Data Science I

Objectives

None

General characterization

Code

400112

Credits

7.5

Responsible teacher

Docente a designar

Hours

Weekly - Available soon

Total - Available soon

Teaching language

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

Prerequisites

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

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

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;

Evaluation method

English

Subject matter

Theoretical and practical classes

Programs

Programs where the course is taught: