Data Science micro-credentials are for ICT professionals who wish to develop skills to support careers in Data Science and Data Engineering. Data Engineers design and build the infrastructure for data manipulation. While Data Scientists are experts in Machine Learning and Analytics methods that derive insights from data.
Data Science micro-credentials address skill development needs in areas such as big data programming, machine learning, data analytics and AI and are delivered by leading Data Science experts.
Introduction to Data Analytics
Introduction to probability and statistical inference, focusing on understanding concepts and methodologies.
Data Prog with R
Beginners introduction to the open-source statistical programming language R, and how to build programmes and handle datasets
Data Programming with Python
Learn how to manipulate data and perform statistical analysis using Python.
Predictive Analysis 1
The micro-credential uses a variety of real-world examples to illustrate the use of different data analytical techniques and regression models, covering both theoretical and practical considerations.
Introduction to computationally intensive modern Statistical methods, including concepts and methods involved in simulating from distributions using R.
This micro-credential delivers the basic techniques in Time Series Analysis. It will mainly focus on examples in the world of economics and finance.
Statistical Machine Learning
This micro-credential delivers a collection of techniques for discovering patterns in data and making predictions at the intersection of Machine Learning and Statistics.
Bayesian Analysis (Online)
This micro-credential will provide an introduction to Bayesian analysis with an emphasis on concepts in Bayesian theory and practice, focusing on statistical programming (Stan programming language).
This micro-credential will cover many common statistical techniques used to analyse high dimensional data (including: clustering techniques; classification techniques; ordination techniques; etc) using R software.
Data Programming with C
Covers the essence of programming with data using the languages C and C++, focus on incorporating such code into the R statistical environment.
Data Programming with SAS
Focuses on how to manipulate data and perform statistical calculations using SAS.
Adv. Data Prog. with R
Covers advanced use of R and Rstudio, following on from the Data Programming with R micro-credential.
Statistical Network Analysis (online)
Focuses on the analysis of relational data and reviews the available methodologies and algorithms that can be employed to model network interactions.