Learn Gdp Analysis in Power BI and Excel

As the primary reason of the existence of Resagratia which is to democratize data analytics and support the future workforce, Resagratia brings you closer to working on real life projects through the use of case study courses. Case study courses are in-depth, detailed examination of peculiar scenarios within a real world context. Typically, case study data are gathered from different sources through the use of several methods in order to portray data-driven research in a way that adequately supports continuous learning lifelong learning

Curriculum Content

GDP analysis using Excel and Power BI

Get started on the Resa case study courses with this experiential course on the Analysis of Gross Domestic Product (GDP). In this case study, you will delve into what GDP entails, the different types of GDP, how GDP is measured, and the factors that contribute to GDP growth. You will get to take ownership of this project and have the skills to apply this to analysing other economic indicators. From sourcing and transforming data to analysing, reporting and publishing your insights to the world. This course utilizes data analytics tools such as Excel, Power Query and Power BI to bring your data to life and nudges you in the direction of time series and predictive analytics. Let’s get started.

4 hours

Personalized course work

Study group sessions

Expert tutors

Geovanni Ubah

Geovanni Ubah

Data Analyst at M-Kopa

Organizations where some of our students come from

/static/media/cu.9b013cf236264c32c994.png
/static/media/lbschool.097d77e16cc33d3b5f1e.png
/static/media/egbin-power.af5e008af772f9565023.png
/static/media/m-kopa.7b2b8a0d882f84b6d223.png
/static/media/shell.23463a783a2912e9e382.png
data:image/png;base64,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
/static/media/trade-depot.b963d0ac0cbf3f682df8.png
/static/media/uofibadan.e72d2718d02929786cde.png
/static/media/dangote.2b0773bb79fcfa46c9a7.jpg
data:image/png;base64,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

Want to skill up?

Join our learners to gain in-demand skills

FAQs

Frequently asked questions

  • Do I need to have prerequisite knowledge of case study topics?

    No, as all case study courses will cover the basic domain knowledge needed to be able to undertake the course for you to be fully immersed in your learning journey

  • Is the Course free?

    Full access to the case study courses are only provided for premium users

  • How is a case study project of benefit to me?

    Asides from building your data analytics portfolio, you will gain the confidence and skills of a professional data analyst with real-life project experience

  • What do I need to get started?

    There is little or no prerequisite knowledge required to get started. However, you do need access to stable internet to get the best out of the learning contents

resa logo

Empowering individuals and businesses with the tools to harness data, drive innovation, and achieve excellence in a digital world.

Copyright 2025Resagratia. All Rights Reserved.