Tech Jobs at Percona
Remote & Onsite

1. MySQL Product Manager
2. Redis/Valkey Database Engineer
3. Senior Software Engineer (C++ / Mongo)

Apply Here: https://kenyatrends.co.ke/tech-jobs-at-percona/

JavaScript Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to JavaScript
| | |-- Setting Up Development Environment (IDE: VSCode, Sublime Text, etc.)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables (var, let, const) and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| | |-- For...in and For...of Loops
| |
| |-- Exception Handling
| | |-- Try-Catch Block
| | |-- Finally Block
| | |-- Throwing Errors
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Declarations
| | |-- Function Expressions
| | |-- Arrow Functions
| |
| |-- Parameters and Arguments
| | |-- Default Parameters
| | |-- Rest and Spread Operators
| |
| |-- Scope
| | |-- Global and Local Scope
| | |-- Hoisting
| | |-- Closures
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Objects and Properties
| | |-- Methods
| |
| |-- Prototypes and Inheritance
| | |-- Prototype Chain
| | |-- Inheritance with Prototypes
| |
| |-- Classes
| | |-- Class Syntax
| | |-- Constructors
| | |-- Inheritance (extends and super)
| |
| |-- Encapsulation
| | |-- Private and Public Members (using # for private)
|
|-- Advanced JavaScript
| |-- Asynchronous JavaScript
| | |-- Callbacks
| | |-- Promises
| | |-- Async/Await
| |
| |-- Event Loop
| | |-- Understanding the Event Loop
| | |-- Microtasks and Macrotasks
|
|-- Data Structures
| |-- Arrays
| | |-- Array Methods (map, filter, reduce, etc.)
| | |-- Array Manipulation
| |
| |-- Objects
| | |-- Creating and Manipulating Objects
| | |-- Object Methods (keys, values, entries)
| |
| |-- Sets and Maps
| | |-- Working with Sets
| | |-- Working with Maps
|
|-- Browser APIs
| |-- Document Object Model (DOM)
| | |-- Selecting Elements
| | |-- Manipulating Elements
| | |-- Event Handling
| |
| |-- Fetch API
| | |-- Making HTTP Requests
| | |-- Handling Responses
| |
| |-- Web Storage
| | |-- LocalStorage and SessionStorage
|
|-- Libraries and Frameworks
| |-- jQuery
| | |-- Basics of jQuery
| | |-- DOM Manipulation with jQuery
| |
| |-- React
| | |-- Components and JSX
| | |-- State and Props
| | |-- Lifecycle Methods
| |
| |-- Angular
| | |-- Components and Templates
| | |-- Services and Dependency Injection
| | |-- Routing
| |
| |-- Vue
| | |-- Vue Instance
| | |-- Templates and Directives
| | |-- Vue Router
|
|-- Build Tools and Module Bundlers
| |-- NPM and Yarn
| | |-- Package Management
| | |-- Scripts and Dependencies
| |
| |-- Webpack
| | |-- Module Bundling
| | |-- Loaders and Plugins
| |
| |-- Babel
| | |-- Transpiling JavaScript
| | |-- Using Presets and Plugins
|
|-- Testing in JavaScript
| |-- Unit Testing
| | |-- Jest (Setup, Writing Tests, Mocking)
| | |-- Mocha and Chai
| |
| |-- End-to-End Testing
| | |-- Cypress
| | |-- Selenium WebDriver
|
|-- Deployment and DevOps
| |-- Continuous Integration/Continuous Deployment (CI/CD)
| | |-- GitHub Actions
| | |-- Travis CI
| |
| |-- Containers and Microservices
| | |-- Docker (Dockerfile, Image Creation, Container Management)
| | |-- Kubernetes (Pods, Services, Deployments, Managing JavaScript Applications on Kubernetes)

More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Telegram Channel
https://t.me/TechPsyche

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More than 40,000 people have gotten developer jobs after completing these – including at big companies like Google and Microsoft.
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𝟮. 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗮𝗿𝗮𝗴𝗲
Discover a range of free learning content. You can learn by selecting individual modules, or dive right in and take an entire course end-to-end.
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Back End Engineer Jobs at Deel
Locations: Europe, Middle East, Africa
1. Senior Back-End Engineer - Fintech
2. Senior Backend Engineer (Node.js + AWS)
3. Team Lead, Engineering (Node.js/Typescript)
Apply Here: https://kenyatrends.co.ke/backend-engineer-jobs-at-deel/

Remote Jobs at Quora

1. Data Scientist - Monetization, Quora (Remote)
2. Senior Product Manager - Ads, Quora (Remote)
3. Principal Product Manager - Creators, Poe (Remote)
4. Product Designer - Quora (Remote)
5. Staff Software Engineer - Developer Tools, Poe (Remote)
6. Staff Security Software Engineer (Remote)
7. Staff Machine Learning Engineer - Poe (Remote)
8. Staff Full Stack Software Engineer - Creators, Poe (Remote)
9. Staff Full Stack Software Engineer - Core Product, Poe (Remote)

Apply Here: https://kenyatrends.co.ke/remote-jobs-at-quora/

Steve Jobs talks about his Journey with Apple.

