Degree in Business Intelligence and Data Analytics

Lead the transformation of companies through the use and analysis of data.

Principles of programming

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Description
Data Analytics is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of "Principles of Programming" is to help you learn the fundamental tools in R that will allow you to start analyzing data. After this course, you'll have the first toolsto tackle some data analytics challenges, using some parts of R software.
Type Subject
Primer - Obligatoria
Semester
First
Course
1
Credits
5.00

Professors

Rafael Alfonso Espinosa Valencia
Giuseppe D'Amico
Previous Knowledge
Objectives

Learning Outcomes of this subject are:
R1. Understand the world of computing
R2. Understand the stages of development of a programming project
R3. Know the Fundamentals of programming
R4. Know how to import and view structured data
R5. Know how to analyze structured data
R6. Know how to communicate with data

Contents

First part of quatrimester
- Understand the world of computing.
- Different ways of computing. API, scrapping.
- Structured and unstructured data
- Local versus Cloud
- Application: R code, Python versus Appliance (BigML, Gephi, QGIS)
- What problems do programmers / data scientists solve?
- The contribution of data science in the business world.

Second part of quatrimester
- What are the different profiles: Data Scientist, Data Engineer, Programmer, Functional Analyst, Technical Analyst, Big Data Expert, etc.?
- What is the methodology to follow when we program?
- What is the CRISP Methodology?
- Where are the sources available?
- How can I request access to Twitter-like sources?
- Most frequent problems

Methodology

The subject has two teaching sessions every week. Each Session is divided into Two parts: a first part is masterful in which the teacher explains the new Contents and 1 Second in which the students work in new Exercises to consolidate the subject. Every two or three sessions, individual or group evaluation activities are carried out by means of written tests, collection of exercises carried out at home, etc.

Evaluation

R2 15% - CLASS ACTIVITY (TEST, QUESTIONNAIRE, HOMEWORK, ETC.)

R4 35%:
- 15% WORK IN GROUP
- 20% 1st MIDTERM

R5 15% - WORK IN GROUP

R6 35%:
- 15% GROUP PROJECT
- 20% FINAL EXAM (ORDINARY CALL)

Evaluation Criteria
Basic Bibliography

- Laudon, Kenneth C. and Laudon, Jane P. (2014). Management Information Systems: Managing the Digital Firm. Global Edition. 13th edition.
- Davenport, T & Harris, J. Competing on Analytics: The New Science of Winning (2014, 2017). Havard Business School
- Foster Provost and Tom Fawcett. Data Science for Business. O'Reilly (2013).
- Carl Anderson. Creating a Data-Driven Organization. O'Reilly. (2015).
- DJ Patil. Building Data Science Teams. O'Reilly. (2011).
- DJ Patil and Hilary Mason. Data Driven: Creating a Data Culture. O'Reilly. (2015).

Additional Material