STAT221-18S1 (C) Semester One 2018

Introduction to Statistical Computing Using R

15 points

Details:
Start Date: Monday, 19 February 2018
End Date: Sunday, 24 June 2018
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 4 March 2018
  • Without academic penalty (including no fee refund): Sunday, 20 May 2018

Description

Statistical computing skills are essential within the modern workplace of statisticians and other quantitative/analytical positions. This course will develop and build your skills in computer programming for statistics, using the free statistical computing package R which is one of the most widely used tools for data analysis. The course provides excellent preparation for the many UC statistics courses that use R and, more generally, courses that require quantitative computing skills. The newly developed computing skills will also be used to unleash the power of modern computational statistical techniques for analysing complex real world data.

Statistical computing skills are a "must-have" for becoming a statistician and are extremely valuable for any quantitative role where you need to undertake data analysis. This course assumes no prior knowledge of computer programming. The intention is to develop the key skills you need and best prepare you for all our statistics courses and wider education in quantitative computing. Following successful completion you will have the skills to develop your own computer program, from scratch, for analysing your data.

In addition to developing your skills and experience in computing, you will also learn about a range of modern statistical techniques which employ the power of computers to analyse complex real world data.

An integral feature of the course is that the lectures and labs are all taught in computer labs so you can instantly get hands-on experience in implementing these statistical techniques efficiently in R, to take advantage of the ample computing power available to your generation.

As well as the fundamental of programming (data structure, logic and control flow, functions, etc.), the course will cover simulation of random numbers which can be used to mimic and thus study real world phenomena. Such simulation tools provide exploratory and inferential techniques to manipulate, visualise and make decisions from complex real world data. In particular, the course will cover:
1) random number generators;
2) simulation studies;
3) permutation and resampling methods (in particular bootstrapping)
4) kernel density estimation
Demonstrations and descriptions of these powerful tools are available on Wikipedia if you want to find out more.

Learning Outcomes

University Graduate Attributes

This course will provide students with an opportunity to develop the Graduate Attributes specified below:

Critically competent in a core academic discipline of their award

Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

Prerequisites

STAT101 and (MATH102 or EMTH118); or any one of MATH103, MATH199, EMTH119.

Restrictions

STAT218

Course Coordinator / Lecturer

Daniel Gerhard

Lecturer

Varvara Vetrova

Assessment

Assessment Due Date Percentage 
Assignments (x4) 40%
Final Examination 60%


Note: To pass this course, you must both pass the course as a whole (≥50% over all the assessment items) and obtain at least 40% in the final examination.

Textbooks / Resources

Recommended Reading

Rizzo, Maria L; Statistical computing with R ; Chapman & Hall/CRC, 2008 (or 2007).

Indicative Fees

Domestic fee $749.00

International fee $3,788.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mathematics and Statistics .

All STAT221 Occurrences

  • STAT221-18S1 (C) Semester One 2018