COURSE CONTRIBUTION TO PROGRAM
R is one of the leading software for data science worldwide.It is widely used in big companies and institutions, such as McKinsey&Company, Banco Santader or Telefónica.
Learning R is fundamental to understand better what is data science and to learn how to put analytics and artificial intelligence at work.
The objective of the seminar is therefore to introduce relevant concepts of R and data analysis, focusing in particular on the interpretation and validity of the results that are obtained. At the end of the course, students should:
- Understand the very basics of statistics applied to business and decision-making
- Be familiar with R, and be able to make statistical analysis and basic modelling (hypothesis tests, regression models, etc.)
CONTENT
1. Session: Fundamentals of Data Analytics with R
Analysis of S&P500 performance
- What is R and R-Studio
- Basic operations in R
- How to import files
- Basic statistical analysis (summary, mean, median, variance¿)
- How to select data
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2. Session: Data Visualization
World Development Indicators at a glance
- Advanced grouping techniques
- Basic visualization tools for exploring one single variable
- Basic visualization tools for exploring relationships between variables
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3. Session: Data correlations and hypothesis testing
Lending Club performance
- Correlation and AUC
- Statistical estimation and error
- Introduction to confidence intervals
- Introduction to hypothesis testing
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Methodology
The course format and methodological approach are based on a combination of explanations and practical parts. During the sessions participants will be provided with the material needed to follow this course. The material includes both the theoretical content of the different subjects to be discussed and the data needed to practice the concepts learned.
Participants will be with provided with real data sets for practices and will work in groups to solve different challenges by applying quantitative methods.
The course is divided in three sessions, each one including practice cases coming from real business situations.
Students may use their laptops/tablets on the lectures/practice sessions ONLY for the course activities. Use emailing, facebooking, tweeting, chatting, skyping, internet surfing, etc. should NOT be done during classes.
Bibliography
- Bishop, C. M. (2006). Pattern recognition. Machine Learning, 128.
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
- Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons.
