Depending on their academic background, students in the MSc in Business Analytics will be required to complete one of two Pre-Programmes:
1. Business Integration Path (BIP). For students with a non-business academic background. This course starts 3 weeks before the beginning of the regular Master's programme classes and includes an online module which normally starts around the beginning of July.
It consists of:
• 4 self Learning online courses that start on 3rd July and have to be completed before the beginning of the in class courses which take place on Sant Cugat Campus.
• 3 weeks starting on August (before the Welcome Week) held in class on the Sant Cugat campus:
• Business Policy
• Introduction to Finance
• Introduction to Marketing
• IT for Managers
• Managing People
• Operations and Logistics
2. Pre-Programme in Data Science. For students with an academic background in business. It comprises the following subjects:
a. Databases SQL and non-SQL: if you want to analyse data, find patterns, or build big data applications, then you must first understand data and databases and know how to query and retrieve data. In this course, you will learn SQL and the main offers in relational databases, including non-SQL databases.
b. Intro to programming with Python: if you want to work in data science and business analytics you need to know programming. This is a basic skill. Two main languages dominate the scene: Python and R (with newcomers such as Julia). In this course, you will learn the basics of Python programming with a focus on data science and scientific programming.
c. Computer Science 101: This course teaches the essential ideas of Computer Science for a zero-prior-experience audience. Computers can appear very complicated, but in reality, computers work within just a few, simple patterns. In this course participants will play and experiment with short bits of "computer code" to bring to life to the power and limitations of computers.
d. Intro to R – Stats & Analytics: this course uses R for exploring statistics and analytics. We will approach inferences and causality relationships – together with the most common fallacies and basic elements of hypothesis testing, distributions, and descriptive statistics in R.