Big Data Analytics for Business

economics and finance

intermediate

EK1_WO4

EK1_UO3

EK1_UO4

EK1_UO9

EK1_kO3

Basic knowledge of descriptive statistics

The exponential growth in the amount of human-generated data and increasingly powerful machines continues to accelerate. Data scientists have recently proposed different methods to analyze this category of data, known as Big Data.

It is important that economic decision-makers are at least able to fully understand and interpret the results provided by data scientists at economic level. This is precisely the ultimate goal of this course in terms of social competence for the future economists and / or business leaders: to familiarize them with big data visualization and analysis as a source of valuable information addressing to fundamental questions of professionals. The course begins with a basic introduction to big data. It then discusses what the analysis of this data entails, as well as the associated technical, conceptual and ethical challenges. It also provides a first practical experience in the management and analysis of large and complex data structures .

TEACHING EFFECT’S

KNOWLEDGE

The student knows different types of statistical observations. He is able to define the concept of Big Data in different as-pects related to business analytics and data mining shown in the lecture. The student knows how to visualize the data and interpret them.

SKILLS

Student is able to choose quantitative methods depending on the type of the problem at hand.

The student is able to properly apply the Big Data basic quantitative methods and interpret main issues of the found re-sults.

COMPETENCES

Student is able to choose quantitative methods depending on the type of the problem at hand.

The student is able to properly apply the Big Data basic quantitative methods and interpret the found results.

The student gives his opinion on the strengths and limitations of the content of the empirical conclusions.

Student activity Student workload in hours

participation in the lecture 30

preparation for the inter group discussions 25

consultation 5

time to write the work 10

time to the self-assessment of the inter-group work 5

preparation for the exam 25

TOTAL HOURS 100

NUMBER OF ECTS 100 hours / 30 (25) hours ≈ 4

Form of the course: Lecture

Assessment: 2( 1 work in groups and a final written exam)

The final grade includes: a grade from the written test (50%) and a grade from the work self-assessment in-ter groups (50%).

10 pts - grade: 5,0;

8-9 pts - grade: 4,5;

7-8 pts - grade: 4,0;

6-7 pts - grade: 3,5;

5 - 6 pkt - ocena 3,0;

below 5 pts - grade: 2,0

2 –bad work- a student has not provided the work, or the work is not her independent achievement, is chaotic with regard to Big Data different concepts and technical analysis methods. a student does understand basic concepts related to Big Data analytics. He avoids any discussion related to Big Data issues.

3 – enough good- a student proves to understand basic concepts of Big Data in different aspects related to business analytics and data mining shown in the lecture. He can visualize the statistical data using the taught software during the lectures. He still shows difficulties to master the empiric side of Bid Data with respect to data mining techniques. a student basic insights related to Big Data concepts and visualization. He does not master computational tech-niques but recognizes its usefulness. He would be ready to increase knowledge and competences for professional purposes.

4 – good_ a student has provided a good work and stated problems and positions correctly. He is able to choose and apply the adequate quantitative methods depending on the type of the problem at hand. a student initiates discussions related to big data issues and can understand various reports presented by data engineers in the fields of business or economics analytics.

5 - very good- a student has provided a good work and stated problems and positions correctly. He is able to choose and apply the adequate quantitative methods depending on the type of the problem at hand. He can interpret adequately the solution and he shows to allude to the literature proposed in the syllabus. The student initiates discussions related to Big Data issues, knows to select and apply efficiently computational techniques to solve problems in business analytics, He understands the implications of Big Data in business, places them in the broader background of everyday.