Data Analytics and Mining

2025/2026

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered by:

School of Data Analysis and Artificial Intelligence

Type:

Mago-Lego

When:

1, 2 module

Instructors

Kogan, Alexandra

Sohrabi, Majid

Course Syllabus

Abstract

This course serves as an introduction to Data Analytics and Mining, offering participants a foundational understanding of essential concepts and techniques in the field. During this course, students become acquainted with math concepts related to data science and master basic methods of collecting, processing, and transforming data using Python. The curriculum covers fundamental principles such as data preprocessing, visualization, classical machine learning methods, and deep neural networks. Topics include basics of classification methods, regression, image recognition, and natural language processing. Emphasizing practical skills, the course explores Python as a primary tool due to its accessibility and widespread use in data analysis. Geared towards beginners, it covers fundamental principles including data importation, storage, manipulation, and basic analytical methods. Designed to accommodate students with python programming experience, the course serves as a springboard into more specialized areas within Data Analytics and Mining, such as machine learning, statistical data processing, and data visualization.

Learning Objectives

• The students will get familiar with Data Analysis and Mining techniques as well as basic concepts in Machine Learning. How to work with tabular data, preprocessing, cleaning, and exploratory data analysis. Also, the students will learn how to implement a simple machine-learning model for prediction task.

Expected Learning Outcomes

• Students get familiar with Overview of Data Analysis and Mining, Importance and Applications in Industry, Types of Data: Structured vs. Unstructured, Overview of the Data Mining Process
• Students get familiar with Data Cleaning
• Students get familiar with EDA and data visualization
• Students get familiar with statistical analysis
• Students get familiar with Data Mining Techniques
• Students get familiar with Machine Learning approaches
• Students get familiar with Advanced Data Mining and Machine Learning
• Students get familiar with Data Visualization and Reporting

Course Contents

• Introduction to Data Analysis and Mining
• Data Preprocessing and Cleaning
• Exploratory Data Analysis (EDA)
• Statistical Foundations for Data Analysis
• Data Mining Techniques
• Machine Learning Fundamentals
• Advanced Data Mining and Machine Learning
• Data Visualization and Reporting

Assessment Elements

• Quiz
  In-class mini quizzes are held weekly or biweekly. Each quiz consists of several questions and is graded on a 10-point scale. All quizzes are conducted through the LMS platform.
• Final Project

Interim Assessment

• 2025/2026 2nd module
  0.5 * Quiz + 0.5 * Final Project

Bibliography

Recommended Core Bibliography

• Han, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques (Vol. 3rd ed). Burlington, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=377411
• Han, J., Kamber, M., Pei, J. Data Mining: Concepts and Techniques, Third Edition. – Morgan Kaufmann Publishers, 2011. – 740 pp.
• Hastie, T., Tibshirani, R., Friedman, J. The elements of statistical learning: Data Mining, Inference, and Prediction. – Springer, 2009. – 745 pp.

Recommended Additional Bibliography

• Core concepts in data analysis: summarization, correlation and visualization, Mirkin, B., 2011

Authors

• SOHRABI MAJID
• Антропова Лариса Ивановна
• KOGAN ALEKSANDRA SERGEEVNA