ICDL Data Analytics – Foundation

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, ▪
• Describe measures of variation of a data set: quartiles, variance, range.
• Calculate the variation of a data set: quartile, variance, range. 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 pivot tables.
• Insert and filter a timeline

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. 3.4 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.
• Import a data set from a spreadsheet into a data visualization tool and save the file.

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.