Programming and Algorithm II – Instituto Politécnico de Coimbra

Base Knowledge

N/A

Teaching Methodologies

The course unit will be taught through theoretical and practical lessons.

The evaluation of this curricular unit, in the continuous assessment mode, will be done using an individual test, with a weight of 40%. One project, programming work in group, with presentation and defense, with a weight of 60%. The free topic project on Data Science, with max 3 students, will have several phases: proposal, “pitch”, data collection, analysis, processing, answering questions, visualization of results, report and defense with “poster” session (% to be defined).In the evaluation of programs will be taken into account the components of style (modularity, abstraction, readability, comments, etc.) and functionality (correctness and efficiency of the program in all possible test inputs).

Examination (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. 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

João Gilberto de Matos Orvalho

Grading Methods

Exam

- Exam - 100.0%

Continuing Evaluation

- Frequency - 40.0%
- Individual and/or Group Work - 60.0%

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#