Introduction to Data Science

Course Description

This course introduces the students to five key facets of a data-based research: (1) data wrangling, cleaning, and sampling to obtain a suitable data set, (2) data management to facilitate efficient access to big data, (3) exploratory data analysis to generate hypotheses and intuition, (4) prediction based on statistical methods such as regression and classification, and (5) communication of results through visualization, stories, and interpretable summaries.

Learning Outcomes

  1. Use Python and other tools to scrape, clean, and process data
  2. Use data management techniques to store data locally and in cloud infrastructures
  3. Use statistical methods and visualization to quickly explore data
  4. Apply statistics and computational analysis to make predictions based on data
  5. Describe the outcome of data analysis using descriptive statistics and visualizations
  6. Use cluster and cloud infrastructure to perform data-intensive computation

Forms of Teaching

Lectures

Lectures and examples in jupyter notebook

Exercises

Examples in jupyter notebook

Laboratory

help with projects

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Seminar/Project 25 % 40 % 25 % 40 %
Final Exam: Written 50 % 60 %
Comment:

final exam, data analysis task on a computer

Week by Week Schedule

  1. Course administration. Overview of the data science field. Supporting technologies for data science.
  2. Data handling: data acquisition, data models, common dataset issues, data reshaping, data cleanup. Laboratory: data handling in Python.
  3. Data visualization: various graphs for dataset visualization, best practice for data visualization, visualization for special purposes, visualization tools. Laboratory: data visualization in Python.
  4. Hypothesis testing. Confidence intervals. Relating two variables.
  5. Applied linear regression in descriptive data analysis. Data transformation. Linear regression assumptions.
  6. Data collection by observation.
  7. Applied supervised machine learning (classification and prediction).
  8. --
  9. Applied machine learning (data collection, labeling, discretization, features, normalization, izbor modela, metrics, model assessment).
  10. Applied unsupervised machine learning (clustering).
  11. Introduction to deep learning (neural networks, loss, invariance and equivariance, convolutional neural networks, recurrent networks)
  12. Text handling (text, feature vectors, bag of words, tokenisation, stop words, n-grams, TF/IDF, attention)
  13. Handling graphs and networks (nodes and edges, directed and undirected graphs, centrality measures, Graph convolutional networks)
  14. Project presentations.
  15. Final exam.

Study Programmes

University graduate
[FER3-HR] Audio Technologies and Electroacoustics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Communication and Space Technologies - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computational Modelling in Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Computer Engineering - profile
Elective Course of the Profile (1. semester)
Elective Courses (1. semester)
[FER3-HR] Computer Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Control Systems and Robotics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Data Science - profile
Core-elective courses (1. semester)
[FER3-HR] Electrical Power Engineering - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electric Machines, Drives and Automation - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Electronic and Computer Engineering - profile
Elective Courses (1. semester) (3. semester)
Elective Courses of the Profile (1. semester) (3. semester)
[FER3-HR] Electronics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Information and Communication Engineering - profile
Elective Courses (1. semester) (3. semester)
Elective Courses of the Profile (1. semester)
Elective Coursesof the Profile (3. semester)
[FER3-HR] Network Science - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Software Engineering and Information Systems - profile
Elective Course of the profile (3. semester)
Elective Course of the Profile (1. semester)
Elective Courses (1. semester) (3. semester)

Literature

Jacob T. Vanderplas, Jake VanderPlas (2016.), Python Data Science Handbook, O'Reilly Media
Matt Harrison, Theodore Petrou (2020.), Pandas 1.x Cookbook, Packt Publishing Ltd
Alice Zheng, Amanda Casari (2018.), Feature Engineering for Machine Learning, "O'Reilly Media, Inc."
François Chollet (2021.), Deep Learning with Python, Second Edition, Simon and Schuster

For students

General

ID 240719
  Winter semester
5 ECTS
L1 English Level
L1 e-Learning
45 Lectures
0 Seminar
15 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

88 Excellent
75 Very Good
63 Good
50 Acceptable