WSQ

Effective Data Analysis using Excel

OVERVIEW

course details

Duration and Time

Training Time

9.00am - 6pm

Course Duration

16 HRS

Course Information

Course Reference Number

TGS-2023020187

Funding Validity Period

17 Apr 2023 To
16 Apr 2025

Mode of Training

Classroom

COURSE SYNOPSIS

COURSE SYNOPSIS

This course will equip learners with data analysis skills using Excel by applying the various formulae and functions available in Excel as well as using Power Pivot to connect to different and multiple data sources for statistical analysis.

COURSE OBJECTIVES

By the end of this course, learners will be able to:

  • Apply principles of data modelling and visualization to create data models to identify and predict trends and patterns.
  • Use data science techniques to perform data analysis on data sets.
  • Apply data protection and legal guidelines to support the creation of business insight reports
  • Apply DAX language to summarize data to provide business insights from data source.
  • Apply statistical techniques and formulas to produce analytical dashboards and insight reports.
  • Join multiple tables using unions to extract data analysis aligned to the domain context.
  • Create clean required dataset with functions and formulas to visualize data.

Target Audience (Operational, Supervisory & Managerial)

This training is relevant to PMETs who are aspiring to move away from just creating spreadsheets onto a data analytics scale using a tool that they are relatively familiar with.

Assumed Skills

  • Be able to listen and speak English at a proficiency level equivalent to the Employability Skills Workforce Skills Qualifications (“ES WSQ”) Workplace Literacy (“WPL”) level 3.
  • Be able to read and write English at a proficiency level equivalent to ES WSQ WPL level 3.
  • Be able to manipulate numbers at a proficiency level equivalent to ES WSQ Workplace Numeracy (“WPN”) level 3.
  • Learners to have minimum GCE ‘O’ level or ITE certificate education
  • Learner should have at least 1 year’s working experience in any industry.
  • Be able to operate a computer.

Course Content

Learning Unit 1 – Understanding Data Modelling and Visualization to Perform Data Analysis
  • Create Data Models using Power Pivot
  • Create Pivot Tables and Pivot Charts for Forecasting and Recognizing Patterns and Trends.
Learning Unit 2 – Using Excel Functions to Apply Techniques on Data Sets for Data Analysis
  • Introduction to Advanced Excel Functions and Features for Data Analysis
Learning Unit 3 – Using Power Pivot for Data Modelling and Applying Data Integrity
  • Using Power Pivot Models to Create Business Insight Reports
  • Applying Data Protection Rules in Excel
Learning Unit 4 – Using DAX for Analysis to Provide Business Insights
  • Using DAX to summarize data for analysis and business insights
Learning Unit 5 – Using Statistical Techniques and Data Analysis Tool Pak for creation of Dashboard reports
  • Introduction to Statistical Formulas and Techniques for Dashboard Reports
  • Data Analysis Tool Pak for Business Insight Reports
Learning Unit 6 – Using Excel Functions and Queries to Extract Data for Data Analysis
  • Joining Multiple Tables to Extract Data for Data Analysis and Visualization
Learning Unit 7 – Transforming Data for Data Analysis using Excel Functions and Formulae
  • Data cleaning and validation of Required Dataset for Visuals
Course Outline

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

COURSE FEES

Company Sponsored

Funding Type Full Fee GST Funding Amount Nett Fee Payable (incl. GST)
SME $480 $43.20 $336 $187.20
Non-SME $480 $43.20 $240 $283.20
Non-SME
(MCES)
$480 $43.20 $336 $187.20

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

Self-Sponsored

Funding Type Full Fee GST Funding Amount Nett Fee Payable (incl. GST)
MCES $480 $43.20 $336 $187.20
Normal $480 $43.20 $240 $283.20

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

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