A research scientist in computer and information science invents new methods, tests them rigorously, and turns them into working systems. Day to day you read papers, turn a fuzzy problem into a mathematical model, write code to test it, run experiments, and explain results to people who decide what happens next. Think of a question like 'can we make this model train with less data?' You frame it, form a hypothesis, build a baseline, change one thing, measure, and report honestly. This roadmap builds each skill on the last: math and programming first, then machine learning and systems, then rea
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Stage 1 of 4 · about 40 weeks
Write confident Python, learn core math (linear algebra, calculus, probability, statistics), and understand data structures, algorithms, and Big-O. Use Git and the Bash command line. Finish with 3 small projects, such as implementing sorting, a linear regression from scratch with NumPy, and a simple data analysis.
Confident Python
Python is the working language of research computing. Every idea you test later, from models to experiments, becomes code first. If writing code is slow for you, research feels slow too.
Git and the Bash command line
Researchers live in terminals and on remote machines. Git records every change so you can undo mistakes and prove what you ran. Reproducibility starts here.
Data structures, algorithms and Big-O
Research code often runs on huge inputs. Knowing why one approach takes seconds and another takes days lets you pick the right tool, and it is the core of technical interviews.
Linear algebra and calculus
Models are matrices and functions, and learning means following gradients. Without this you can run machine learning code but not understand or improve it.
Probability and statistics
Data is noisy. Probability gives you the language of uncertainty and statistics tells you whether a result is real or luck. Every experiment you run later depends on this.
Data analysis and technical communication
Research is only useful if others can follow it. Handling real data and explaining results clearly are daily tasks, and they match the writing and reading skills listed for this occupation.
Then 3 more stages (about 300 weeks in all) with lessons, drills, projects, 10 interview questions with strong answers and free resources.