Mohammad Sabokrou
Associate Professor
Office 218
Data Science Fundamentals
A practical introduction to asking clear questions, preparing trustworthy data, analysing evidence, and communicating responsible conclusions.
16 weeks2h lecture + 2h labAI & Robotics
m.sabokrou@newuu.uz
Email before an office visit to reserve a short time slot.
Amir Hossein Yari
a.hossein@newuu.uz
Send completed lab notebooks to this email.
Email subject: FDS Lab [number] – [Student ID] – [Full Name]
Example: FDS Lab 01 – 20261234 – Ali Karimov
Office visits
When to visit
By appointment in Office 218. Please email first to reserve a short time slot.
Class timetable
| Day | Time | Session | Room | Group(s) |
|---|---|---|---|---|
| Monday | 10:30–12:30 | Lab | MATH-B01 | JCS 1 |
| Wednesday | 13:30–15:30 | Combined lecture | Conference Hall | JCS 1/2 · SAR 1/2 · SSE 1/2 · SCS 1/2 |
Source: official NewUU timetable · verified 13 September 2026.
Course book
Book
Dataset
Fundamentals of Data Science
The book is listed once. Slides and labs are released inside their matching week below.
Volume 1 Main Text
The course text for self-study and reference.
Open bookCourse dataset
Download the synthetic university data and its data dictionary for labs and self-study.
Open datasetsOnly ready student materials are published. Instructor solutions and teaching notes remain private.
16-week learning plan
Select a published week to open its slides and lab. Future weeks have no public files yet.
01
Questions, data, and evidence
Chapters 1–2 · Rows, variables, sources, samples, and responsible framing.
02
Describing data with care
Chapter 3 · Centre, spread, distributions, skew, and outliers.
03
Data cleaning and quality
Chapter 4 · Missingness, duplicates, data types, and reproducible checks.
04
Transformation, pipelines, and leakage
Chapter 5 · Scaling, encoding, features, and safe preprocessing.
05
Exploratory data analysis
Chapter 6 · Patterns, confounding, and careful interpretation.
06
Visual communication and dashboards
Chapter 7 · Clear charts, useful dashboards, and honest messages.
07
Revision and EDA brief
Review labs and consolidate the first part of the course.
08
Midterm exam
Questions, tables, statistics, cleaning, transformation, EDA, and communication.
09
Probability and simulation
Chapter 8 · Base rates, conditional reasoning, and simulation.
10
Sampling and uncertainty
Chapter 9 · Intervals, resampling, power, and effect size.
11
Regression as a workflow
Chapter 10 · Baselines, residuals, and generalisation.
12
Classification and evaluation
Chapters 11–12 · Metrics, thresholds, and fairness checks.
13
Clustering and dimensionality reduction
Chapter 13 · Distance, k-means, PCA, and uncertainty.
14
Text, ethics, and responsible delivery
Chapters 14–15 · Text analysis, privacy, and professional practice.
15
Revision and capstone presentation
Bring evidence, limitations, and recommendations together.
16
Final exam
Assessment across the concepts and methods from the semester.