ISE01

Data Analysis

Department
Data management
Campus
Ultimo
Level
Level 1
Semester
Bootcamp 2026 S1
Method
Face to Face, Online

If you’re a fresh graduate from any IT or IS. This course is an excellent option for you to cick start your IT career in Data ansysis. The course covers the following modules:

  1. Introduction to Data Analytics
  2. Data Preparation & Cleaning (Excel & KNIME)
  3. Descriptive Analytics with Excel
  4. Descriptive Analytics with Power BI
  5. Descriptive Analytics with KNIME
  6. Introduction to Predictive Analytics with Weka
  7. Predictive Analytics with KNIME
  8. Predictive Analytics with Power BI
  9. Prescriptive Analytics with Excel
  10. Prescriptive Analytics with KNIME
  11. Comparative Analytics (Cross-Tool Projects)
  12. Capstone Project

Provided by the AI Institute of Education, this programme is available by face to face + Bleneded allowing you to study flexibly while balancing work and personal lifes.

The course deepens your understanding of  your previous IT study and experience. The course is very flexibly so you may swap some topics and add others. This happened in the agreement with the academic consultant, who will sit with students individually and determines the best fit modules. 

  • Banking
  • Economic Policy
  • Financial Sector Management
  • Quantitative Finance
Time Place Room Date Range Instructor
10:00am – 01:00pm Ultimo 75 13/11/2025-12/01/2026 Not assigned yet

Data Analysis is a very demanding job in Australia and in the International market. Data is powerfull and dealing with large amount of data in real envrionments is a challenging task. 

The programme aims to promote an understanding of the principles of System administration for both Windows and LINUX though Level 1 management. This provides you with a prompt troubleshooting for servers. Also, it helps you resolving level one tickets, including operating system issues, printers issues, Active directory managements, Windows management, Certificate authority, Ubuntu management and other topics.

Entry requirements

Any certificate or degree in IT or IS. This may include 

  1. Diploma of IT or related
  2. Bachelor of IT or related
  3. Master of IT or related
  4. Graduate Diploma of IT or related
  5. PhD of IT or related
  6. Relevant experience 

Course structure and modules

Week 1 – Introduction to Data Analytics

  • Overview of descriptive, predictive, and prescriptive analytics.
  • Introduction to workflow-based tools (KNIME) and BI platforms (Power BI).
  • Project: Explore a sample dataset in Excel and KNIME.

Week 2 – Data Preparation & Cleaning (Excel & KNIME)

  • Handling missing values, duplicates, and inconsistencies.
  • Excel: Power Query basics.
  • KNIME: Data cleaning nodes.
  • Project: Clean and transform a raw dataset.

Week 3 – Descriptive Analytics with Excel

  • Summarizing data: pivot tables, charts, descriptive statistics.
  • Using Excel add-ins for analytics.
  • Project: Sales performance dashboard in Excel.

Week 4 – Descriptive Analytics with Power BI

  • Data connections and transformations in Power BI.
  • Visualizations: bar, line, scatter, KPIs.
  • DAX basics for calculations.
  • Project: Interactive dashboard for business KPIs.

Week 5 – Descriptive Analytics with KNIME

  • Workflow building for summary stats.
  • Data grouping, aggregation, visualization nodes.
  • Project: KNIME workflow to analyze customer demographics.

Week 6 – Introduction to Predictive Analytics with Weka

  • Machine learning basics: classification vs regression.
  • Weka GUI: datasets, preprocessing, classifiers.
  • Project: Predict student performance using Weka classifiers.

Week 7 – Predictive Analytics with KNIME

  • Machine learning nodes in KNIME.
  • Cross-validation, model evaluation.
  • Project: Build a churn prediction model.

Week 8 – Predictive Analytics with Power BI

  • Integrating R/Python scripts in Power BI.
  • Using AI visuals and forecasting features.
  • Project: Power BI forecasting dashboard (sales or demand).

Week 9 – Prescriptive Analytics with Excel

  • Goal Seek, Solver add-in.
  • Optimization for decision-making.
  • Project: Resource allocation optimization in Excel.

Week 10 – Prescriptive Analytics with KNIME

  • KNIME extensions for optimization.
  • Integration with Python/R for advanced prescriptive modeling.
  • Project: Prescriptive model for supply chain optimization.

Week 11 – Comparative Analytics (Cross-Tool Projects)

  • Same dataset analyzed in Excel, Power BI, Weka, and KNIME.
  • Comparing results and insights.
  • Project: End-to-end mini project (customer segmentation and recommendations).

Week 12 – Capstone Project

  • Full analytics cycle: descriptive → predictive → prescriptive.
  • Team or individual project using at least 2 tools.
  • Examples:
    • Predicting healthcare outcomes.
    • Sales forecasting and optimization.
    • Customer churn prediction + marketing strategy.

How you study

You need to bring your own device (Laptop mainly)

You will be given an account to access our lab computers. The lab includes:

  1. Laptops for assembly
  2. Desktops for Assembly
  3. Printers for Assembly
  4. Servers for Assembly
  5. Ticketing system
  6. Firewall and routers configurations
  7. Active Directory management
  8. Others

Career opportunities

Any Level one IT and Desk support. this may work for:

  1. Financial Institutes
  2. Academic Institutes
  3. Health care Institutes
  4. Government Institutes
  5. IT companies
  6. Insurance companies
  7. Transportation companies
  8. Data centers
  9. Airports
  10. Flight companies
  11. Service desk support companies
  12. etc