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| SYLLABUS |
| Text Mining,
6 ECTS Credits |
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COURSE CATEGORY Master´s Programme in Statistics and Data Mining
MAIN FIELD OF STUDY Statistic - STA
SUBJECT AREA Statistics – ST1
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COURSE CODE |
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732A47 |
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| AIM OF THE COURSE |
After completion of the course the student should on an advanced level be able to:
- account for and use the principles for storing and accessing textual information
- account for techniques for information extraction and information retrieval
-apply text processing techniques to prepare documents for statistical modelling
- apply relevant statistical models for analyzing textual data and correctly interpret the results
- use statistical models for prediction of textual information
- evaluate the performance of statistical models for textual data
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| CONTENTS |
The course aims to show how to textual data can be retrieved, linguistically pre-processed and subsequently analyzed quantitatively using formal statistical methods and models. The course brings together expertise from the areas of database methodology, computational linguistics and statistics.
The course proceeds in four stages:
1. Introductory modules
- Introduction to Python programming
- Introduction to statistical modeling
- Introduction to computational linguistics
2. Data models and information retrieval for textual data
3. Statistical models for textual data
4. Text mining project
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| TEACHING |
| The course consists of lectures, lab exercises and a text mining project. The lectures are devoted to presentations of concepts, and methods. The computer lab exercises are devoted to practical application of text mining tools. In the project work, the student will get hands-on experience in solving a text mining problem. Homework and independent study are a necessary complement to the course. Language of instruction: English. |
| EXAMINATION |
Written and oral report on the Text mining project. Written reports on lab assignments. Detailed information about the examination can be found in the course’s study guide.
Students failing an exam covering either the entire course or part of the course two times are entitled to have a new examiner appointed for the reexamination.
Students who have passed an examination may not retake it in order to improve their grades. |
| ADMISSION REQUIREMENTS |
For acceptance to the course, the student must have a bachelor’s degree with a total of at least 90 ECTS credits (1.5 years of full-time studies) in mathematics, applied mathematics, statistics, and computer science. The undergraduate courses in mathematics should include both calculus and linear algebra. Basic undergraduate courses in statistics and computer science are also required.
Documented knowledge of English equivalent to Engelska B/Engelska 6: internationally recognized test, e.g. TOEFL (minimum scores: Paper based 575 + TWE-score 4.5, and internet based 90+TWE-score 20), IELTS, academic (minimum score Overall band 6.5 and no band under 5.5), or equivalent. |
| GRADING |
| The course is graded according to the ECTS grading scale A-F |
| CERTIFICATE |
| Course certificate is issued by the Faculty Board on request. The Department provides a special form which should be submitted to the Student Affairs Division. |
| COURSE LITERATURE |
| The course literature is decided upon by the department
in question. |
| OTHER INFORMATION |
Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus.
The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed. |
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Text Mining
Text Mining |
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Department responsible
for
the course or
equivalent:
IDA - Department of Computer and Information Science |
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| Registrar No: 2012-01070 |
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Course Code: 732A47 |
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Exam codes: see Local Computer System |
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| Subject/Subject Area : Statistic - STA |
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| Level |
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Education level |
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Subject Area Code |
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Field of Education |
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| A1X |
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Advanced level |
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TE |
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The syllabus was approved by the Board of Faculty of Arts and Science 2013-10-18
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