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Statistics: Master Course - Statistical Methods for Data Science (course package)

Course · 60 credits

Description

Are you interested in statistical methods in data science? Then this is the course package for you. You will learn advanced analytical methods in machine learning and analysis of high-dimensional data. The package concludes with a thesis course where you write a Master’s thesis focusing on a practical or methodological problem involving modern data science techniques.

Course Package Contents

The following courses are included in the package:

STAN48 Statistics: Programming for Data Science, 7.5 cr.

In this course, you will learn modern statistical computing as viewed in data science through implementations in popular computing platforms such as R and Python. You will learn the R and Python environment, and to use the R packages and Python modules for statistics and working with data frames, arrays, and matrices. You will also learn methods for generating random variables, Monte Carlo methods, and bootstrap and resampling methods. Furthermore, the course will cover Bayesian computing and Markov Chain Monte Carlo methods. Optimisation and other numerical methods are also included.

STAN51 Statistics: Machine Learning from a Regression Perspective, 7.5 cr.

In this course, you will the basics of machine learning and doing so by focusing on those methods that build in one way or another on standard regression analysis. Some of the topics covered are classification based on logistic regression, model selection using information criteria and cross-validation, shrinkage methods such as lasso, ridge regression and elastic nets, dimension reduction methods such as principal components regression and partial least squares, and neural networks. You will find that theoretical studies are interwoven with empirical applications.

STAN52 Statistics: Advanced Machine Learning, 7.5 cr.

The course is a continuation of STAN51 where you will deepen your knowledge in machine learning methods. Some of the topics covered include bootstrapping, ensemble methods such as boosting and random forests, unsupervised machine learning methods such as PCA and clustering algorithms as well as applications of machine learning methods to problems, such as causal inference and text analysis. Theoretical studies are interwoven with empirical applications.

STAN53 Statistics: High-dimensional Data Analysis, 7.5 cr.

The central theme of the course is multivariate and high-dimensional data. Statistical methods presented include both classic multivariate methods, e.g. principal component analysis, factor analysis, discriminant analysis, and cluster analysis, and modern high-dimensional methods using e.g. penalisation, functional analysis, and methods for sparse matrices.

STAN47 Statistics: Deep Learning and AI Methods, 7.5 cr.

This course presents an application-focused and hands-on approach to learning neural networks and reinforcement learning. It can be viewed as first introduction to deep learning methods, presenting a wide range of connectionist models, which represent the current state-of-the-art. It explores the most popular algorithms and architectures in a simple and intuitive style. You will learn the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning. The course covers feed-forward neural networks, convolutional neural networks, and the recurrent connections to a feed-forward neural network.

STAN49 Statistics: Analysis of Textual Data, 7.5 cr.

The course provides an introduction to statistical analysis of text. You will study both methods based on classic statistical approaches (including Bayesian models) and modern approaches such as deep learning and recurrent neural networks. Topics covered include text representation, text classification, text clustering, topic modelling, sentiment analysis and text summarisation.

STAN40 Statistics: Thesis, 15 cr.

Thesis course where you write a Master’s thesis.

After the Courses

After the courses, you will be well equipped for a career as statistician, data scientist or analyst.

If you complete all the courses, and otherwise fulfil the requirements for the degree, you can apply for a Degree of Master of Science in Statistics (60 credits).

Closed for applications

Application opportunities

Contact

Department of Statistics

Visiting address
Tycho Brahes väg 1, 223 63 Lund

Postal address
Box 7080, 220 07 Lund

+46 46 222 89 21

http://www.stat.lu.se/en

Viktoria Svenby

Study advisor

viktoria [dot] svenby [at] ics [dot] lu [dot] se

Pierre Carbonnier

Study advisor

+46 46 222 89 06

pierre [dot] carbonnier [at] stat [dot] lu [dot] se

Requirements and selection

Entry requirements

A Bachelor's degree in statistics, or a Bachelor's degree in mathematics or computer science including at least 60 credits in statistics, or the equivalent.

Selection criteria

Seats are allocated according to: ECTS (HPAV): 100 %.

