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Short Course
on
Practical Time Series Analysis and Forecasting for Business
The financial industry currently has a shortage of people with the necessary data science skills to address problems relating to large and/or complex data sets. Nowadays financial institutions are confronted with an abundance of data, and so in order to maintain a competitive advantage, it is imperative to have analysts with these necessary data science skills. This short course will address one of the topics needed to become a skillful data scientist.

Purpose of the course:

This short course aims to equip participants with the skills necessary to comprehend time series forecasting principles applicable to business problems where time series data is prevalent.

Admission requirements:

Admission requirements: 
NQF level 7 majoring in either Mathematics/Statistics/Engineering/Computer Science.
Learning assumed to be in place: 
NQF level 7.

Course outcomes and assessment criteria :

Course outcomes and the associated assessment criteria: 

Study Unit

Outcomes

Assessment Criteria

  After completion of this course, participants will: Participant will be assessed on the following criteria:
 
  1. Demonstrate applied knowledge and understanding of basic time series analysis concepts and understand how they are applied in practice.
  2. Demonstrate the ability to identify real-world practical time series problems that can be solved via the application of various time series models.
  • Critically discuss and implement an exploratory analysis on time series data and be able to identify the movement components contained therein;
  • Identify, fit and forecast different autoregressive and moving average time series models to stationary data sets and report the appropriateness of the model in a consistent manner;
  • Identify candidate exponential smoothing models applicable to different data sets and compare the appropriateness of these model to other candidate models;
  • Identify non-stationary time series data and apply the appropriate techniques to transform the data to stationarity;
  • Evaluate the performance of various different candidate models based on various goodness-of-fit- and accuracy statistics, i.e. statistics calculated from the hold-out sample.

 

Assessment: 
Assignments and participation.
Method of assessment: 
Learning objectives will be accomplished through the successful completion of assignments and participation in short course activities, proving insight into the topics at hand.

Additional information

Mode of delivery: 
Mixed
Target group: 
Graduates and/or industry professionals working in fields that require data science and analytical skills.
Contact us
Name: 
Me M Cockeran
Telephone number: 
018 299 2552