Programming and Algorithm II

Base Knowledge

N/A

Teaching Methodologies

The assessment of this curricular unit, in the form of continuous assessment, is carried out using 1 individual test, weighing 40%: written component, without consultation, 50% and oral component, to explain the algorithm and justify the code developed, 50%.
One project, individual programming work, with presentation and defense, weighing 60%. When evaluating programs, the components of ethics (bibliographical references, comments and others), style (sequence, abstraction, readability, comments, etc.) and functionality (correctness and efficiency of the program in all possible test inputs) will be taken into account. .

Exam (100%).

Learning Results

In this curricular unit the learner is expected to acquire knowledge and develop skills to use programming languages in a structured way. Analyze problems, develop and implement algorithmic solutions. Implement algorithms in Python programming language and others (Javascript, C, etc). Know how to edit, compile and run programs in various environments and platforms.

Program

Data science with Python:

Python basics

Python control structures

Processing data from a file

Dates and times

Processing JSON data

Jupyter Notebbok, Github

Introduction to NumPy

Reading data from a file using Pandas, manipulating data with Panda

Extracting rows and columns

Data Aggregation using Pandas

Joining Pandas Dataframes

Wide and Long Data Formats

Visualizing Data using Matplotlib

Curricular Unit Teachers

Grading Methods

Exam
  • - Exam - 100.0%
Continuing Evaluation
  • - Individual and/or Group Work - 60.0%
  • - Frequency - 40.0%

Internship(s)

NAO

Bibliography

Python Data Science Handbook

https://jakevdp.github.io/PythonDataScienceHandbook/index.html  

 

Python for Computational Science and Engineering

https://github.com/fangohr/introduction-to-python-for-computational-science-and-engineering/blob/master/Readme.md

 

“The Little Book of Algorithms”

http://bit.do/LBOA

 

Beginner’s Guide to Jupyter Notebooks for Data Science

https://morioh.com/p/a41ec25edc0a  

 

What is GitHub?

https://docs.google.com/document/d/1v3IQrC_0pFxsRBXsvCEzKBDAmYjzuSJCvXhkg8ewDn0/edit#

 

Harvard University: cs50.dev