.ly/SQL-CheatSheet

https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf

Java Programming Cheatsheet

https://introcs.cs.princeton.edu/java/11cheatsheet/

Javascript Cheatsheet

quickref.me/javascript.html

bit.ly/JavaScript-Cheatsheet

Data Analytics Cheatsheets

https://bit.ly/40fpy1J

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Python Cheat sheet

quickref.me/python.html

https://bit.ly/3AcFffI

GIT Cheatsheet

https://education.github.com/git-cheat-sheet-education.pdf

https://telegra.ph/Git-Cheat-Sheet-Atlassian-10-23

https://about.gitlab.com/images/press/git-cheat-sheet.pdf

HTML Cheatsheet

https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf

htmlcheatsheet.com

CSS Cheatsheet

htmlcheatsheet.com/css

jQuery Cheatsheet

https://bit.ly/3A9L8dw

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9 Best Machine Learning Use cases in our Daily Lives 🚀

👓 Youtube Recommendation
👓 Voice Assistants
👓 arrow Smartphone Camera
👓 Google Maps routes
👓 Email Filtering
👓 Search
👓 Translation
👓 Chatbots
👓 Fraud Protection

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Machine Learning Cheat Sheet

Here is the list of latest trending tech stacks in 2024 👇👇

1. Frontend Development:
   - React.js: Known for its component-based architecture and strong community support.
   - Vue.js: Valued for its simplicity and flexibility in building user interfaces.
   - Angular: Still widely used, especially in enterprise applications.

2. Backend Development:
   - Node.js: Popular for building scalable and fast network applications using JavaScript.
   - Django: Preferred for its rapid development capabilities and robust security features.
   - Spring Boot: Widely used in Java-based applications for its ease of use and integration capabilities.

3. Mobile Development:
   - Flutter: Known for building natively compiled applications for mobile, web, and desktop from a single codebase.
   - React Native: Continues to be popular for building cross-platform applications with native capabilities.

4. Cloud Computing and DevOps:
   - AWS (Amazon Web Services), Azure, Google Cloud: Leading cloud service providers offering extensive services for computing, storage, and networking.
   - Docker and Kubernetes: Essential for containerization and orchestration of applications in a cloud-native environment.
   - Terraform: Infrastructure as code tool for managing and provisioning cloud infrastructure.

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5. Data Science and Machine Learning:
   - Python: Dominant language for data science and machine learning, with libraries like NumPy, Pandas, and Scikit-learn.
   - TensorFlow and PyTorch: Leading frameworks for building and training machine learning models.
   - Apache Spark: Used for big data processing and analytics.

6. Cybersecurity:
   - SIEM Tools (Security Information and Event Management): Such as Splunk and ELK Stack, crucial for monitoring and managing security incidents.
   - Zero Trust Architecture: A security model that eliminates the idea of trust based on network location.

7. Blockchain and Cryptocurrency:
   - Ethereum: A blockchain platform supporting smart contracts and decentralized applications.
   - Hyperledger Fabric: Framework for developing permissioned, blockchain-based applications.

8. Artificial Intelligence (AI) and Natural Language Processing (NLP):
   - GPT (Generative Pre-trained Transformer) Models: Such as GPT-4, used for various natural language understanding tasks.
   - Computer Vision: Frameworks like OpenCV for image and video processing tasks.

9. Edge Computing and IoT (Internet of Things):
   - Edge Computing: Technologies that bring computation and data storage closer to the location where it is needed.
   - IoT Platforms: Such as AWS IoT, Azure IoT Hub, offering capabilities for managing and securing IoT devices and data.

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Enjoy Learning 🤝🤝

20 Common Lies about Artificial Intelligence

𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐝𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:

𝘐 𝘨𝘦𝘵 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘧𝘳𝘰𝘮 𝘥𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 𝘢𝘴𝘱𝘪𝘳𝘢𝘯𝘵𝘴 𝘢𝘯𝘥 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘴 𝘰𝘯 𝘩𝘰𝘸 𝘵𝘰 𝘨𝘢𝘪𝘯 𝘤𝘰𝘮𝘮𝘢𝘯𝘥 𝘰𝘧 𝘗𝘺𝘵𝘩𝘰𝘯.

📍𝐋𝐞𝐚𝐫𝐧 𝐂𝐨𝐫𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Master Python libraries for data analytics, like
-pandas for dataframes,
-NumPy for numerical operations,
-Matplotlib/Seaborn for plotting,
-scikit-learn for machine learning.

📍𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code.

📍𝐔𝐬𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦-𝐒𝐨𝐥𝐯𝐢𝐧𝐠 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance.

📍𝐃𝐨 𝐌𝐨𝐜𝐤 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Work on end-to-end Python analytics projects—data loading, cleaning, analysis, and visualization.

📍𝐋𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐏𝐚𝐬𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Review your previous Python projects to see where your code can be more efficient.

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Top Platforms for Building Data Science Portfolio

Build an irresistible portfolio that hooks recruiters with these free platforms.

Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.

1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace

7 Websites to Learn Data Science for FREE🧑‍💻

✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp

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Data Science Resources: https://t.me/DataScienceResourcesTP

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Remember, I'm here for you😊I provide anything I think you might need as long as it's in Tech🥳

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Coding Interview Preparation:
Top 10 Sites to review your resume for free:

1. Zety Resume Builder
2. Resumonk
3. Free Resume Builder
4. VisualCV
5. Cvmaker
6. ResumUP
7. Resume Genius
8. Resumebuilder
9. Resume Baking
10. Enhancv

Coding is tricky. Coding in interviews feels even harder. It’s intimidating, uncertain and hard to prepare. Here are 4 ways to do it!

1. Interview Cake: I think it is some of the best prep available and it is targeted toward weaknesses many data scientists have in algorithms and data structures: https://www.interviewcake.com

2. Leetcode: While developed for software engineering interviews, it has a LOT of useful content for learning algorithms. For data science, I'd suggest focusing on Easy/Medium: https://leetcode.com/

3. Cracking the Coding Interview: Amazing book, sometimes referred to as CTCI. A classic and one you should have: https://cin.ufpe.br/~fbma/Crack/Cracking%20the%20Coding%20Interview%20189%20Programming%20Questions%20and%20Solutions.pdf

4. Daily Coding Problem: The book and the website are awesome. Work on a daily problem. This was my go to resource for when I was looking to stay sharp: https://www.dailycodingproblem.com/

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6 Data Concepts

Fundamental concepts in statistics for entry-level data analysts.

➡️ Descriptive Statistics

👉 Mean: The average value of a dataset.
👉 Median: The middle value of a dataset.
👉 Mode: The most frequently occurring value in a dataset.
👉 Range: The difference between the highest and lowest values.
👉 Variance: Measures how much the values in a dataset vary from the mean.
👉 Standard Deviation: The square root of the variance, representing the average distance of each data point from the mean.

Basics Of HTML

Linux File System in 1 Page

Which Jobs AI Can't Replace?! 😶‍🌫️

https://techurl.in/dIJaG

Confused about which field to dive into—Front-End Development (FE), Back-End Development (BE), Machine Learning (ML), or Blockchain?

Here's a concise breakdown of each, designed to clarify your options:

### Front-End Development (FE)
Key Skills:
- HTML/CSS: Fundamental for creating the structure and style of web pages.
- JavaScript: Essential for adding interactivity and functionality to websites.
- Frameworks/Libraries: React, Angular, or Vue.js for efficient and scalable front-end development.
- Responsive Design: Ensuring websites look good on all devices.
- Version Control: Git for managing code changes and collaboration.

Career Prospects:
- Web Developer
- UI/UX Designer
- Front-End Engineer

### Back-End Development (BE)
Key Skills:
- Programming Languages: Python, Java, Ruby, Node.js, or PHP for server-side logic.
- Databases: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data management.
- APIs: RESTful and GraphQL for communication between front-end and back-end.
- Server Management: Understanding of server, network, and hosting environments.
- Security: Knowledge of authentication, authorization, and data protection.

Career Prospects:
- Back-End Developer
- Full-Stack Developer
- Database Administrator

### Machine Learning (ML)
Key Skills:
- Programming Languages: Python and R are widely used in ML.
- Mathematics: Statistics, linear algebra, and calculus for understanding ML algorithms.
- Libraries/Frameworks: TensorFlow, PyTorch, Scikit-Learn for building ML models.
- Data Handling: Pandas, NumPy for data manipulation and preprocessing.
- Model Evaluation: Techniques for assessing model performance.

Career Prospects:
- Data Scientist
- Machine Learning Engineer
- AI Researcher

### Blockchain
Key Skills:
- Cryptography: Understanding of encryption and security principles.
- Blockchain Platforms: Ethereum, Hyperledger, Binance Smart Chain for building decentralized applications.
- Smart Contracts: Solidity for developing smart contracts.
- Distributed Systems: Knowledge of peer-to-peer networks and consensus algorithms.
- Blockchain Tools: Truffle, Ganache, Metamask for development and testing.

Career Prospects:
- Blockchain Developer
- Smart Contract Developer
- Crypto Analyst

### Decision Criteria
1. Interest: Choose an area you are genuinely interested in.
2. Market Demand: Research the current job market to see which skills are in demand.
3. Career Goals: Consider your long-term career aspirations.
4. Learning Curve: Assess how much time and effort you can dedicate to learning new skills.

Each field offers unique opportunities and challenges, so weigh your options carefully based on your personal preferences and career objectives.

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Python Cheat Sheet


https://x.com/tech_psyche/status/1846295907458777552

Some useful PYTHON libraries for data science

NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++

SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.

Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.

Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.

Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.

Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.

Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.

Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.

Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.

SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.

Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.

Additional libraries, you might need:

os for Operating system and file operations

networkx and igraph for graph based data manipulations

regular expressions for finding patterns in text data

BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run

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