Introduction to Data Science
This is an expository subject that aims to present introductory concepts in Data Science, elucidate the meanings of the main concepts in the area and motivate students to take courses in Mathematics, Statistics and Computing that make up the Undergraduate Data Science course. Big Data and Data Driven Economy. Data Science and Organizations. Data-based Strategic Intelligence. Statistical Learning and Machine Learning. Artificial intelligence. Programming Languages, Algorithms and Computational Platforms. Distributed and Cloud Computing. Data Modeling. Database. Applications of Data Science techniques in several areas such as Epidemiology, Finance, Economics, etc.
Basic Information
Workload
60 hours
Requirements
Does not exist
Mandatory:
- Tukey, J. Exploratory Data Analysis. Pearson. 1977
- Wes McKinney. Python Data Analysis, O'Reilly. 2017
- Cole Nussbaumer Knaflic. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley, 2015
Complementary:
- Philipp K. Janert. Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists. O'Reilly, 2011.
- Osvaldo Martin. Bayesian Analysis with Python. Packt. 2016
- Shai Vaingast. Beginning Python Visualization: Crafting Visual Transformation Scripts. Apress. 2014. Petrou, Theodore. Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization
- Rossant, Cyrille. IPython Interactive Computing and Visualization Cookbook