DTS 2122 units200 LevelFirst Semester

Introduction to Data Science

B.Sc. Data Science, University of Uyo

DTS 212 (Introduction to Data Science) is a 2-unit, 200 Level course taught in the First Semester at the University of Uyo, Akwa Ibom State, Nigeria.

The formal treatment of the data science process, building on UUY-DTS 111: problem formulation, data acquisition, feature construction, model families, evaluation, and the ethics and limits of inference from data.

On Areté, DTS 212 comes with

  • 2 recommended textbooks
  • an AI tutor that has read this course’s outline and notes
Sign in to study DTS 212

Free for University of Uyo students — sign in with your email, no password.

Course outline

  1. 01Framing a data science problem: from a vague question to a measurable target
  2. 02Data acquisition strategies and sampling; sampling bias and its consequences
  3. 03Data cleaning and preparation: missing data mechanisms, imputation, outlier treatment
  4. 04Feature engineering, encoding categorical variables, scaling and normalisation
  5. 05Exploratory data analysis: distributions, relationships, and the grammar of graphics
  6. 06Supervised vs unsupervised learning; regression vs classification
  7. 07Model evaluation: train/test split, cross-validation, and the bias–variance trade-off
  8. 08Overfitting, underfitting and regularisation as concepts
  9. 09Communicating results; reproducibility and the ethics of data-driven claims

Recommended textbooks

  • An Introduction to Statistical Learning — James, Witten, Hastie & Tibshirani

    2nd ed. — free at statlearning.com; the single best book for this course

  • Python Data Science Handbook — Jake VanderPlas

    Free at jakevdp.github.io; the practical companion

How to pass DTS 212

  • The bias–variance trade-off is the conceptual centre of this course and of the whole degree — be able to explain it with a picture, in words, and with a formula
  • Never evaluate a model on data it was trained on. Internalise this now and it will save your final-year project
  • Watch the free ISLR video lectures from the authors; they follow the book chapter by chapter
  • For every technique, learn the assumption it makes and what breaks when the assumption fails — that is what distinguishes a data scientist from a button-pusher

DTS 212 — questions and answers

What is DTS 212 at the University of Uyo?
DTS 212 (Introduction to Data Science) is a 2-unit, 200 Level course taught in the First Semester at the University of Uyo, Akwa Ibom State, Nigeria. The formal treatment of the data science process, building on UUY-DTS 111: problem formulation, data acquisition, feature construction, model families, evaluation, and the ethics and limits of inference from data.
What topics are covered in DTS 212?
The DTS 212 outline covers 9 topics: Framing a data science problem: from a vague question to a measurable target; Data acquisition strategies and sampling; sampling bias and its consequences; Data cleaning and preparation: missing data mechanisms, imputation, outlier treatment; Feature engineering, encoding categorical variables, scaling and normalisation; Exploratory data analysis: distributions, relationships, and the grammar of graphics; Supervised vs unsupervised learning; regression vs classification; Model evaluation: train/test split, cross-validation, and the bias–variance trade-off; Overfitting, underfitting and regularisation as concepts; Communicating results; reproducibility and the ethics of data-driven claims.
What textbooks are recommended for DTS 212?
Recommended reading for DTS 212: An Introduction to Statistical Learning by James, Witten, Hastie & Tibshirani; Python Data Science Handbook by Jake VanderPlas.
How do I pass DTS 212?
The bias–variance trade-off is the conceptual centre of this course and of the whole degree — be able to explain it with a picture, in words, and with a formula Never evaluate a model on data it was trained on. Internalise this now and it will save your final-year project Watch the free ISLR video lectures from the authors; they follow the book chapter by chapter For every technique, learn the assumption it makes and what breaks when the assumption fails — that is what distinguishes a data scientist from a button-pusher
Where can I find DTS 212 lecture notes and past questions?
Areté (https://www.aretecyb.tech/courses/dts-212) carries 2 recommended textbooks for DTS 212, plus an AI tutor that has read the outline. It is free for University of Uyo students — sign in with your email, no password.

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