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Lecture 1

Introduction to the Master’s Programme

”Statistics and Data Mining”

Practical questions

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Personnel at Statistics and Machine Learning department

Introductory course "Statistics and Data Mining"

Name Name

Oleg Sysoev

Senior lecturer

Responsible for “Statistics and Data Mining”

Mattias Villani

Professor

Division chief

Anders Nordgaard

Senior lecturer

Ann-Charlotte Hallberg(Lotta)

Director of studies

Lecturer

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Personnel at Statistics and Machine Learning department

Introductory course "Statistics and Data Mining"

Name Name

Bertil Wegmann

Postdoc

Per Sidén

PhD student

Annelie Almquist

Administrator(registration, course reporting,…)

Lilian Alarik

Study councellor

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Personnel at Statistics and Machine Learning department

Introductory course "Statistics and Data Mining"

Name Name

Linda Wänström

Senior lecturer

Måns Magnusson

PhD Student

Karl Wahlin

Senior lecturer

Responsible for the bachelor program

Josef Wilzén

PhD Student

Personnel at ADIT

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Introductory course "Statistics and Data Mining"

Name Name

Patrick Lambrix

Professor

Jose Pena

Senior lecturer

This course

Lectures – Attendance is obligatory Reading one statistical paper and writing a summary Reading one more statistical paper and writing a critical

review URKUND is used Plagiarism is forbidden! (discovered

plagiarism implies a request to disciplinary board) Course end: January 2016 Grading for this course: Pass fail Several teachers from IDA are involved You meet other IDA master students

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Introductory course "Statistics and Data Mining"

Statistics and Data Mining program

Aims: To build advanced models for explaining complex real-

life systems and predicting new events To extract, organize and explore large volumes of data To learn how to discover important (hidden) information

(trends, patterns) from large and complex data sets To get an in-deep knowledge of models and methods

Competences: Data mining, machine learning, statistical modeling,

visualization methods, databases, programming etc

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Introductory course "Statistics and Data Mining"

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Introductory course "Statistics and Data Mining"

Job opportunities

A plenty of jobs are waiting in USA and Europe Master program gives excellent background to search jobs as

analyst, engineer, manager or consultant in Business Intelligence (BI) Customer Resource Management (CRM) Bioinformatics Economics IT industry

…and many other areas where large or complex datasets are involved

Example jobs: Predictive Modeling and Data Mining Scientists/Analysts, USA Statistical Modeller/Software Developer, London Analytiker, Försäkringskassan

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Introductory course "Statistics and Data Mining"

Master program overview Master program= 120 ECTS credits

Obligatory courses (42 ECTS) You must take and finish these courses to get a degree

Introductory courses (at least 6 ECTS) Advanced R programming: recommended for all students missing a solid

programming background Statistical methods: recommended for people with a little statistics in the

background, i.e. computer scientists or engineers (check syllabus if you are not sure)!

Profile courses (at least 12 ECTS) Those are courses in statistics that you need to take in order to get a degree

in Statistics. Complementary courses

If you have found some interesting course which is not in the schedule, we may count it as profile, contact Oleg S.

Master thesis (30 ECTS)

In order to make a sufficient progress in studies, you need to gain 30 ECTS credits/ semester

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Introductory course "Statistics and Data Mining"

Semester admission rules

at least 6 ECTS credits of the first semester to be admitted to the second semester

at least 40 ECTS credits of the first year, in order to be admitted to the third semester

65 ECTS credits of the programme, including all obligatory courses, in order to be admitted to the master thesis course.

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Introductory course "Statistics and Data Mining"

Master program overview

12Introductory course "Statistics and Data Mining"

Year 1Semester 1 Semester 2

Period 1 Period 2 Period 3 Period 4Advanced Academic Studies

(732A42, 3 credits)Data mining - clustering and

association analysis(732A31, 15 credits)

Philosophy of science (720A04

,3 credits)

Time series analysis(732A34, 6 credits)

Introduction to Machine Learning(732A52, 9 credits)

 

Computational statistics(732A38, 6 credits)

Bayesian learning,(732A46,  6 credits)

Advanced R programming(732A50, 6 credits)

 

Neural Networks and Learning Systems

(TBMI26, 6 credits)

Multivariate statistical methods

(732A37, 6 credits)Statistical methods ( 732A49, 6 credits)

Web programming and interactivity

(TDDD24, 4 credits) 

Master program overview

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Introductory course "Statistics and Data Mining"

Year 2

Semester 3 Semester 4

Period 1 Period 2 Period 3 Period 4Visualization

(732A39, 6 credits) 

Advanced Machine learning(732A37, 6 credits)

 

  

  

 MASTER THESIS

(732A30, 30 credits)Optimization(TAOP23, 6 credits)

Text Mining(732A47, 6 credits)

Probability theory(732A40, 6 credits)

Database Technology(TDDD37 , 6 credits)

Data mining project (732A32, 6 credits)

Statistical evidence evaluation (732A45, 6 credits)

EXCHANGE STUDIES

Obligatory courses

Academic studies (several sessions, ends before january) Introduction to Machine Learning

Predictive modelling: Ridge regression, Generalized additive models, neural networks, support vector machines etc

Data Mining – Clustering and Association analysis Unsupervised learning, focus on algorithms

Philosophy of science Laws of nature and scientific models, theories and observations

Computational statistics Random number generation, MCMC

Bayesian learning Using prior knowledge to make better decisions and inferences

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Introductory course "Statistics and Data Mining"

Introductory courses

Statistical Methods Probability, conventional distributions: Normal, Poisson,

Gamma… Point and interval estimation Hypothesis testing Basics of Bayesian statistics

Advanced programming in R Basic programming (loops, data types) Advanced topics (debugging, peformance enhancement

etc).

