Tech Career Path: A Guide for Computer Science Students

 

المسار	اللغات الأساسية / قواعد البيانات	أهم التقنيات والأدوات	ما يجب تعلمه	Roles
Software Development –Backend- Java تطوير البرمجيات - جافا	Java, SQL/Oracle	Spring Boot, Spring MVC, JPA/Hibernate, Maven/Gradle, Git, Docker	OOP, Algorithms, Data Structures, REST APIs, Databases, Authentication	Java Backend Developer, Software Engineer
Software Development – Backend - C# / .NET تطوير البرمجيات - سي شارب	C#, SQL/Oracle	.NET, ASP.NET Core, Entity Framework, LINQ, NuGet, Git, Docker	OOP, Algorithms, Data Structures, REST APIs, Web APIs, SignalR, Databases, Authentication	.NET Developer, Backend Developer, Software Engineer
Software Development – Backend - Python تطوير البرمجيات - بايثون	Python, SQL	Django / FastAPI / Flask, SQLAlchemy, Git, Docker	OOP, APIs, Databases, Authentication	Python Developer, Backend Developer
Frontend Development تطوير الواجهات الامامية	HTML, CSS, JavaScript, TypeScript	React, Angular, Vue, npm, Git	DOM, Responsive Design, REST APIs, State Management	Frontend Developer, Web Developer
Full-Stack Development تطوير البرمجيات (تطوير الواجهات الخلفية + امامية)	JavaScript/TypeScript + Backend Language (C#/Java/Python)  + SQL/Oracle/Postgres	React/Angular + Spring/.NET/Node.js + Docker	Frontend + Backend + Database + Deployment	Full-Stack Developer
Software Testing / QA اختبار البرمجيات	Java / C# / Python	Selenium, Playwright, Cypress, Postman, JUnit, TestNG, NUnit, Git, CI/CD	Test Cases, Test Plans, API Testing, UI Automation, Regression Testing	QA Engineer, Automation Tester, SDET
Systems Analysis تحليل النظم	SQL + أساسيات البرمجة	UML, BPMN, Jira, Confluence, Draw.io, SQL	Requirements, Use Cases, User Stories, Process Modeling, Database Modeling	System Analyst, Business/System Analyst
Data Analysis تحليل البيانات	Python, SQL	Pandas, NumPy, Excel, Power BI, Tableau, Jupyter	Statistics, Data Cleaning, Visualization, SQL Queries	Data Analyst, BI Analyst
Data Science / AI علوم البيانات - الذكاء الاصطناعي	Python, SQL	NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow, Jupyter	Statistics, ML, Data Processing, Model Evaluation	Data Scientist, ML Engineer
DevOps / Cloud نهج التطوير والتشغيل / الحوسبة السحابية	Bash, Python + أساسيات لغة برمجة	Linux, Git, Docker, Kubernetes, CI/CD, AWS/Azure/GCP	Linux, Networking, Containers, Automation, Deployment	DevOps Engineer, Cloud Engineer

A → Z Software Development Lifecycle

A strong end-to-end process can be organized into 10 major stages, with specific deliverables/documents at each stage. 

#	Stage	Main question	Key outputs / documents
1	Idea & Discovery	What are we trying to solve?	Product Vision, Problem Statement, Business Case
2	Feasibility & Planning	Is it possible and worthwhile?	Feasibility Study, Project Charter, Initial Plan
3	Requirements	What exactly should the system do?	BRD, PRD, SRS, User Stories, Use Cases
4	Analysis	How should the requirements work together?	Process Models, Use Cases, Domain Model, Data Requirements
5	Architecture & Design	How will we build it?	Architecture Document, HLD, LLD, API/Data Design
6	Development	How do we implement it?	Source Code, Code Standards, Build Configuration
7	Testing & Quality	Does it work correctly and safely?	Test Strategy, Test Plan, Test Cases, Automation, Defects
8	Release Preparation	Is it ready for production?	Release Plan, Release Notes, Deployment Plan, Go-Live Checklist
9	Deployment & Delivery	How do we put it into production?	Deployment Scripts, CI/CD Pipeline, Runbook
10	Operations & Maintenance	How do we keep improving it?	Monitoring, Incident Reports, Change Requests, Maintenance Releases

And then the lifecycle repeats:

Idea → Requirements → Design → Development → Testing → Release → Production → Feedback → Change → Development…

Thinking about the lifecycle as four connected chains:

1. Business chain

Problem → Business Goal → Product → Requirements

2. Engineering chain

Requirements → Analysis → Architecture → Design → Code

3. Quality chain

Requirements → Risk → Test Analysis → Test Design → Automation

→ Evidence → Quality Decision

4. Delivery chain

Code → Build → Test → Deploy → Monitor → Feedback

This gives you a much more mature view of software engineering than simply:

Requirements → Development → Testing → Deployment.