UEN No.: 198802365N


ICDL Data Analytics – Foundation (Synchronous e-learning)

course details

Duration and Time

Training Time


Course Duration

16 HRS

Course Information

Course Reference Number


Funding Validity Period

26 Nov 2021 To
31 Dec 2022

Mode of Training

Synchronous e-Learning


The ICDL Data Analytics – Foundation module sets out essential knowledge and skills relating to data analytics concepts, statistical analysis, data set preparation, data set summarisation and data visualization.

After the course, you will be able to:

  • Understand the key concepts relating to the application of data analytics in business.
  • Understand and apply key statistical analysis concepts.
  • Import data into a spreadsheet and prepare it for analysis using data cleansing and filtering techniques.
  • Summarise data sets using pivot tables and pivot charts.
  • Understand and apply data visualization techniques and tools.
  • Create and share reports and dashboards in a data visualization tool.
1. Concepts and Statistical Analysis
1.1 Key Concepts
  • Identify the main types of data analytics: descriptive, diagnostic, predictive, prescriptive, quantitative, qualitative.
  • Outline the business benefits of data analytics: identifies patterns/trends, improves efficiency, supports decision making, presents information effectively.
  • Identify the main phases of data analysis: business understanding, data understanding, data preparation, modelling, evaluation, deployment.
  • Recognise data protection considerations when analysing data like: anonymise personal data if possible, comply with applicable data protection regulations.
1.2 Statistical Analysis
  • Describe measures of central tendency of a data set: mean, median, mode.
  • Calculate the central tendency value of a data set using a function: mean, median, mode.
  • Describe measures of variation of a data set: quartiles, variance, range.
  • Calculate the variation of a data set: quartile, variance, range.
2. Data Set Preparation
2.1 Importing, Shaping
  • Import data into a spreadsheet application: .csv file, spreadsheet, website table, database table.
  • Remove duplicate data.
  • Validate that given values belong to a reference data set using the vlookup function.
  • Validate that given values belong to a specified range using one or more if functions.
  • Extract values from a string using text functions: left, right, len, mid, find.2.2 Filtering
  • Format a data set as a built-in table.
  • Insert and use table slicers.
3. Data Set Summarisation
3.1 Pivot Table Data Aggregation
  • Change the method of aggregation for a value: sum, average, count, minimum, maximum.
  • Display multiple aggregation values.
  • Display values as: % calculation, difference from specific values, running total, ranked.3.2 Pivot Table Frequency Analysis
  • Automatically, manually group data and rename groups.
  • Ungroup data.3.3 Filtering Pivot Tables
  • Use the report filter
  • Insert and use slicers to filter single, multiple pivot tables.
  • Insert and filter a timeline3.4 Using Pivot Charts
  • Insert a pivot chart for an existing pivot table.
  • Create a pivot chart from fields in a data set.
4. Data Visualization
4.1 Concepts and Setup
  • Understand the concept of data visualization using reports and dashboards. Outline common visualizations like: charts, key performance indicators (KPIs), maps.
  • Recognise common data visualization tools and their functions like: visualise data, publish and share business intelligence.
  • Understand good design practice in reports and dashboards like: clean and uncluttered layout, descriptive titles, consistent fonts and colour, use of colour for emphasis and understanding.
4.2 Visualization
  • Create tables in a report.
  • Visualise data as a chart: column, bar, line, pie.
  • Apply, edit font and background conditional formatting to show: high/low values, above/below average values.
  • Apply, edit data bars.
  • Apply, edit visual level filters.
4.3 Publishing and Sharing
  • Publish a report.
  • Create a dashboard.
  • Share a report, dashboard using a link. Share a report to web.

Data scientists, finance/accounting professionals, consultants, project managers, administrators and other professions who require some knowledge of data management and analysis.

  • Learners must possess WPL level 5 and WPN level 5.
  • Learners must be able to operate computers at intermediate level
Course Outline

Download the PDF below for a summary of the course outline.


Company Sponsored

Funding Type Full Fee 9% GST (on full course fee) Funding Amount Nett Fee Payable (incl. GST)
Small Medium Enterprises (SMEs) $0 $0 $0 (70% off course fee) $0
Non Small Medium Enterprises $0 $0 $0 (50% off course fee) $0
Singaporean Employees aged 40 years and above $0 $0 $0 (70% off course fee) $0

Course Fee Funding:
SME: 70% of course fee
Non-SME: 50% of course fee
Non-SME(MCES): 70% of course fee

$4.50 per training hour capped at $100,000 per enterprise per calendar year


Funding Type Full Fee 9% GST (on full course fee) Funding Amount Nett Fee Payable (incl. GST)
Singapore Citizens (40 years and above) $0 $0 $0 (70% off course fee) $0
Singapore Citizens (21 to 39 years old) & Permanent Residents (21 years old and above) $0 $0 $0 (50% off course fee) $0

Course Fee Funding:
MCES: 70% of course fee
Normal: 50% of course fee


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