| Code | DE0750 | |||||||||||||||||||||||||||||||||||||||
| Name | Business Analytics | |||||||||||||||||||||||||||||||||||||||
| Status | Compulsory/Courses of Limited Choice | |||||||||||||||||||||||||||||||||||||||
| Level and type | Post-graduate Studies, Academic | |||||||||||||||||||||||||||||||||||||||
| Field of study | Computer Science | |||||||||||||||||||||||||||||||||||||||
| Faculty | ||||||||||||||||||||||||||||||||||||||||
| Academic staff | Ilze Birzniece | |||||||||||||||||||||||||||||||||||||||
| Credit points | 6.0 | |||||||||||||||||||||||||||||||||||||||
| Parts | 1 | |||||||||||||||||||||||||||||||||||||||
| Annotation |
The volume of data worldwide is growing daily, and potential business value hides in data. Looking for new business opportunities in data today is an essential part of the growth of business in any sector. Business intelligence, dashboarding and data visualization is the starting point for business analytics. Knowledge discovery from data is a process that includes data retrieval, data pre-processing, selection and application of appropriate analytical methods, and interpretation of results. Data mining is the use of statistical and machine-learning techniques on historical data aiming to obtain an explanation or prediction. The course deals with key data mining approaches in supervised and unsupervised learning – regression, classification, clustering and association rules mining ? by introducing the most popular methods in each of them. Text mining and dealing with unstructured and semi-structured data is one of the topical classification targets. The course focuses on building analytical comprehension and practice, using the no-code tool Weka (additionally ? Python programming language for experienced users) to analyse real data sets and interpret the insights. Big data analytics is related to the capabilities of high performance computing. Students work in teams and apply their knowledge and skills in data analytics to develop a capstone project. . The study course is adapted to a blended learning methodology and includes asynchronous and synchronous study activities, as well as the necessary support materials for asynchronous study activities. In this course, students acquire advanced digital skills in accordance with the European Digital Competence Framework for Citizens (DigComp).. The course does not require previous experience in data mining or programming.. |
|||||||||||||||||||||||||||||||||||||||
| Contents |
|
|||||||||||||||||||||||||||||||||||||||
|
Goals and objectives of the course in terms of competences and skills |
The goal of the course is to develop a comprehension of data analytics capabilities and skills to select and apply appropriate approaches to particular business data needs. The objectives of the course: 1. Introduce the needs and opportunities of business analytics. 2. Raise awareness of data extraction and processing to acquire data-driven knowledge. 3. Develop skills to work with data mining techniques and tools for decision support. 4. Promote analytical capabilities, critical thinking and academic writing skills. | |||||||||||||||||||||||||||||||||||||||
|
Learning outcomes and assessment |
Characterize data pre-processing tasks and conduct data transformations - Project, examination Discriminate data mining approaches, select and apply appropriate methods for particular data - Practical works, project, examination Analyze business needs and link them to capabilities data analytics - Home works, project, examination Derive data-driven business decisions - Home work, project, examination, practical works Using data mining tools create solutions for discovering knowledge from data and representing it (DigComp Level 7) - Practical works, project Perform different tasks according the principles of academic integrity - Home works, group work, project, examination, practical works |
|||||||||||||||||||||||||||||||||||||||
| Evaluation criteria of study results |
Home works - 20%
Practical woks (labs) - 15% Group project - 35% Examination - 30% |
|||||||||||||||||||||||||||||||||||||||
| Course prerequisites | Basic knowledge about data storage and processing with application software. | |||||||||||||||||||||||||||||||||||||||
| Course planning |
|
|||||||||||||||||||||||||||||||||||||||