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Course Orientation

Motivation: You are beginning an introductory course in Data Science and Machine Learning. Skills in that domain are increasingly valuable. When you combine them with your existing domain expertise, this will be a real treasure.

In this nugget you will learn who this course is designed for, how class sessions and course materials work together, and why building your learning around a personal project is strongly recommended.

Who This Course Is For

This course was designed with engineering students in mind — people who work with measurement data, calibration curves, and sensor readings, but who may be less confident with terms like "machine learning" or "data science." If that is you, you are closer to being able to use these tools than you think.

Engineers bring domain knowledge that a generic data scientist does not have. Adding data science skills to that foundation produces an interdisciplinary combination that can genuinely stand out.

But the core skill this course builds — turning raw data into reasoned decisions — is not exclusive to engineering. If you come from a business programme, the social sciences, or any field where you work with structured information and want to extract meaning from it, the methods here apply equally well. The way of thinking transfers.

Course prerequisites: Basic Python programming experience. No prior knowledge of statistics or machine learning is assumed. What is assumed: that you are serious about your work and willing to engage.

How This Course Works

Class Sessions

Lectures in this course aim at being discussion sessions, not pure content deliveries. You are expected to read the relevant script nugget before class. Class time is ideally used to work through hard questions together, compare reasoning with fellow students, and test whether you actually understood what you read.

Come prepared. The discussion only works if everyone has done the reading.

Lab Sessions

Lab sessions are where you build hands-on skill. The format may vary: guided notebooks, short exercises, online course segments, or other practical Python programming activities. It depends on the topic. Where possible, you will work with data from your own project.

Work on a Personal Project

The best learning happens when it is tied to personal motivation and stakes. Therefore, working on a personal project alongside is strongly recommended. This can be a question from your own field, approached with real data, carried through a full analysis cycle, and documented as a result.

Your personal project: It can be small. It should be yours.

Every technical block in this course — data handling, modeling, evaluation — can feed directly into what you do with your own dataset. Students who work this way consistently get more out of the course.

The full case for doing a personal project and how to find one is covered in 🖝 Why a Project?.

Course Materials

This Script

This script is the written reference for the entire course. It covers every topic examined in the written exam, explains the reasoning behind each method, and supports every practical exercise. Use it as you would a good textbook: read before class, re-read when something is unclear, return to it during project work.

Each nugget takes roughly ten to fifteen minutes to read carefully. That is the intended preparation time before each topic.

Reading Tip: Take notes and connect what you read to your own knowledge and experience. The goal is not coverage or rote learning. The goal is understanding.

Lab Materials

Lab materials will be a mix of practical resources introduced session by session. The common thread is hands-on Python work applied to real data.

Summary

This course is primarily designed for engineering students, but its methods apply to any field where data informs decisions.
Lectures aim at being structured discussions; lab sessions build hands-on skill through practical exercises.
Anchoring your learning in a personal project is strongly recommended.
This script is the primary written reference: read it, return to it, and use it for revision.
Lab materials will be a mix of formats; the common thread is applied Python programming.

As always: Happy learning, happy life! 🫶