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Career Paths: From Courses to Real Roles

This section helps students connect what they learn in class with practical roles, extra skills to self-study, and portfolio projects needed for internships. Pair it with Choose Your Direction and Career Preparation.

1. Software Engineer​

Relevant courses​

  • Programming Techniques
  • Data Structures & Algorithms
  • Advanced Programming
  • Database Systems
  • Software Engineering
  • Principles of Programming Languages
  • Operating Systems
  • Web Programming
  • Software Testing
  • Software Architecture

What these courses give you​

You will understand programming foundations, algorithms, databases, software design, testing concepts, and how applications interact with operating systems and networks.

What you still need to self-study​

  • Git and GitHub workflow: branch, pull request, merge conflict, code review
  • One practical stack:
    • Backend: Java Spring Boot, Node.js/NestJS, or Python FastAPI
    • Frontend: React and TypeScript
  • REST API, authentication, authorization, JWT
  • SQL in practice: joins, indexing, schema design
  • Docker and deployment basics
  • Unit test, integration test, debugging
  • Linux command line and basic server deployment

Portfolio evidence​

Before applying for an internship, aim to have:

  • 1 backend project with database, authentication, API documentation
  • 1 team project using GitHub issues, branches, pull requests
  • 1 deployed application or demo video
  • A clean README explaining architecture, setup, features, and trade-offs

Typical roles​

Backend Intern, Frontend Intern, Full-stack Intern, Mobile Developer Intern, QA Engineer Intern, Junior Software Engineer.

2. AI / Machine Learning Engineer​

Relevant courses​

  • Linear Algebra
  • Probability & Statistics
  • Discrete Structures
  • Programming for Artificial Intelligence & Data Science
  • Introduction to Artificial Intelligence
  • Machine Learning
  • Deep Learning & Applications
  • Natural Language Processing
  • Digital Image Processing & Computer Vision
  • Data Mining

What these courses give you​

You will gain the mathematical and theoretical base for machine learning, model training, image processing, NLP, and data-driven problem solving.

What you still need to self-study​

  • Python ecosystem: NumPy, Pandas, Matplotlib, Scikit-learn
  • PyTorch or TensorFlow
  • Dataset collection, cleaning, labeling, and versioning
  • Model evaluation: precision, recall, F1-score, confusion matrix
  • Experiment tracking and reproducibility
  • GPU environment, CUDA basics, Colab/Kaggle workflow
  • Serving models through FastAPI or Flask
  • Docker for packaging and deployment
  • Reading papers and reproducing a baseline model

Portfolio evidence​

  • 1 end-to-end ML project: data β†’ training β†’ evaluation β†’ inference API
  • 1 focused Computer Vision or NLP project
  • A report explaining dataset, metrics, failed experiments, and improvements
  • GitHub repository with reproducible environment and trained-model instructions

Typical roles​

AI Intern, Machine Learning Intern, Computer Vision Intern, NLP Intern, Data Science Intern, AI Research Assistant.

3. Data Analyst / Data Engineer​

Relevant courses​

  • Database Systems
  • Database Management Systems
  • Data Mining
  • Data Warehousing & Decision Support Systems
  • Big Data Analytics & Business Intelligence
  • Big Data
  • Systems Analysis & Design
  • Digital Transformation

What these courses give you​

You will understand database systems, analytics concepts, data mining, data warehousing, business intelligence, and system-level data flows.

What you still need to self-study​

  • SQL deeply: joins, CTE, window functions, query optimization
  • Python with Pandas and data-cleaning workflow
  • Excel for practical business data handling
  • Power BI or Tableau for dashboards
  • ETL pipeline concepts: extract, transform, load
  • APIs, JSON, CSV, data validation
  • Basic cloud storage and data warehouse concepts
  • Docker and scheduling basics
  • How to explain insights to non-technical stakeholders

Portfolio evidence​

  • 1 dashboard project using Power BI, Tableau, or Streamlit
  • 1 SQL-heavy project with a realistic database schema
  • 1 ETL pipeline from API/raw files into a database
  • 1 business-style report: problem, KPI, analysis, recommendation

Typical roles​

Data Analyst Intern, BI Intern, Data Engineer Intern, Product Analyst Intern, Business Intelligence Intern.

4. Embedded / IoT Engineer​

Relevant courses​

  • Digital Systems
  • Programming Techniques
  • Computer Architecture
  • Operating Systems
  • Computer Networks
  • IoT Application Development
  • Parallel Computing
  • Intelligent Systems

What these courses give you​

You will understand digital logic, programming, computer architecture, networking, and the foundations needed to build connected devices.

What you still need to self-study​

  • C/C++ for embedded systems
  • Microcontroller platforms: STM32 or ESP32
  • UART, SPI, I2C, CAN, USB, Ethernet
  • GPIO, interrupts, timers, PWM, ADC
  • FreeRTOS or Zephyr basics
  • Debugging with serial logs, ST-Link/J-Link, logic analyzer, oscilloscope
  • MQTT, HTTP, WebSocket
  • PCB basics and reading schematics
  • Linux basics for Raspberry Pi or edge devices
  • Device documentation and test reports

Portfolio evidence​

  • 1 STM32 or ESP32 firmware project
  • 1 sensor-to-cloud IoT project using MQTT or HTTP
  • 1 project with a clear block diagram, wiring diagram, BOM, and source code
  • 1 debugging write-up explaining an actual issue and how it was fixed

β†’ Hands-on labs: Courses β†’ Microcontrollers and Courses β†’ IoT. Bench tools: Lab Equipment Guides.