At one point he was fired from his own Company!!
He talks about why he dropped out of Reed College.
Also what kept him moving and focused on his goals.

Watch Here: https://youtu.be/xIKS2EK2Gzg

🌟 We’re Hiring: Full Stack Developer - Innovate, Build, Lead! 🌟

Are you a problem-solving powerhouse with a passion for building innovative tech solutions? 🚀 We’re looking for a Full Stack Developer to join our dynamic team and help us tackle exciting challenges in the tech space.
What You’ll Work On

💻 Frontend: Vue.js, React.js
🖥 Backend: Python (Django Framework), Golang
🌐 Cloud: Google Cloud, Azure, Terraform (a plus!)
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⚡️ Embedded Systems: Basic knowledge of Linux embedded systems
Why Join Us?

✨ Be part of a forward-thinking, innovative team
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🔍 Someone who thrives under pressure and loves a challenge
🤓 A self-starter with excellent problem-solving skills
🌟 A tech enthusiast who’s experienced with remote collaboration and agile methodologies

This isn’t just another job – it’s an opportunity to create, innovate, and leave your mark on the tech world.

💡 If this sounds like you, let’s talk! Apply now or share with someone who fits this description.
📩 admin@aredgroup.com

If You Are a Software Developer, Keep This in Mind

1. Keep It Simple
Complexity is your enemy. Write code that’s easy to understand and maintain.

2.Focus on User Needs
Always think about the end-user. Build software that solves real problems.

3. Test Early, Test Often
Testing is not an option. Catch bugs early by writing tests and using automation tools.

4. Write Clear Code
Make sure your code is easy to read and understand for others (and your future self).

5. Document Your Code
Well-documented code saves time and helps others understand your work.

6. Modularize Your Code
Break your code into smaller, reusable parts. It’s easier to manage and update.

7. Keep Learning
Technology changes fast. Stay curious and keep up with new tools and best practices.

Remote Jobs at Sportserve

1. Software Development Manager
2. Fraud and Risk Analyst/Associate
3. Web3 Full Stack Developer (NodeJS & VueJS)
4. Senior Fullstack Developer (React, NextJS, NodeJS)
5. DevOps Full Stack Developer (Web3)
6. IT Incident Manager

Apply Here: https://kenyatrends.co.ke/remote-jobs-at-sportserve/

Git Cheat Sheet Here:
https://t.me/TechPsyche/131

jQuery Cheat Sheet Here:
https://t.me/TechPsyche/133

Python Cheat Sheet Here:
https://t.me/TechPsyche/135

JavaScript Cheat Sheet
https://t.me/TechPsyche/136

Rust Cheat Sheet
https://t.me/TechPsyche/137

SQL Cheat Sheet Here: [Geeks for Geeks]
https://t.me/TechPsyche/138

SQL For Data Science Cheat Sheet Here: [DataCamp]
https://t.me/TechPsyche/139

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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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

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

SQL in 30 Days
Week 1: Beginner Level

Day 1-3: Introduction and Setup
1. Day 1: Introduction to SQL, its importance, and various database systems.
2. Day 2: Installing a SQL database (e.g., MySQL, PostgreSQL).
3. Day 3: Setting up a sample database and practicing basic commands.

Day 4-7: Basic SQL Queries
4. Day 4: SELECT statement, retrieving data from a single table.
5. Day 5: WHERE clause and filtering data.
6. Day 6: Sorting data with ORDER BY.
7. Day 7: Aggregating data with GROUP BY and using aggregate functions (COUNT, SUM, AVG).

Week 2-3: Intermediate Level

Day 8-14: Working with Multiple Tables
8. Day 8: Introduction to JOIN operations.
9. Day 9: INNER JOIN and LEFT JOIN.
10. Day 10: RIGHT JOIN and FULL JOIN.
11. Day 11: Subqueries and correlated subqueries.
12. Day 12: Creating and modifying tables with CREATE, ALTER, and DROP.
13. Day 13: INSERT, UPDATE, and DELETE statements.
14. Day 14: Understanding indexes and optimizing queries.

Day 15-21: Data Manipulation
15. Day 15: CASE statements for conditional logic.
16. Day 16: Using UNION and UNION ALL.
17. Day 17: Data type conversions (CAST and CONVERT).
18. Day 18: Working with date and time functions.
19. Day 19: String manipulation functions.
20. Day 20: Error handling with TRY...CATCH.
21. Day 21: Practice complex queries and data manipulation tasks.

