(in Polish) Wprowadzenie do Big Data science - wykład

elementary

(in Polish) Zgodnie z programem studiów uchwalonym przez Senat UKSW:

https://monitor.uksw.edu.pl/docs/search

BDAS2_W03, BDAS2_W04, BDAS2_W05

The aim of the class is to present the theoretical and practical foundations of data engineering, especially in the field of Big Data. The student gains methodological awareness and orientation in the world of rapidly growing social Big Data - its volume, variety and speed of processing (Volume-Variety-Velocity, the so-called 3V), and acquires knowledge of its acquisition, storage, refining and processing. In addition to introducing the concept of Big Data, students learn what the analysis of this data entails, as well as the technical, conceptual and ethical challenges involved.

1. Big Data: definition, history and its place among other data analysis methods.

2. The digital age, or more and still more data. Data acquisition, types of data, and algorithmic ways to transform them into big data sets.

3. From order to disorder, or the invasion of unstructured data. Big data as a search for patterns in data.

4. Why is Big Data technology useful? Four epistemological "promises" of Big Data analysis. Do we still need the theory?

5. Danetization of the world: when words become data. Text mining algorithms for analyzing words.

6. Danetization of the world: when location becomes information.

7. Danetization: when interactions become data. Social Network Analysis for describing interactions in social groups.

8. Danetization: about the role of the "digital footprint". How data is analyzed on the Internet and for what purposes.

9. Value chain in the era of Big Data.

10. Intermediaries of new data. Data governance. European Union regulations.

11. Big Data: the death of the expert or the development of expert systems? How Big Data supports the process of expert staking and diagnostics.

12. Data analysis methods used in Big Data in a nutshell.

13. Threats from Big Data: facts and myths.

14 Control. How Big Data enhances the diagnostic and predictive accuracy of social phenomena.

15. Colloquium

The student acquires knowledge of:

1. theoretical and practical foundations of data engineering

2. acquisition, storage and processing of data

3. data analysis using algorithms

The basis for completing the course is a test of the knowledge acquired during the lecture. To pass the test, the student must give 60% correct answers.