Intro to Data Analysis and Interactive Visualization

Course pre-requisite(s): Students are expected to have a basic knowledge in economics and some experience with Excel or other spreadsheet program. Programming experience is not required.

Course Overview

The amount of data is growing exponentially every year: they are generated by enterprises regardless of the activity field, we generate them ourselves in social networks, applications, etc. Data become a valuable resource, and someone who knows how to handle them correctly, analyze them and make data-driven decisions has a huge advantage over others. The ability to visualize data becomes especially important when personal offline presentation of data analysis results is impossible. The growing popularity of interactive dashboards and infographics requires a new skill - working in specialized programs for visualizing data. This is exactly what this course aims to do - to improve your data literacy: teach you to understand data, work with them and present them to others.

This course provides an introduction to Data Analysis, including the processes, infrastructure, and current practices used to transform business data into useful information for supporting business decision-making.

The course is structured into two parts: introduction to data analysis theory and practice of interactive visualization. In the first part, you will study the role of data in the modern world, the evolution of Business Intelligence, the route that data takes from the operator to the analyst and then to the end user, learn the basic skills of working with databases, intro to data analysis. The second part of the course will study the basic theory of data visualization, popular types and visualization tools.

Learning Outcomes

By the end of the course students

Will find out:

  • basic concepts regarding business intelligence and management information systems;
  • understanding the role of data in supporting management decision making;
  • the tools and concepts that apply to dashboard creation;
  • knowledge on how to select and use appropriate data mining algorithms and techniques.

Should be able to:

  • define main statistical terms (population, sample, and parameter);
  • differentiate the four levels of data: nominal, ordinal, interval, and ratio;
  • understand the flow of data from the source to the end user;
  • create and interpret business process models;
  • describe a data distribution statistically and graphically;
  • create dashboards using PowerBI, Tableau and online visualization tools.

Will improve:

  • skills in data analysis;
  • visualization skills;
  • communication skills;
  • teamwork skills.

Course Content

  • Data analysis
    • Introduction to Data Analysis and Business Intelligence
    • Data types and Data sources
    • Databases, MapReduce, SQL
    • Business processes vs. data flow
    • Data Analysis and Interpretation
  • Data visualization
    • Introduction to Data Visualization and Dashboarding Software
    • Analyzing and Visualizing Data with Power BI
    • Data Representation and Visualization in Tableau
    • Online visualization tools
  • Assessment

Instructional Method

Lectures&workshops, interactive teaching, discussions, case studies, supporting videos, collaborative teamwork, presentation.

Required Course Materials

Facilities: laptops, Internet connection, multimedia projector.

Required readings and useful links:

  1. A Guide to the Business Analysis Body of Knowledge (BABOK Guide). International Institute of Business Analysis, Canada. 2009. v. 1.6. 329p. URL: https://cs.anu.edu.au/courses/comp3120/public_docs/BOKV1_6.pdf
  2. Black K. Business Statistics For Contemporary Decision Making. 2010. URL: https://faculty.ksu.edu.sa/sites/default/files/business-statistics-for-contemporary-decision-making-by-ken-black_0.pdf
  3. Chen C., Härdle W., Unwin A. Handbook of Data Visualization. URL: https://haralick.org/DV/Handbook_of_Data_Visualization.pdf
  4. Evans J.R. Business Analytics: Methods, Models, and Decisions. 2020
  5. Fayyad U., Piatetsky-Shapiro G., Smyth P. From Data Mining to Knowledge Discovery in Databases. AI MAGAZINE. 1996. pp.37-54. URL: http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-Fayyad.pdf
  6. Kijsanayothin P., Chalumporn G., Hewett R.  On using MapReduce to scale algorithms for Big Data analytics: a case study. Journal of Big Data. 2019. volume 6.
  7. Marr B. Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. 2016. URL: http://www.bdbanalytics.ir/media/1169/bernard-marr-big-data-in-practice_-how-45-successful-companies-used-big-data-analytics-to-deliver-extraordinary-results-wiley-2016.pdf
  8. Silva Y.N., Almeida I., Queiroz M. SQL: From Traditional Databases to Big Data. 2016. DOI: 1145/2839509.2844560
  9. Wexler S., Shaffer J., Cotgreave A. The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios. Wiley, 2017. 448 p. 
  10. Yau N., Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. 2011

Assessment

Case Studies (40%)

Presentation defense (25%)

Quiz (15%)

Final Test (20%): tests, true/false statements, practical tasks.