Base Knowledge
Although it is not an elimination factor, it is strongly recommended that the student has completed the Programming Curricular Unit (1st semester of the 1st year).
Teaching Methodologies
The teaching activity takes place in person or by videoconference, with exposition of concepts, techniques and methods, with great focus on
the resolution of practical problems. Problem-solving support software will be used.
Learning Results
In this curricular unit students will learn the fundamentals of Python programming necessary for the development of data analysis algorithms
in the areas of accounting and finance. In this curricular unit after a brief introduction to the concepts of algorithmics and programming
students will gain contact with Python’s basic libraries for data analysis such as NumPy, Pandas, MatPlotLib, Scipy, Scikit-learn. In addition
to these libraries they will learn how to manipulate data sets with SQL language. It is intended that students gain a set of skills that allow
them to autonomously perform data analysis projects.
Program
1. Review Python Fundamentals
2. NumPy package
3. Working with data
3.1. Pandas package
3.2. Data set reading
3.3. Filtering, Cleaning, Manipulating Data
4. Data Visualization
4.1. Matplotlib Package
4.2. Understanding motivations between different graphs
5. Machine Learning
5.1. Introduction to Machine Learning
5.2. SciKit Learn package
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
Main Bibliography:
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 with AI, Big Data and
The Cloud. Pearson.Stephen Klosterman (2019) Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn. Packt Publishing.