Typical roles​

Embedded Firmware Intern, IoT Intern, Hardware–Software Integration Intern, Edge AI Intern, Robotics Software Intern.

5. Cybersecurity / Network Engineer​

Relevant courses​

  • Computer Networks
  • Operating Systems
  • Network Administration
  • Cryptography & Network Security
  • Network Security Assessment
  • Information System Security
  • Software Security
  • Mobile Device Systems

What these courses give you​

You will understand network protocols, operating systems, core security principles, cryptography, and security assessment concepts.

What you still need to self-study​

  • Linux administration and Bash
  • TCP/IP, DNS, HTTP/HTTPS, TLS, routing, VLAN
  • Wireshark and packet analysis
  • Python or Bash automation
  • Web security: OWASP Top 10
  • Vulnerability scanning and basic hardening
  • Log collection and analysis
  • Docker and network lab setup
  • Writing clear security reports with evidence and remediation steps

Portfolio evidence​

  • A home lab diagram with virtual machines, firewall, VLAN, and monitoring
  • Wireshark packet-analysis report
  • Security assessment of a deliberately vulnerable lab application
  • CTF write-ups showing methodology, not just flags
  • Secure API demo with authentication, validation, and logging

Typical roles​

Security Analyst Intern, SOC Intern, Network Engineer Intern, Penetration Testing Intern, Application Security Intern.

6. Robotics / Computer Vision Engineer​

Relevant courses​

  • Computer Architecture
  • Operating Systems
  • Computer Networks
  • Introduction to Artificial Intelligence
  • Machine Learning
  • Deep Learning & Applications
  • Digital Image Processing & Computer Vision
  • Intelligent Systems
  • IoT Application Development

What these courses give you​

You will have the software, AI, systems, and networking base needed for robot perception and autonomous systems.

What you still need to self-study​

  • C++ and Python for robotics
  • ROS 2: nodes, topics, services, launch files, TF frames
  • Linux and Docker
  • OpenCV and camera calibration
  • Sensor interfaces: camera, IMU, LiDAR, encoder
  • Coordinate systems, transforms, and basic kinematics
  • Motor control and embedded communication
  • Simulation with Gazebo, Isaac Sim, or similar tools
  • System integration and field testing

Portfolio evidence​

  • ROS 2 simulation project
  • Camera-based object detection or tracking demo
  • Robot or edge-device system diagram
  • Video demo showing the system operating in real conditions
  • README covering calibration, limitations, and test results

β†’ Related guide: Robotics & ROS. Real example: VR Teleoperation of a Denso VS-6577 Robot Arm.

Typical roles​

Robotics Intern, Computer Vision Intern, Autonomous Systems Intern, Perception Engineer Intern, Industrial Automation Intern.

7. Product / Technical Business Analyst​

Relevant courses​

  • Database Systems
  • Data Mining
  • Systems Analysis & Design
  • Software Project Management
  • Professional Skills for Engineers
  • Digital Transformation
  • Big Data Analytics & Business Intelligence

What these courses give you​

You will understand systems, data, project workflows, and how to communicate technical work in a structured way.

What you still need to self-study​

  • Excel and Power BI
  • SQL and practical dashboards
  • Requirement gathering and user stories
  • Product metrics and KPI thinking
  • Writing technical documents, proposals, and reports
  • Presentation and stakeholder communication
  • Basic UX/UI and Figma
  • Agile/Scrum workflow: backlog, sprint, ticket, retrospective

Portfolio evidence​

  • Dashboard with a short business recommendation
  • Product requirement document for a technical feature
  • User-flow or wireframe for a small application
  • A project presentation explaining problem, users, solution, metrics, and trade-offs

Typical roles​

Business Analyst Intern, Product Analyst Intern, Technical Product Intern, Solutions Engineer Intern, Product Operations Intern.

Skills That Are Not Optional​

Regardless of role, students should build these alongside coursework.

By the end of Year 1​

  • One programming language used confidently
  • GitHub profile and basic Git workflow
  • Basic Linux / WSL usage
  • Technical English reading habit
  • At least two small personal projects

By the end of Year 2​

  • Data Structures & Algorithms practice
  • OOP and database fundamentals
  • Team project using Git
  • Basic documentation and presentation skills
  • One project that is complete enough to show publicly

By the end of Year 3​

  • Choose one primary career direction
  • Build one substantial project in that direction
  • Learn the industry tools relevant to that role
  • Start applying for research, competitions, and internships
  • Prepare CV, LinkedIn, GitHub, and portfolio

Before internship or graduation project​

  • Read and understand job descriptions
  • Match missing skills against your learning plan
  • Have evidence, not just course grades:
    • source code
    • demo
    • technical report
    • project presentation
    • team contribution history
  • Be able to explain what you built, why you made technical decisions, and what you would improve