Periods
(4-Sep-2017
– 22-Oct-2017)
(23-Oct-2017
– 15-Dec-2017)
(8-Jan-2018
– 4-Mar-2018)
(5-Mar-2018
– 27-May-2018)
Teaching schedule for Master's Degree Programme in Computational Big Data Analytics.
See the Curricula for requirements of General, Advanced and Other and Optional Studies.
Correspondence between old and new courses, statistics curricula 2012-2015 compared to CBDA curricula 2015-2018, can be found in separate document (finnish only).
Course registration
Period I: Mon 1.8. - Thu 31.8.2017
Period II: Mon 18.9. - Sun 15.10.2017
Period III: Mon 27.11.- Mon 1.1.2018
Period IV: Mon 29.1.- Sun 25.2.2018
See the course information for instructions on course registration. If a student has registered for a course but will not be taking it, he/she must cancel his/her registration. If a student does not participate in the course and does not cancel his/her enrolment by the time set by the teacher, or if he/she discontinues the course, he/she will be assigned a fail grade for the course in question.
Course feedback
Course feedback can be given through the course-specific questionnaire opened by the teacher responsible.
Cross-Institutional Studies (Tampere3)
Service for Cross-Institutional Studies lists courses offered to all degree students at Tampere University of Applied Sciences, Tampere University of Technology and the University of Tampere. For instructions and details, see the service.
Recommended and/or interesting studies for CBDA-students include
- MAT-02506 Probability Calculus (4 ECTS),
- MAT-63807 Introduction to data mining: The B-course and GUHA-method (5 ECTS) and
- MAT-72006 Advanced Algorithms and Data Structures (7 ECTS).
For details, see information on Cross-Institutional Studies in TUT (below).
Final grade and graduation
Final grade in Advanced studies (for degree certificate) can be asked from study coordinators (mtt-studies@uta.fi, cs-studies@uta.fi).
See also: Applying for the degree certificate and graduation deadlines.
Recommended Cross-Institutional Studies in TUT include
- MAT-02506 Probability Calculus, 4 ECTS (basic level course, part of prerequisities in CBDA, can not be included inside the minimum requirement of 120 ECTS of Masters Degree).
- MAT-63807 Introduction to data mining: The B-course and GUHA-method, 5 ECTS (Can be included in Advanced Studies in CBDA: "Advanced Courses in Methods of Computational Data Analytics").
For other courses, contact your personal study plan teacher or study coordinator.
- Search the course from the service for cross-institutional studies.
- Update your study plan and inform your psp-teacher.
- Apply for studies of your choice.
- Follow the instructions.
Teachers:
- Zheying Zhang (SDE)
- Jaakko Peltonen (CBDA)
- Martti Juhola (CBDA)
Enroll on the course TIETA6 Tietorakenteet in NettiOpsu
Self studying, weekly excercises, practical work, and exam.
Course Contents
Matrix basic operations, random number generation, cross-validation, Jackknife, Bootstrap, use of the R program
Modes of Study and Registration
Independent work, only for CBDA-students.
Please see the Moodle page for details and instructions.
Contact teaching (lectures and excercises) is only in Finnish. For details, see the teaching schedule of the finnish version (MTTTA14 Tilastotieteen matriisilaskenta ja laskennalliset menetelmät). The lectures are based on study material which is available in English.
Students can get support for their studies in Statistics from the workshop.
The idea is that students work independently or in groups with the problems that have arisen. The workshop has a teacher and you can ask for the advice from him. However, the idea is that the teacher will not solve the problems for you. The workshop does not provide any compensation of the solved exercises.
Period I: especially for students taking MTTTA14 Matrices for Statistics and Computational Methods.
Period II: escpecially for students taking MTTTA2 Matemaattisen tilastotieteen perusteet.
Period III: escpecially for students taking MTTTA4 Statistical Inference 1.
If you are considering to begin the thesis work, it is a good time to enrol. It is recommended that you'd begin the seminar before you have agreed on the topic and/or supervisor of the thesis.
Students can enrol any time, not just at the beginning of the period. If enrolment is not open, contact the teacher by email.
