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Programming for Data Science

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Base Knowledge

None.

Teaching Methodologies

The teaching activity takes place in person, with the presentation of concepts, techniques and methods, with a strong focus on solving practical problems.

Software to support problem solving will be used.

Learning Results

The curricular unit of Programming for Data Science is intended for students with no previous programming experience. 

It is a structuring unit of the master’s course in data analysis and decision support systems since students will learn the fundamentals of Python programming needed to perform programming activities in other curricular units and develop Data Science projects.

With this curricular unit the students are expected to achieve the following objectives:

  • Design simple algorithms by applying appropriate programming methodologies.
  • Know and understand the Python language.
  • Know some of the fundamental Python libraries for conducting projects in Data Science, such as NumPy, Pandas, MatPlotLib, Scipy and Scikit-learn.
  • Know basic SQL language commands for manipulating datasets
  • Understand and adapt existing programs and develop new programs coded in Python language.

In terms of competences, the students are expected to be able to

  • Use the Python language for the implementation of algorithms
  • Use the Python language for data manipulation
  • Use the Python language for the visualization of results
  • Use the Python language to implement Data Science projects

Program

  1. Introduction to PYthon
    1. Basics of Python
    2. Python Data Structures
    3. Python Programming Basics
  2. Working with data in Python
  3. Python data visualization
  4. Introduction to learning machines in Python
  5. Other Python applications

Curricular Unit Teachers

António Rui Trigo Ribeiro

Internship(s)

NAO

Bibliography

Principal:

  • Antonio Trigo. (2018, June 12). PyTrigo – Introdução à Data Science com Python (Version v0.12). Zenodo.http://doi.org/10.5281/zenodo.1288006

Complementar:

  • Jake VanderPlas (2016). Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media.
  • Wes McKinney (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O’Reilly Media.
  • Joel Grus (2019). Data Science from Scratch: First Principles with Python. O’Reilly Media.
  • Paul Deitel and Harvey Deitel (2019). Intro to Python for Computer Science and Data Science: Learning to Program withAI, Big Data and The Cloud. Pearson.
  • Stephen Klosterman (2019) Data Science Projects with Python: A case study approach to successful data scienceprojects using Python, pandas, and scikit-learn. Packt Publishing