English language requirements

Most of Lund University’s programmes require English Level 6 (unless otherwise stated under 'Entry requirements'). This is the equivalent of an overall IELTS score of 6.5 or a TOEFL score of 90. There are several ways to prove your English language proficiency – check which proof is accepted at the University Admissions in Sweden website. All students must prove they meet English language requirements by the deadline, in order to be considered for admission.

How to prove your English proficiency – universityadmissions.se

Country-specific requirements

Check if there are any country-specific eligibility rules for you to study Bachelor's or Master's studies in Sweden:

Country-specific requirements for Bachelor's studies – universityadmissions.se

Country-specific requirements for Master's studies – universityadmissions.se

Apply

Start Autumn Semester 2024

Day-time Lund, full time 100%

In English

Study period

2 September 2024 - 8 June 2025

Application

You can only apply for this course in the 'Swedish student' application round. Find out more: Applying for studies – when to apply

Day-time Lund, full time 100%

In English

Study period

2 September 2024 - 8 June 2025

Application

Closed for applications

How to apply

Lund University uses a national application system run by University Admissions in Sweden. It is only possible to apply during the application periods.

When to apply for studies

Step 1: Apply online

  • Check that you meet the entry requirements of the programme or course you are interested in (refer to the section above on this webpage).
  • Start your application – go to the University Admissions in Sweden website where you create an account and select programmes/courses during the application period.
    Visit the University Admissions in Sweden website
  • Rank your programme/course choices in order of preference and submit them before the application deadline.

Step 2: Submit documents

  • Read about how to document your eligibility and how to submit your documents at the University Admissions in Sweden website. Follow any country-specific document rules for Master's studies or Bachelor's studies
    Country-specific requirements for Bachelor's studies – universityadmissions.se
    Country-specific requirements for Master's studies – universityadmissions.se
     
  • Get all your documents ready:
    - official transcripts and high school diploma (Bachelor's applicants)
    - official transcripts and degree certificate or proof that you are in the final year of your Bachelor's (Master's applicants)
    - passport/ID (all applicants) and
    - proof of English proficiency (all applicants).
     
  • Prepare programme-specific documents if stated in the next paragraph on this webpage.
  • Upload or send all required documents to University Admissions before the document deadline.
  • Pay the application fee (if applicable – refer to the section below on this webpage) before the document deadline.

* Note that the process is different if you are applying as an exchange student or as a part of a cooperation programme (such as Erasmus+).
* If you have studied your entire Bachelor's programme in Sweden and all of your academic credits are in Ladok, you do not have to submit transcripts or your diploma when applying for a Master's programme. However, there may still be other documents you need to submit! See the link below. 

Svensk student? 
Läs instruktionerna om att söka till ett internationellt masterprogram på lu.se

Tuition fees

Non-EU/EEA citizens

Full programme/course tuition fee: SEK 135 000

First payment: SEK 67 500

Convert currency – xe.com

Citizens of a country outside of the European Union (EU), the European Economic Area (EEA) and Switzerland are required to pay tuition fees. You pay one instalment of the tuition fee in advance of each semester.

Tuition fees, payments and exemptions

EU/EEA citizens and Switzerland

There are no tuition fees for citizens of the European Union (EU), the European Economic Area (EEA) and Switzerland.

Application fee

If you are required to pay tuition fees, you are generally also required to pay an application fee of SEK 900 when you apply at the University Admissions in Sweden website. You pay one application fee regardless of how many programmes or courses you apply to.


*Note that there are no tuition or application fees for exchange students or doctoral/PhD students, regardless of their nationality.

Scholarships & funding

Lund University Global Scholarship programme

The Lund University Global Scholarship programme is a merit-based and selective scholarship targeted at top academic students from countries outside the EU/EEA.

Lund University Global Scholarship

Swedish Institute Scholarships

The Swedish Institute offers scholarships to international students applying for studies in Sweden at Master's level.

Scholarship information on the Swedish Institute website

Country-specific scholarships and funding options

Lund University has agreements with scholarship organisations and funding bodies in different countries, which may allow applicants to apply for funding or scholarships in their home countries for their studies at Lund University.

External scholarships

Information about scholarships from external organisations