Week 4: Advanced Level

Day 22-28: Advanced Topics
22. Day 22: Working with Views.
23. Day 23: Stored Procedures and Functions.
24. Day 24: Triggers and transactions.
25. Day 25: Security and user privileges.
26. Day 26: Performance tuning and query optimization.
27. Day 27: Introduction to NoSQL databases (optional).
28. Day 28: Working with NoSQL databases (optional).

Day 29-30: Real-World Applications
29. Day 29: Building a simple application that uses SQL.
30. Day 30: Final review and practice, explore advanced topics in depth, or work on a personal project.

Remember to practice regularly, work on small projects, and use online resources and SQL platforms for hands-on experience. Adjust the plan based on your progress and interests, and you'll be well on your way to becoming proficient in SQL!

SQL for Data Analysis: https://t.me/SQLforDataAnalysisTP

Follow this Channel for More Tips:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

*Complete Roadmap to learn SQL in 2025* 👇👇

1. Basic Concepts
- Understand databases and SQL.
- Learn data types (INT, VARCHAR, DATE, etc.).

2. Basic Queries
- SELECT: Retrieve data.
- WHERE: Filter results.
- ORDER BY: Sort results.
- LIMIT: Restrict results.

3. Aggregate Functions
- COUNT, SUM, AVG, MAX, MIN.
- Use GROUP BY to group results.

4. Joins
- INNER JOIN: Combine rows from two tables based on a condition.
- LEFT JOIN: Include all rows from the left table.
- RIGHT JOIN: Include all rows from the right table.
- FULL OUTER JOIN: Include all rows from both tables.

5. Subqueries
- Use nested queries for complex data retrieval.

6. Data Manipulation
- INSERT: Add new records.
- UPDATE: Modify existing records.
- DELETE: Remove records.

7. Schema Management
- CREATE TABLE: Define new tables.
- ALTER TABLE: Modify existing tables.
- DROP TABLE: Remove tables.

8. Indexes
- Understand how to create and use indexes to optimize queries.

9. Views
- Create and manage views for simplified data access.

10. Transactions
- Learn about COMMIT and ROLLBACK for data integrity.

11. Advanced Topics
- Stored Procedures: Automate complex tasks.
- Triggers: Execute actions automatically based on events.
- Normalization: Understand database design principles.

12. Practice
- Use platforms like LeetCode, HackerRank, or learnsql for hands-on practice.

Here are some free resources to learn  & practice SQL 👇👇

More SQL Learning Resources: https://t.me/TechPsyche

Udacity free course- https://techurl.in/tYrRG

SQL For Data Analysis: https://t.me/SQLResourcesTP

For Practice- https://stratascratch.com/?via=free

SQL in 30 Days: https://t.me/SQLResourcesTP/6

Top 10 SQL Projects with Datasets: https://t.me/DataScienceResourcesTP/5

Join for more free resources: https://t.me/TechPsyche

ENJOY LEARNING 👍👍

Please go through this top 10 SQL projects with Datasets that you can practice and can add in your resume

📌1. Social Media Analytics:
(https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset)

🚀2. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)

📌3. HR Analytics:
(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-
attrition-dataset)

🚀4. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)

📌5. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)

🚀6. Inventory Management:
(https://www.kaggle.com/datasets?
search=inventory+management)

📌 7.Customer Relationship Management:
(https://www.kaggle.com/pankajjsh06/ibm-watson-
marketing-customer-value-data)

🚀8. Financial Data Analysis:
(https://www.kaggle.com/awaiskalia/banking-database)

📌9. Supply Chain Management:
(https://www.kaggle.com/shashwatwork/procurement-analytics)

🚀10. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)

Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself.

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

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

Bookmark Post for Later Use: https://tinyurl.com/SQLProjectsDatasets

Hope this piece of information helps you

𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐝𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:

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📍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.

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

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Hope you'll like it

Like this post if you need more resources like this 👍❤️

Telegram Channel: https://t.me/DataAnalysisResourcesTP

Follow this Channel for More:
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150+ Best Programming Books Collection

Reading programming books is key to building a solid foundation in software development. Unlike online tutorials & articles, books offer structured, in-depth coverage of essential concepts like algorithms, data structures, and design patterns. They also provide expert insights, real-world examples, and best practices that help avoid common mistakes and enhance problem-solving skills.

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Reading programming books is a valuable way to strengthen technical skills, gain practical knowledge, and become a more proficient developer

Here's a collection of 150+ Programming & Tech Books
https://topmate.io/learning_resources/1362011

Web Development Beginner Level Projects

Web Development Intermediate Level Projects