TTK University of Applied Sciences
TTK University of Applied Sciences
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Curriculum subjectData Processing and Statistics
| Subject |
| Subject code |
RKE122 |
| Subject name |
Data Processing and Statistics |
| Credit points |
6 ECTS |
| Grading method |
Grade prelim |
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| Curriculum subject |
| Curriculum |
2019 OK |
| Study year |
1 |
| Semester |
Fall semester |
| Subject type |
Mandatory |
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| General description |
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| The main concepts of information tecnology. The main principles of designing and creating presentations. Text processing and reference system according to the TCE instruction for written papers. Basic techniques of handling data and analysing the results by using methods of mathematical statistics and MS Excel software statistical tools. |
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| General aim |
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| The objective of the course is to create conditions to successful use of the MS Office in studies and worklife. The course introduces the basic ideas of the collection, analysis, interpretation, presentation, and organization of data. Every person in today's society needs to have a basic understanding of data analysis and statistical concepts, in order to be able to think critically about the quantitative information we encounter every day, from opinion polls to headline news reports based on scientific studies. |
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| Aim |
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By the end of this course, the student should
-know and understand the principles and main terms of information technology;
-be able to work with a presentation software and be able to create and design presentations;
-be able to work with a word processor and to compile text documents needed for worklife;
-be able to work with a spreadsheet application, to analyze data and to present the results graphically;
-be able to use computers for obtaining, processing and storing information;
-be able to explain and apply principles of study design and data collection and be able to Identify the relevant population, sample, study units and variables;
-be able to produce and interpret graphical summaries and common numerical summary statistics of data;
-be familiar with common discrete and continuous probability distributions;
-be able to construct confidence intervals for numerical statistics of a population and to perform hypothesis tests;
-be able to describe relations between two variables using pivot tables, correlation coefficients and simple linear regression model. |
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| Evaluation methods |
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| The course ends with a final grade, summarizing the previous graded tasks. |
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| Current rounds |
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| xxx KLO11/21 |
| xxx LO11/21 |
| xxx OK11/21 |
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