Business Intelligence
with Power BI

Learning Outcome
Develop interactive dashboards in Power BI that present data insights accurately.

Topics:

• Overview of business intelligence applications and the role of Power BI in driving data-driven decision-making.
• Introduction to Power BI features, including data preparation, visualization tools, and workspace navigation.
• Fundamentals of multidimensional reporting and techniques for organizing and analyzing data.
  Developing interactive dashboards using visuals, slicers, filters, and drill-through features for dynamic insights.

Learning Outcome
Select statistical methods to interpret data effectively for informed decision-making.

Topics:

• Introduction to statistical techniques: Probability theory, probability distribution, and hypothesis testing.
• Applying probability distributions to analyze and interpret data trends.
• Techniques for recognizing significant trends and identifying aberrant results in large data sets.
• Using Power BI to apply hypothesis testing and validate statistical outcomes.

Learning Outcome
Examine data accuracy and acceptability using statistical techniques.
Topics:

• Test conditions for applying various statistical techniques.
• Introduction to uncertainty analysis and its importance in decision-making.
• Statistical tests for determining data accuracy and reliability.
• Tools for implementing statistical tests within Power BI.

Learning Outcome
Use Power BI tools to troubleshoot data issues and optimize performance through effective analysis.

Topics:

• Techniques for interpreting results from statistical modeling.
• Methods for uncovering trends and patterns in large data sets.
• Identifying and analyzing potential causes of unacceptable data or performance issues.
• Power BI tools for data troubleshooting and optimization.

Learning Outcome
Utilize R and Python tools to implement statistical data analysis applications for solving data-driven problems.

Topics:

• Introduction to R and Python for data analysis.
• Understanding statistical data analysis applications using R and Python.
• Analyzing large and complex data sets using R, Python, and Power BI.
• Using R and Python scripts for data cleaning, predictive modeling, and hypothesis testing.

Learning Outcome
Set up discussions with stakeholders to ensure issues relating to big data issues are examined and analysed.

Topics:

• Techniques for big data analytics: Best practices and key considerations.
• Identifying organizational issues where big data analytics can be applied.
• Derive insights and recommend solutions based on the documented discussions.

Business Intelligence (BI) is the process of collecting, analyzing, and visualizing data to help organizations make informed, strategic decisions. It involves using tools and technologies—like dashboards, reports, and data visualizations—to turn raw data into meaningful insights that support business performance, efficiency, and growth.

BI helps companies understand trends, identify opportunities, monitor key performance indicators (KPIs), and solve problems faster. It combines data from various sources to provide a comprehensive view of operations, making it easier for decision-makers to take action based on real-time, accurate information. At its core, Business Intelligence empowers businesses to become more data-driven, agile, and competitive.

Power BI is a powerful business analytics tool developed by Microsoft that enables users to connect, visualize, and share data in interactive and meaningful ways. It allows you to pull data from various sources—such as Excel, databases, cloud services, and more—and turn it into dynamic dashboards and reports that help uncover insights and support better decision-making.

This course has been accredited as a WSQ course and is eligible for funding under the SkillsFuture funding, subject to all eligibility criteria being met. Participants are advised to assess the suitability of the course and its relevance to his/her business activities or job roles. The SkillsFuture Funding provides 50% - 70% course fee subsidy support for direct training costs subject to all eligibility criteria being met.

Find out more on https://www.skillsfuture.gov.sg/funding-individuals

Yes. For self-sponsored Singaporeans aged 25 years old and above, you can use your SkillsFuture Credit to offset the remaining course fees after WSQ funding.

To check your SkillsFuture credit balance, please follow these steps:

1. Go to https://myskillsfuture.gov.sg
2. Click on ‘Submit SkillsFuture Credit Claims’
3. Login with your SingPass
4. Click on the arrow (>) at the top right hand corner. You will be able to see a drop-down list of your Available SkillsFuture Credits.

After you have registered for a course, a VI representative will reach out to guide you with the SkillsFuture Credits

Yes, all our courses are eligible for Union Training Assistance Programme (UTAP) Funding. NTUC Union members can use UTAP to offset 50% of unfunded course fees (capped at $500 per year).

This claim must be done after completion of the course. Please refer to the UTAP FAQ for more information.

Yes, both can be utilized concurrently. UTAP claims are processed after SkillsFuture Credits have been applied.

Illustrative Example:

• Total Course Fee: $1,000
• IBF Subsidy (70%): $700
• Remaining Fee: $300
• SkillsFuture Credit Applied: $200
• Out-of-Pocket Expense: $100
• UTAP Reimbursement (50% of $100): $50
• Final Cost After Reimbursements: $50

A laptop is required for this course. No special software or other hardware is required for this course participation.

Upon successful completion of this WSQ-accredited course, you'll receive a Business Intelligence with Power BI certification to showcase your knowledge and expertise in this field.

Our programmes are highly respected by leading companies, who actively shape our curriculum and utilize our courses for employee training.