Data Science I

Objectives

The world most valuable resource is data, not oil. Hence, it is not surprising that organizations increasingly look to have in their ranks experts in the sciences of data who are able to identify the correct sources of data, raise relevant business-oriented questions, and extract useful knowledge and insights for the organization. In that sense, the Data Science for Hospitality & Tourism Course unit explores different techniques to perform a descriptive analysis of data using python, as well as an understanding of how data science benefits from Big Data and the different storing technologies.

Classes will involve a mix of lectures and practical exercises. Moreover, the course will have a strong active learning component, as such students are expected to actively participate in the class and read the recommended materials prior to each class. A short introduction to Python will be delivered in the first weeks of the course to enable students to explore and practice many of the theoretical concepts taught in the classes on their own.

Intended Learning Outcomes:

Understand the different nature of data and its sources
Explain why python is the preferred programming language for Data Scientists;
Perform the extraction, exploration, transformation, and analysis, of data using Python;
Perform descriptive analytics that helps you understand your data;
Report your analysis using meaningful visualizations and simple models;
Understand the role of Big Data Technologies in Data Science
Understand the different technologies and architectures for storing data for both Transactional and Analytical operations

General characterization

Code

400112

Credits

7.5

Responsible teacher

Flávio Luís Portas Pinheiro

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

None

Bibliography

[A] VanderPlas, Jake. Python data science handbook: essential tools for working with data. " O'Reilly Media, Inc.", 2016.

[B] McKinney, Wes. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.", 2012.

[C] Grus, Joel. Data science from scratch: first principles with python. " O'Reilly Media, Inc.", 2015

[D] Additionally, students will be able to find a rich online documentation for each of the Libraries covered during the course, and suggested readings will be share in the Moodle page.

Teaching method

Theoretical and practical classes

Evaluation method

To successfully finish this course students need to score a minimum of combined 9.5 points from the following components:

1)Practical Exam (40%): consists of the analysis of a data set provided by the teaching staff, which should be completed within the two hours of one class;

2)Final Project (60%): The final project consists of the elaboration of a report that details the process of acquisition, transformation, and analysis of a dataset. The project is to be developed in groups of up to two elements. More details about the project will be shared during the first couple of weeks in the Moodle page;

Subject matter

Week	Instructor	Content
1	Flávio	Introduction Course Overview The importance of Data Science in organizations Sources of Data and its nature What is Python and Why are we learning it? Setting up Python and Ipython (Anaconda and Jupyter Notebooks) A simple python program ¿Hello World!¿ Chapters C1, A0 and D
2	Flávio	Introduction to Python and Numpy Variables & Data Structures (Arrays, Lists, Dictionaries, Tuples Control flow statements Functions (Def and Lambda) Python Standard Library Chapters A1 and A2
3	Flávio	Introduction to Pandas, Loading data with Pandas, Manipulating information Export data with Pandas Chapters A3, B6, and B7
4	Flávio	Statistics and Probability Describing a single set of data Correlation Simpson¿s Paradox Correlation versus Causation Normal Distribution and the Central Limit Theorem Hypothesis Inference Chapters C5, C6, and C7
5	Flávio	Sources of Data, getting data Working with data Data preparation Exploring n-dimensional data Cleaning, Rescaling, and Dimensionality reduction Chapters B7, C9, and C10
6	Flávio	Multilinear Regressions Clustering K-means algorithm Chapters C12, C15, and C19
7	Flávio	Social Network Analysis What is a network and its characterization Identify the central individuals in a social network Recommender Systems Chapters C21 and C22
8	Francisco	Data Storing with Relational Database Management Systems Operational versus Analytical storing architectures CRUD Operations Chapter C23
9	Francisco	What is Big Data and its implications to Data Science Understand the role of Big Data in data Analytics Distributed systems and parallel programming Chapter C24
10	Francisco	Business Intelligence Reporting your results Chapter B9 and C4
11	Francisco	Example of a Data Science project Discussion of the benefits of Data Science for the support of decision making
12	Francisco	Final Project discussion Brainstorming ideas and possible data sources Formation of Groups
13	Francisco	Final Project support Bring your questions and discuss with the intstructor
14	Francisco	Project Report Delivery Oral Presentations

Programs

Programs where the course is taught: