| 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 lay in the data. Looking for new business opportunities in data today is an essential part of the growth of business in any sector. Knowledge discovery from data is a helical 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 from supervised and unsupervised learning – regression, classification, clustering and association rules mining - by introducing the most popular methods in each of them. The need and opportunities for analytics arise in variaty of tasks., e,g, sensor data processing, social network analysis, customer relationship etc. Text mining and dealing with unstructured and semi-structured data is one of the topical classification targets. The course focuses on compreheanson and practice, using freeware tool Weka (additionally - Python language for experienced users) to analyse real data sets and interpret the insights. Students work in teams and apply their knowledge and skills in data analysis to develop a capstone project.. |
|||||||||||||||||||||||||||||||||||||||
| Contents |
|
|||||||||||||||||||||||||||||||||||||||
|
Goals and objectives of the course in terms of competences and skills |
The goal of the course is to provide knowledge 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. Provide knowledge and skills to work with data mining techniques and tools. 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 Apply at least one data mining tool - 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 - 15%
Practical woks (labs) - 25% Group project - 30% Examination - 30% |
|||||||||||||||||||||||||||||||||||||||
| Course prerequisites | Basic knowledge about data storage and processing. | |||||||||||||||||||||||||||||||||||||||
| Course planning |
|
|||||||||||||||||||||||||||||||||||||||