Applied AI For Data Insights
Unlock Insights, Drive the Future
Applied AI for Data Insights is an 8-week micro-credential programme that builds practical, job-relevant data analytics capabilities through a structured combination of theory and hands-on application. Learners develop capabilities across the data analytics lifecycle, from data acquisition to insight communication.
The syllabus covers Python programming and statistical fundamentals, followed by data sources, problem formulation, data collection, cleaning, processing, and normalization using industry-relevant tools to prepare real-world datasets for analysis.
Learners gain hands-on experience through real-world projects involving web scraping, data cleaning, normalization, analysis, and visual storytelling, with emphasis on data integrity, standard operating procedures and regulatory compliance.
By the end of the programme, learners will be able to design and execute complete data analysis workflows that support informed, data-driven decision making across teams and industries.
Syllabus Overview
Theory Learning Project (TLP):
TLP 1 – Programming Foundations: Apply Python fundamentals to prepare datasets for analysis.
TLP 2 – Statistical Fundamentals: Interpret basic statistical measures and distributions to support analysis.
TLP 3 – Data Types & Problem Definition: InterpretIdentify appropriate data sources and define clear analysis problem statements.
TLP 4 – Data Collection & Cleaning: Acquire data and perform cleaning to ensure data quality and readiness.
TLP 5 – Data Processing & Normalization: Process and normalize data for structured analysis outputs.
TLP 6 – Data Processing Case Studies: Apply data processing techniques to real-world analytical case studies.
TLP 7 – Data Visualization Tools: Use visualization tools to present analytical results clearly.
TLP 8 – Visualization Techniques & Data Patterns: Analyse data patterns and interpret trends, clusters, and outliers.
Practical Learning Project (PLP):
PLP 1 – Project Planning & Data Acquisition: Define project objectives, identify data requirements, and acquire real-world datasets through web scraping or other data sources.
PLP 2 – Data Preparation & Analysis: Clean, process, and normalise project data, then apply analytical techniques to generate meaningful insights.
PLP 3 – Visualisation & Project Presentation: Develop visualisations and present data-driven findings clearly, following documentation, SOP, and compliance requirements..
Course Outcomes
- Apply Python and statistical fundamentals to analyse real-world datasets.
- Identify data sources, define analysis problems, and prepare data for analysis.
- Acquire, clean, process, and normalise data using appropriate techniques.
- Generate insights using analytical methods and structured workflows.
- Design data visualisations to communicate patterns, trends, and comparisons.
- Execute an end-to-end data analysis project in accordance with SOP and compliance requirements.