STAT221-19S2 (C) Semester Two 2019

Introduction to Statistical Computing Using R

15 points
Details:
Start Date: Monday, 15 July 2019
End Date: Sunday, 10 November 2019
Withdraw Dates
Last Day to withdraw from this course:
  • Without financial penalty: Friday, 26 July 2019
  • Without academic penalty (including no fee refund): Friday, 27 September 2019

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.

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

  • 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.
    • University Graduate Attributes

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

      Employable, innovative and enterprising

      Students will develop key skills and attributes sought by employers that can be used in a range of applications.

Pre-requisites

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

Restrictions

STAT218

Timetable 2019

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Thursday 11:00 - 12:00 Ernest Rutherford 464 Computer Lab 15 Jul - 25 Aug
9 Sep - 20 Oct
Lecture B
Activity Day Time Location Weeks
01 Monday 16:00 - 17:00 Ernest Rutherford 212 Computer Lab 15 Jul - 25 Aug
9 Sep - 20 Oct
Lecture C
Activity Day Time Location Weeks
01 Wednesday 10:00 - 11:00 Ernest Rutherford 464 Computer Lab 15 Jul - 25 Aug
9 Sep - 20 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Wednesday 14:00 - 15:00 Ernest Rutherford 464 Computer Lab 22 Jul - 25 Aug
9 Sep - 20 Oct
02 Thursday 09:00 - 10:00 Ernest Rutherford 464 Computer Lab 22 Jul - 25 Aug
9 Sep - 20 Oct

Course Coordinator / Lecturer

Daniel Gerhard

Lecturer

Giulio Dalla Riva

Assessment

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.

Indicative Fees

Domestic fee $764.00

International fee $4,000.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Mathematics and Statistics.

All STAT221 Occurrences

  • STAT221-19S2 (C) Semester Two 2019