DAT110 Introduction to Data Analysis and -Visualisation
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Showing course contents for the educational year 2020 - 2021 .
Course responsible: Kristin Tøndel
ECTS credits: 10
Faculty: Faculty of Science and Technology
Teaching language: NO
(NO=norsk, EN=Engelsk)
(NO=norsk, EN=Engelsk)
Teaching exam periods:
This course starts in Spring parallel. This course has teaching/evaluation in Spring parallel.
Course frequency: Annually
First time: Study year 2019-2020
Preferential right:
Students taking a master in Data Science.
Course contents:
- Datatypes and data-uploading from various file formats.
- Visualization and explorative analysis for identification of structure and trends (histograms, scatterplots, box-plots etc.).
- Some simple statistics (mean, median, variance etc).
- Correlation and covariance (of variables and matrix data).
- Crosstables.
- Elementary normal distribution theory, normalizing transformations and testing of normality.
- Geometric distributions, binomial distributions, Poisson-distributions.
- Inference (parametrical and non-parametrical for investigation of one and two samples) and simple analysis of variance.
- Least squares modelling (linear and polynomial fit).
- Logistic regression (classification with two groups).
- Smoothing of time-dependent data.
- Cluster analysis (k-means).
Learning outcome:
Skills and insight into basic data analysis techniques based on modern data collection. The student will learn to choose appropriate methods for
1) Explorative data analysis (plotting/visualisation and descriptive statistics),
2) Visualisation,
3) Inference,
4) Modelling and prediction with continous and categorical responses (regression and classification) and validation of predictive models.
5) Introduction to cluster analysis and smoothing of time series data.
Learning activities:
Lectures, and group work with manual- and computer lab exercises (assistant teachers and course responsible will be present at the groups). 4 hours mandatory attendance per week. Student-active learning is used.
Teaching support:
Guidance during tutoring sessions.
Syllabus:
Information about relevant literature will be given before the start of the course at the course home page.
Prerequisites:
Calculus 1 & 2 + elementary linear algebra.
Recommended prerequisites:
INF100 Principles of Information Processing or similar.
Mandatory activity:
Mandatory exercises (hand-ins) and 2 double lectures mandatory attendance per week. Requirements for approval of mandatory activity will be announced at the beginning of the term.
Assessment:
Written examination: 3.5 hours.
Nominal workload:
Lectures 78 hours, exercises 26 hours, preparation for presentations 96 hours, colloquia and self study 100 hours.
Entrance requirements:
Special requirements in Science
Reduction of credits:
5 credits with STAT100
10 credits with MATH-INF110
Type of course:
Lectures: 2 hours per week. Lectures/exercises: 2 hours per week. Exercises: 2 hours per week.
Examiner:
The external and internal examiner jointly prepare the exam questions and the correction manual. The external examiner reviews the internal examiner's examination results by correcting a random sample of candidate's exams as a calibration according to the Department's guidelines for examination markings.
Examination details: Written exam: A - E / F