- PROGRAM OVERVIEW
- COURSE STRUCTURE
- LEARNING OUTCOMES
- LEARNING REQUIREMENTS
- PROGRAM OUTLINE
- COURSE GRADING
This course presents the fundamentals of data analysis and visualization. It introduces all the necessary concepts like database modeling and design, SQL, business intelligence, multidimensional databases and non-SQL databases.
Data Analytics is a program that includes lectures and hands-on training at our laboratories. The course is self-contained, in the sense that the student is not expected to spend additional hours apart from the course itself.
On the other hand, attendance to classes is required and the class material must be studied to sit for exams.
The student is expected to:
o Understand how to create the different data models (conceptual, logical, physical)
o Use SQL to manipulate data in a relational database
o Understand the business intelligence concept and business value
o Use data analysis tools to create reports and dashboards
o Explain the fundamentals of non-SQL databases
Mathematical experience related to basic concepts about set theory and mathematical logic is required. In addition, It is desired that students have some knowledge about the fundamentals of programming.
Program start date: June 16th
Program end date: July 8th
Course Length: 4 weeks
Duration of classes: 6 hours
Definition of relational databases. Data Modeling. Logical models. Entities, attributes and relationships. Primary Key. Foreign Key. Types of relationships. Constraints. Normalization. Denormalization. Physical models.
Creating a Logical Data Model. Transforming a Logical Data Model to a Physical Database Design.
Introduction to SQL. Select. Operators. Logical conditions. Order by. Functions. Group by. Having. Join. Triggers. Procedures.
Creating tables and inserting data. Programing Triggers and Procedures
Introduction to Business Intelligence. Multidimensional data model. Conceptual, Logical and Physical Data Model.
Connect, import, shape, and transform data for business intelligence (BI).
Data Visualizations Tools. Reports and Dashboards.
Visits to industries and advanced databases
Big Data. NoSQL Databases. Internet of things.
o Kroenke, David M. (2000). Database Processing: Fundamentals, Design and Implementation. New Jersey: Prentice Hall.
o Mannino, Michael V (2007). Administración de Base de Datos – Diseño y desarrollo de aplicaciones. Mexico: Mc. Graw – Hill.
o Kimball, Ralph y Gross, Margy (2002). The datawarehouse toolkit. New York: Wiley.
o Bruce, Thomas A. (1992). Designing Quality Databases with IDEF1X Information Models. New York: Dorset House Pub.
o Giménez, Matilde Celma, Casamayor Ródenas, Juan Carlos y Mota Herranz, Laura (2003). Base de datos relacionales. Madrid: Pearson-Prentice Hall.
The final course grade will be based on a percentage system of the points accumulated during the semester, according to the following scale:
International Programs Office