Enrollment for the whole academic year 2017-2018:
Modes of study
- Lectures
- Exercises (independent work)
- Exam
- Check the courses in Study offerings.
- Update your personal study plan (fill in the TUT course) and send it to your study advisor for approval.
- Apply for study right in Service for Cross-Institutional Studies
- Follow the instructions.
Teachers:
- Zheying Zhang (SDE)
- Jaakko Peltonen (CBDA)
- Martti Juhola (CBDA)
Knowledge about statistical methods and data analysis is of great importance in almost any field of research. In this course, general concepts of statistics will be provided so that the students can be able to independently carry out a small scale empirical research with the statistical software R.
Contents
- Basic concepts (1 lecture)
- What is statistics?
- Statistical research and its phases of work
- Population and sample
- Types of variables and scales of measurement
- Descriptive statistics (2 lectures)
- What is data?
- summary statistics (mean, median, quartiles, standard deviation and variance)
- Plotting the data (scatter plot, histogram, QQ-plot and boxplot)
- Contingency tables
- Basics of statistical inference (4-5 lectures)
- Basic concepts of probability
- Probability distributions (Binomial and Normal)
- Basic concepts in sampling theory
- Confidence intervals (mean and difference of means in independent samples)
- Hypothesis testing (mean and equality of means in independent samples, type I and type II-errors and p-values)
- Introduction to some non-parametric tests
- Linear models (3 lectures)
- Introduction to linear regression
- Basics of logistic regression
- Basics of Analysis of Variance (ANOVA)
A maximum number of 50 students will be allowed in this course (70% doctoral students and 30% masters students).
Please note that this course cannot be included inside the minimum 120 ECTS of Master's Degree Programme in CBDA (basic level course).
MTTTP1 Tilastotieteen johdantokurssi lectured in period I, II or III-IV is recommended for Finnish students.
Enroll on the course TIETA6 Tietorakenteet in NettiOpsu
Self studying, weekly excercises, practical work, and exam.
Students can get support for their studies in Statistics from the workshop.
The idea is that students work independently or in groups with the problems that have arisen. The workshop has a teacher and you can ask for the advice from him. However, the idea is that the teacher will not solve the problems for you. The workshop does not provide any compensation of the solved exercises.
Period I: especially for students taking MTTTA14 Matrices for Statistics and Computational Methods.
Period II: escpecially for students taking MTTTA2 Matemaattisen tilastotieteen perusteet.
Period III: escpecially for students taking MTTTA4 Statistical Inference 1.
Content
- Lecture I (75 minutes): Estimation based on (i) Complete Sample, (ii) Failure-Truncated Sample; Quiz I (30 minutes): Based on Lecture I.
- Lecture II (60 minutes): Estimation based on Time-truncated Sample; Quiz II (30 minutes): Based on Lecture II.
- Lecture III (60 minutes): Prediction of Future Observation(s) Based on Exponential Samples; Quiz III (30minutes): Based on Lecture III.
Modes of study
Participation in course work and a practical work.
Please also note visiting lecture:
Professor (retired), Bikas Sinha, Indian Statistical Institute
Topic: “F AM NOT LICKED” – The Twelve Penny Problem with Applications
- 2.11.2017 at 14.15–15.00, Pinni B3109.
If you are considering to begin the thesis work, it is a good time to enrol. It is recommended that you'd begin the seminar before you have agreed on the topic and/or supervisor of the thesis.
Students can enrol any time, not just at the beginning of the period. If enrolment is not open, contact the teacher by email.
Enrollment for the whole academic year 2017-2018:
Modes of study
- Lectures
- Exercises (independent work)
- Exam
- Check the courses in Study offerings.
- Update your personal study plan (fill in the TUT course) and send it to your study advisor for approval.
- Apply for study right in Service for Cross-Institutional Studies
- Follow the instructions.
Are most published research findings false (Ionnadis, 2005, Open Science Collaboration, 2015)? The course deals with the interpretations of and possible solutions to the lack of replicating results in empirical research (e.g. social psychology and cancer research). As a result of the lack of successful registered replications, a growing number journals such as Psychological Science are favouring practices such as direct replications and registered reports. The course gives a hands-on introduction to two methods which are thought to improve the reliability and replicability of empirical research: p-curve analysis and pre-registration.
