LFU:online

Under the successful completion of this course, students understand the basics of  data analytics and reporting. They can use selected methods of analyzing data and are capable of interpreting data and presenting it visually and verbally.

This course will give an overview of data, data analytics techniques and tools, and various reporting techniques. Data analytics is the process of examining raw data to uncover patterns, trends, correlations, and insights that can inform decision-making. It involves techniques such as statistical analysis, machine learning, and data visualization to interpret and understand data effectively. Reporting, on the other hand, is the communication of the results of data analysis. It presents the findings in structured formats like dashboards, charts, and written summaries, helping stakeholders make informed decisions. Together, data analytics and reporting play a crucial role in business strategy, operational efficiency, and performance monitoring across various sectors.

Preliminary description of course content:

• Day 1: Introduction to data science and analytics: Data, Features, Preprocessing on data, Cleaning of data, Feature selection techniques like PCA, LDA

• Day 2: Different types of Data Sources: Structured, Unstructured, and Semi-Structured data: Relational Databases: Normal forms, Transactional data, SQL

• Day 3: Outlier detection, summarization, association rule mining, decision tree classification, support vector machine, K-means clustering

• Day 4: Regression models: linear, non-linear, logistic, regularization, overfitting, underfitting, 

• Day 5: Anatomy and types of reports; Predictive analysis and reports based on predictive models; reports based on AI/ML models, Graphs, charts, tables, interactive reports

The mode of the course will contain presentations of lecture slides by lecturer; delivery of hands-on exercises using analysis examples; and the presentation of exercises and mini projects by participants.

It will be held online as a block event from October 20-29, 2025 with an additional course day on December 15th. There will be some meetings towards the end of the course to support students in completing their projects (implementation tasks). Finally, at the last meeting the course participants will be asked to present their work which will be then discussed and evaluated.

Participants are graded through marks of written exams, mini exercises and in-class presentation of their final projects. Details will be announced in the first session.

The reading resources will be provided in the first unit.

For practical components in this course, programming skills in Python are required to achieve the defined learning outcomes. Therefore, knowledge of Python and, in particular, familiarity with general concepts of computer programming (e.g., variables, control structures) is needed. We recommend this course to students who are familiar with Python or are able to learn it quickly thanks to their general programming skills.

Due to a compact schedule, this course is very dense. Therefore, it may take up to full-time in the first week and on average over the entire period, half-time.

The acceptance procedure is based on prioritised randomisation. Students advanced in completion of the Digital Science minor get precedence.