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Introductory course "Statistics and Data Mining"

Profile courses

Visualization Static, interactive and dynamic graphics for data analysis

Time Series Analysis Autocorrelation, forecasting, ARIMA models

Probability theory Multivariate random variables, transforms, order statistics,

convergence. Necessary for PhD studies. Multivariate statistical methods

Principal components, factor analysis, canonical correlation Statistical evidence evaluation

Methods to secure, analyze and interpret (technical) evidence to be used in the legal process of particular cases

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Introductory course "Statistics and Data Mining"

Profile courses

Neural networks and learning systems Given by Department of Biomedical Engineering Advanced

neural networks, kernel methods, reinforcement learning, genetic algorithms

Web programming course HTML, XML, PHP

Optimization Linear, nonlinear, network optimization

Data mining project Specify, implement and evaluate a data mining algorithm

Text Mining Extracting text data from different sources and analysis

linguistically and by statistical tools Database technology

Relational databases, relational algebra, SQL, query optimization

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Introductory course "Statistics and Data Mining"

Other information

Master program’s homepage(schedule, courses, news…):http://www.liu.se/en/education/master/programmes/F7MSM/student?l=en

Facebook page:https://www.facebook.com/liustatisticsmaster

Email to staff: name.lastname@liu.se Example: oleg.sysoev@liu.se

Webpages of courses: www.ida.liu.se/~course_code/ This course: www.ida.liu.se/~732A42/

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Introductory course "Statistics and Data Mining"

Course registration To get credits for a course, you must register on it. International students: register for max 120 ECTS, you pay for more

than that! (Swedish language courses not included) Registration is done by Student Portal:

https://www3.student.liu.se/portal/eng If you have problems with registration, contact our administrator

Annelie Almquist (annelie.almquist@liu.se )

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Introductory course "Statistics and Data Mining"

Here you can choose between registration for single subject course or study program

LiU-Account and personal number

It is necessary for you to get a LIU-account as soon as possible (house Zenit, student office) Access to Student Portal Course registration Access to course materials Access to department computers

If you come outside Sweden, it is very important to get a Personal Number at the Tax office: Address: Kungsgatan 37, Linköping Needed for medical help

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Introductory course "Statistics and Data Mining"

Lectures, Labs, Seminars

Lectures: normally presented in PowerPoint, later available either at course page or LISAM. Attendance is typically not obligatory

Labs: typically computer exercises done individually or in groups of two. Attendance is typically not obligatory. A written report should be normally submitted.

Seminars: Discussions of theory and labs, student presentations. Attendance typically obligatory.

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Introductory course "Statistics and Data Mining"

Exams and points

Exams Each course has 1 exam and 2 re-exams You must register for the written or computer exam at

least 10 days in advance. Exam results may not be improved if aim for high grade

and feel that written badly cross every non-empty page in solutions

Exam results should normally be available after 2 weeks Credits

Some courses have separate credits for labs (or project) and for the exam

Credits for some courses can be obtained only after you are completely done with the course

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Introductory course "Statistics and Data Mining"

Course evaluation

KURT: course evaluation system at LiU You evaluate the courses you have done Sent via email The surveys are anonymous! Very important for improvements of courses – please

answer these surveys! You will be invited to a meeting with study councellor

periodically to discuss your current studies and plan the coming studies.

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Introductory course "Statistics and Data Mining"

Schedules of the courses

Some schedules are on the course homepages

Some schedules accessed via TimeEdit: https://se.timeedit.net/web/liu/db1/schema

Type the course name and run

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Introductory course "Statistics and Data Mining"

How to find a room

Room at LiU: http://www.liu.se/karta/?l=en

Room at the department (IDA) Go to www.ida.liu.se and choose “Find IDA room” from the

droplist.

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Introductory course "Statistics and Data Mining"

Useful links

Homepage "Statistics and Data Mining" Information from the faculty Practical Guide Welcome activities for masters

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Introductory course "Statistics and Data Mining"

Questions

Questions related to the program? Contact Oleg Sysoev

http://www.ida.liu.se/department/contact/contactsearch.en.shtml?NAME=Oleg%20Sysoev

Questions about master studies in general? Contact Darja Utgof

http://www.student.liu.se/masters/master-s-coordinators?l=en

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Introductory course "Statistics and Data Mining"