Advanced Professional Certificate in Data Analytics and AI Tools (Synchronous and Asynchronous E-Learning)
About This Course
The exponential growth of the internet, globalization and Artificial Intelligence (AI) have accelerated the push for digital transformation in all sectors. Companies have pivoted and reinvented themselves to remain relevant. Much of this transformation involves changing processes and workflow to gain efficiencies, productivity and competitive advantages, leading to increase revenues and profitability. Businesses started to make investments in technology and retrained their workforces to support those new processes and workflows.
As a result of digital transformation there has been a dramatic increase in the types and quantities of data they began to accumulate. These businesses soon realized that they could use the deep understanding of that data for competitive and even absolute advantages. However, the realization that whilst they had skilled managers and engineers, there was a need for the skill to now work with the data they had, including how manage and deal with different types of data.
There is a new driver for sustainable employability and that is Data and Information Literacy. The data and information literate professional does more than just crunch numbers. The expectation is for data professionals to add value by bringing their domain knowledge – engineering, finance, logistics, etc. – into data activities like data sourcing, wrangling, management, analysis and communication.
In a nutshell, being data and information literate is about being able to:
• Data Collection and Analysis: Seek out, acquire and manage the data that is going into the organization, used within the organization and communicated by the organization.
• Analytical Method Validation: Examine validation datasets, identify variability or deviations, and interpret findings to confirm method performance, consistency, and suitability.
• Data and Information Visualisation: Report and present the findings or insights through dashboards and other means.
What You'll Learn
Module 1: Data Management, Data Wrangling and Exploratory Data Analysis (EDA)
This segment focuses on transforming raw data into a usable format for analysis and visualization. As organisations handle increasing volumes of data from diverse sources, participants will learn essential data wrangling and exploratory data analysis (EDA) skills, including understanding data structures, managing different data types, and preparing data for insights. The programme introduces practical tools such as SQL and Power Query for efficient data extraction and shaping, while also exploring how Generative AI can enhance and streamline the data preparation process.
Module 2.1: Analysing and Interpreting Data
This segment develops essential data analysis and insight-generation skills, from interpreting charts to presenting findings to diverse audiences. Participants will learn to maximise Excel for data analysis, apply forecasting and Delphi methods, understand data governance, and leverage Generative AI to enhance analytical decision-making.
Module 2.2: Mini-Project (Capstone)
The mini project will bring skills like critical thinking, data best practices to the forefront as data analytics is not just about the tools.
Module 3: Communicating Insights, Dashboarding and Data Storytelling
This segment focuses on effectively communicating data insights through visualisation. Participants will learn how to tailor data stories for different audiences and use tools such as Power BI and Tableau to present insights clearly, enabling faster understanding, performance tracking, and deeper analysis.
Entry Requirements
The minimum entry requirements are:
• Diploma or Tertiary level qualification
• Have taken Mathematics as a subject at secondary level
• 3-years working experience