Data has become one of the most important assets, if not the most important, that all organizations have today. All companies, without exception, leverage data to make decisions — from everyday (operational) ones to long-term (strategic) ones.
Being able to analyze datasets and extract meaningful insights has become a critical and highly demanded skill in most companies. Data can come in many different sizes, structures, formats, or types.
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In this course, we will focus on providing tools to visualize common datasets and facilitate their analysis by business users through tools such as applications or dashboards.
First half of the semester: Power BI Desktop: load and prepare data, data modeling fundamentals, data visualization
Second half of the semester: collaborate and sharing with Power BI Service: report and data model components
Project: VanArsdel business case (Microsoft dataset)
Week | Session | Unit | Subject | Continuous Evaluation |
1 | Loading data | Power Query basics |
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2 | Loading data | Power Query intermediate |
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3 | Data modeling | Introduction to dimensional data modeling |
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4 | Data modeling | Relationships, DAX | Homework1 | |
5 | Visualizing data | Concepts, Power BI visuals, report settings |
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6 | Visualizing data | DAX, Time Intelligence, interactions set up |
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7 | Summary MidTerm |
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Midterm | Midterm evaluation |
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8 | Mobile reports |
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9 | Publishing and sharing reports | Power BI Service online components | Homework2 | |
Campus/ holiday |
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10 | VanArsdel dataset project |
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11 | VanArsdel dataset project |
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12 | VanArsdel dataset project |
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13 | Summary Final |
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Final | Final evaluation |
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The following table relates the learning outcomes to the content taught to achieve them:
RA | Syllabus |
R1 | Data Visualization: preliminary steps and objectives |
R2 | Types of Information and charts selection |
R3 | Dashboard design |
R4 | Tools for data visualization |
R5 | Advanced charts technique |
The course is mostly practical (75%).
Students will learn to connect different data sources to Microsoft Power BI using the Power Query editor.
as well as will replicate techniques demonstrated by the teaching staff.
Theoretical content (25%) will also be covered intermittently to provide meaning to what is built, such as the DAX language and the fundamentals of data modeling.
R1, R2, R3, R4 | Continuous Evaluation | 25% |
R2, R3 | Mid-Term Exam | 15% |
R2, R3, R4 | Final Exam | 30% |
R4, R5 | Group Project | 30% |
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· Knaflic, C.N. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Hoboken, New Jersey: Wiley.
· Cairo, A. (2016). The truthful art: data, charts, and maps for communication. New Riders.
· Hopkins, W. (2022). Power BI for the Excel Analyst. Holy Macro! Books.
· Russo, M. and Ferrari, A. (2020). The definitive guide to DAX: business intelligence with Microsoft Power BI, SQL server analysis services, and Excel. Microsoft Corporation By Pearson Education.
· Kimball, R. and Caserta, J. (2009). The data warehouse ETL toolkit practical techniques for extracting, cleaning, conforming, and delivering data. Indianapolis, Ind. Wiley.
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