The course:
- Introduces participants to the ongoing conversation about the replicability of empirical research
- Introduces participants to different interpretative frameworks of p-values and statistical significance (Null Hypothesis Significance Testing, Fisher, Neyman-Pearson, Bayes)
- Provides participants with skills for pre-registration and p-curve analysis
Masters Degree students of CBDA-programme: course can be included in the advanced studies of CBDA (Statistical Data Analytics). For details, contact your Personal Study Plan teacher.
Teachers:
- Zheying Zhang (SDE)
- Jaakko Peltonen (CBDA)
- Martti Juhola (CBDA)
Students can get support for their studies in Statistics from the workshop.
The idea is that students work independently or in groups with the problems that have arisen. The workshop has a teacher and you can ask for the advice from him. However, the idea is that the teacher will not solve the problems for you. The workshop does not provide any compensation of the solved exercises.
Period I: especially for students taking MTTTA14 Matrices for Statistics and Computational Methods.
Period II: escpecially for students taking MTTTA2 Matemaattisen tilastotieteen perusteet.
Period III: escpecially for students taking MTTTA4 Statistical Inference 1.
If you are considering to begin the thesis work, it is a good time to enrol. It is recommended that you'd begin the seminar before you have agreed on the topic and/or supervisor of the thesis.
Students can enrol any time, not just at the beginning of the period. If enrolment is not open, contact the teacher by email.
Enrollment for the whole academic year 2017-2018:
Modes of study
- Lectures
- Exercises (independent work)
- Exam
Teachers:
- Zheying Zhang (SDE)
- Jaakko Peltonen (CBDA)
- Martti Juhola (CBDA)
Students can get support for their studies in Statistics from the workshop.
The idea is that students work independently or in groups with the problems that have arisen. The workshop has a teacher and you can ask for the advice from him. However, the idea is that the teacher will not solve the problems for you. The workshop does not provide any compensation of the solved exercises.
Period I: especially for students taking MTTTA14 Matrices for Statistics and Computational Methods.
Period II: escpecially for students taking MTTTA2 Matemaattisen tilastotieteen perusteet.
Period III: escpecially for students taking MTTTA4 Statistical Inference 1.
This course explores methods for the analysis of longitudinal data and latent variable methods for linear models, generalized linear models, and nonlinear models. Focusing on applications, this course explores: the analysis of repeated measures ANOVA, multivariate approaches, random-effects regression, covariance-pattern models, generalized-estimating equations and generalizations, latent variable methods including finite mixture modelling, and likelihood methods. Students will develop expertise using the SAS and R computer packages, although no previous programming experience will be assumed. Grading is based on homework and computer assignments and a project, as well as several exams.
TUT-students, to enrol please send e-mail to: mtt-studies@uta.fi
TTY:n opiskelijat hakeutuvat kurssille JOO-opiskelijoina. Ilmoittaudu ensin sähköpostitse osoitteeseen: mtt-studies@uta.fi
Students should bring their own laptops in practicals.
If you are considering to begin the thesis work, it is a good time to enrol. It is recommended that you'd begin the seminar before you have agreed on the topic and/or supervisor of the thesis.
Students can enrol any time, not just at the beginning of the period. If enrolment is not open, contact the teacher by email.
Enrollment for the whole academic year 2017-2018:
Modes of study
- Lectures
- Exercises (independent work)
- Exam
Content
It is quite common that we do not get all the information we want for our statistical analysis. For example, in medical research a person can refuse to provide certain information they feel sensitive, such as weight, substance abuse, sexual orientation etc. Particularly, missing data in longitudinal studies is more the rule than an exception. Missing data in statistical analysis causes all sorts of problems. For example, the desired statistical method cannot be directly applied; loss of information or the results obtained can be biased if the analysis is not done properly accomplished. The course introduces various missing data mechanisms and their effects on statistical analysis. In addition, it presents and evaluates some of the commonly used methods for statistical analysis with missing data. Also advanced methods are presented, as well as special methods for the analysis of longitudinal data including likelihood-based methods and multiple imputation.
Modes of Study
Course work, exam.