Data Science Certificate

The Data Science Certificate consists of 4-courses providing a concentrated perspective into the complex field of data science and business analytics.

It is designed to provide students with skills to develop data science programming, analytics, and decision-making techniques, as well as, predictive analytics and data visualization.

These Certificate programs will give students the necessary skills to take on web development and data science positions in our evolving technological world.

Certificate graduates have the confidence to pursue careers in the private sector, in public administration, international organizations, and NGOs.

Graduate Certificate students complete the four main concentration courses of the UBIS MSIT program. Only 4 courses total that can be completed in as short as 4 months or as long as 2 years.

A total of 12 US / 24 ECTS.

• MGT 607 Business Analytics, Data Analytics, and Decision Making
• MIT 630 Programming for Data Science
• MIT 632 Predictive Analytics
• MIT 634 Data Visualization

MGT 607 Business Analytics, Data Analytics, and Decision Making

Credits: 3 US / 6 ECTS

This course provides an overview and motivation for the use of qualitative and quantitative research methods, including observation, interviewing, questionnaires and surveys, and the use of case studies. This course will provide students with the necessary background in research methods to carry out different kinds of research during their graduate studies, but also be able to recognize in the future what makes good research, whether this is in journalism, social and economic reporting, industry, politics, and academia.

MIT 630 Programming for Data Science

Credits: 3 US / 6 ECTS

This course introduces modern programming platforms to analyze data of various volumes and varieties in the field of data science. Software libraries and standard utilities are used to put into practice the various phases of the data science methodology: preparation, analysis, setup, modeling, and evaluation. Using Real-world data sets and a sound methodology, students will develop an understanding of technical programming in one or more languages. They will also develop an understanding of computational modeling and analysis techniques as they pertain to data science. Emphasis is given to the following: Decision Trees, Naïve Bayes, Neural Networks, k-means clustering, and regression modeling.

MIT 632 Predictive Analytics

Credits: 3 US / 6 ECTS

Predictive analytics is the process of extracting useful information from historical data and presenting it for the purpose of predicting future trends. The course introduces the basics and the modeling process of predictive analytics for business applications, including a hands-on introduction to data preparation, model identification and validation, presentation, and interpretation of results.

MIT 634 Data Visualization

Credits: 3 US / 6 ECTS

This course is designed to provide students with the essential principles of data visualization. It provides a very hands-on approach to creating meaningful displays of quantitative and qualitative data and to presenting insights succinctly and clearly. A substantial part of the course addresses issues important to constructing good charts. Topics include visual perception, the various chart types, the effective use of colors, maps, and other visualization techniques for incorporating analytics and storytelling. It then covers the important topic of visualizing and describing variability that occurs in observed values. This leads to the examination of the use of visualization in exploratory data analysis. As well as the rationale and techniques for how to explain and influence data visualization. The course concludes with how to create data dashboards and the importance of the responsible use of data visualization. Students use visualization tools throughout the course.