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Data Analytics Foundations introduces participants to the significance and language of data analytics for business and society. The participant will be introduced to the cross-industry standard process for data mining (CRISP-DM), the most common approach to data mining.
This microcredential offers practice in the foundations of data analytics, including identifying data set and attribute types, data preparation and cluster analysis. Advanced techniques for clustering will help develop skills in identifying problems for cluster analysis and a range of approaches to address these limitations. Applying these data analytics techniques enables interpretation of a data set and visual data exploration.
This course has been designed to provide you with an applied introduction to the field of data analytics, and an orientation to its different usages. It has been designed by the UTS Faculty of Engineering and Information Technology, leveraging the Faculty's unique expertise in the area of artificial intelligence.
In this course, you will meet (virtually) and work with a dedicated course facilitator, who will support your learning and engagement with the teaching resources prepared by the academic team and the lead academic.
This course is structured into five modules. Each module includes self-study materials and facilitated online sessions. The five modules are:
1. Introduction to data analytics
In this module, you are introduced to the basics of data analytics. In the weekly live and online sessions, we will review the material and unpack how data analytics can be further applied.
2. Know your data
In this module, we will go through the definition of data, types of data, instances and attributes of the dataset, data quality issues and methods of data collection.
We will also learn about the standard process of data mining in detail with a real-world business scenario.
3. Data pre-processing
In this module, you are introduced to the concept of pre-processing, its techniques, and the effect of pre-processing in transforming the data into a more understandable form.
4. Data exploration and visualisation
Data visualisation represents data in a graphical form that is easy to understand as "a picture is worth a thousand words". It is particularly useful when we are trying to understand data, its trends and outliers.
Data visualisation allows sharing unbiased representations of data which can be particularly helpful in making recommendations to stakeholders.
5. Data clustering
In the first part of this module, you will be introduced to the clustering concept, methods, requirements and types of clustering. In the second part, you will be introduced to one of the most famous of clustering methods, K-Means Clustering.
This course is delivered in a scheduled format over ten weeks.
Each week (during weeks 1 to 8) you will participate in an online session where you will have the chance to apply what you've learned, ask questions and hear from other participants who are taking the course with you. The workshops are led by the course facilitator.
Weeks nine and ten are planned to give you time to complete the final assignment, with support and scheduled Q&A sessions provided.
This microcredential aligns with the 2-credit point subject, Data Analytics Foundations (42821) in the Master of Technology (C04406). This microcredential may qualify for recognition of prior learning at this and other institutions.
This microcredential is accessible to professionals from a wide range of sectors and backgrounds who are new to working with data.
UTS microcredentials are developed for professionals with a capacity to undertake postgraduate tertiary education.
Upon successful completion of this microcredential you will be able to apply pre-processing, transformation and visualisation to business data sets.
You will be able to interpret a contextualized data set and explain your application of data analytics approaches to a peer.
This microcredential includes weekly, live, one-hour online workshops facilitated by an expert UTS academic supporting self-study and online learning activities. Case studies of real-world business operations will be used to illustrate applications of data mining techniques. The workshop sessions focus on hands-on experience in data mining, data analytics tools and understanding and interpretation of the results. Regular formative quizzes throughout the microcredential will allow participants to gauge their progress.
The graded assessment task is an individual written assessment on data exploration and preparation. This assignment includes reflection on individual practical work on data visualisation, exploration and preparation (pre-processing and transformation) for data analytics with artefacts (screen shots/outputs) of the practical work submitted in the report.
Length: 2,000 words
In order to pass the microcredential, participants must achieve an overall mark of 50% or more.
To complete this online course you will need a personal computer with reliable Internet access and the ability to run the latest version of our supported Internet browsers; Zoom for online meetings and classes and the open-source software, KNIME Analytics Platform.
|5 hrs p/w|