Career-Paths

Career Paths in Computer Science: Roles and Skills Technology and theory meet real work in computer science. The field grows in many directions, from building reliable software to protecting data and designing large systems. This guide outlines common roles and the skills they need, so you can explore what fits you. Representative roles Software Engineer: writes code, builds features, tests products, and works with teams to ship software. Data Scientist: explores data, runs experiments, and shares insights that guide decisions. Cybersecurity Specialist: protects networks, detects threats, and strengthens defenses. AI / ML Engineer: develops models, tests ideas, and helps deploy them to real apps. Web Developer: creates user interfaces and connects data to people, with clean, accessible design. Systems Architect: plans big systems, focuses on reliability, scalability, and long-term goals. Core skills that many roles share Programming fundamentals and problem solving: learn at least one language well and practice thinking step by step. Version control and collaboration: use Git, write clear notes, and work on teams. Debugging and testing: find root causes and verify fixes. Communication: explain ideas simply to teammates and users. Learning mindset: keep up with new tools and best practices. How to choose a path Start with what you enjoy. If you like math and data, data science could fit. If you enjoy building things, software engineering might be best. Talk to mentors, read job descriptions, and try small projects or internships. Create a simple plan: pick two areas, complete a couple of projects, and add them to your portfolio. ...

Data Science Careers: Pathways and Skills Data science careers offer options for many interests. In this field, people turn data into decisions using math, software, and clear communication. You can come from math, engineering, business, or science, and grow by doing real projects and learning new tools. Career pathways Data Analyst / Business Intelligence Analyst Data Engineer Data Scientist Machine Learning Engineer Analytics Manager or Lead Data Scientist Core skills and tools Statistics and probability Programming in Python or R SQL and data querying Data wrangling and cleaning Data visualization and storytelling Cloud platforms (AWS, GCP, Azure) and basic MLOps Version control and collaboration Getting started Build a learning plan around fundamentals and small, real projects Create a portfolio with 3 end-to-end projects that show your process Practice SQL and Python hands-on; use notebooks for experiments Study real-world case studies and explain your findings clearly Choosing a path Think about the industry you like: tech, finance, health, or retail. If you enjoy building data pipelines and ensuring data quality, data engineering may fit. If you like modeling and deriving insights, data science or ML engineering could be your path. Start with fundamentals, then pick 1–2 projects that showcase impact in a domain you care about. ...