| Students are able to effectively structure and manage complex data projects, applying best practices in data organization and coding to solve analytical challenges in economic and business contexts. They are equipped to assess, clean, and prepare datasets for meaningful analysis. Students develop competence in interpreting and visualizing data insights, enabling them to convey complex findings clearly to support decision-making.
Course Goals
This course covers advanced principles of data organization and analytics tailored for economic and business contexts. Students will gain the skills to manage, analyze, and present complex datasets relevant to real-world economic and business questions. The course provides a solid foundation in organizing data projects, coding essentials, and employing data management techniques crucial for effective analysis. Students will learn not only the technical aspects of data analytics but also how to interpret and report findings in a way that informs decision-making. By the end of this course, students will have a comprehensive toolkit to approach advanced data challenges with analytical rigor and clarity.
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- Learning Outcome 3 (LO3): Apply coding techniques to streamline data processing tasks.
- Learning Outcome 4 (LO4): Manage and clean datasets, ensuring data quality and consistency for advanced analyses.
- Learning Outcome 5 (LO5): Develop visualizations to represent complex data patterns and trends effectively.
- Learning Outcome 6 (LO6): Interpret and communicate analytical results, applying them to support data-informed decision-making in economic and business contexts.
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- Learning Outcome 1 (LO1): Recall foundational concepts in data organization, coding, and data management specific to economic and business analytics.
- Learning Outcome 2 (LO2): Understand the principles of data visualization and effective reporting methods for communicating analytical insights.
Course Topics:
- A) Elementary concepts and data organization
How to set up and organize a project, replicability, data types, memory, importing and
exporting data.
- B) Programming preliminaries
Using lists, logical qualifiers, strings, observation numbering;
functions, macros, scalars, and matrices; loops.
Data validation; reorganizing and combining datasets, useful data management commands.
Store, save, and reuse computed results; automate reporting of estimation output and
graphs, produce publication-ready tables.
- E) Data analysis and visualization
Summary statistics, cross-tabulations, graphs, geographical maps.
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