Career Guidance

Career Paths in Computer Science and Tech The tech field offers many routes. You can work with code, data, networks, devices, or people who use tech. The path you choose often matches your interests, your strengths, and your life goals. You don’t need one single road. You can switch later as you learn more. Common roles and what they involve: Software developer: builds apps and programs. You write code, test features, and fix bugs. Typical routes include a computer science degree, a coding bootcamp, or strong self-study with projects. Data scientist: turns data into insights. You work with statistics, Python, and dashboards. A degree in data, math, or CS helps, plus hands-on projects. Cybersecurity analyst: protects systems from threats. You monitor networks, respond to incidents, and follow security rules. Certifications like CompTIA Security+ can help. DevOps engineer: bridges development and operations. You automate deployments, monitor systems, and keep reliability high. Learn cloud basics and scripting. Product manager in tech: guides a product from idea to launch. You learn user needs, plan roadmaps, and work with engineers and designers. Hardware or embedded engineer: designs devices, sensors, or chips. This path blends software with electronics and often requires hands-on projects and an engineering degree. AI/ML engineer: builds intelligent software. You work with models, data, and experimentation. Learn math, Python, and ML frameworks. How to choose a path: ...

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. ...

Data Science Careers: Skills, Tools and Pathways Data science careers offer a mix of problem solving, data wrangling, and teamwork. People enter from many backgrounds, from statistics to engineering. Most roles share a core set of skills, plus role-specific tools. This guide outlines the skills, tools and pathways to help you plan your path. Core skills you will use every day include: Statistics and math fundamentals: probability, descriptive statistics, hypothesis testing. Programming: Python or R, with libraries like pandas, NumPy, scikit-learn. Data wrangling: cleaning, merging, and transforming messy data. Visualization and storytelling: turning numbers into clear charts and messages. Communication and business sense: framing problems, asking the right questions, presenting results. Essential tools and platforms you should know: ...