Programming for Data Science
Objectives
The Programming for Data Science course unit aims at introducing the basics of programming in Python for Data Scientists. The course is oriented to students that do not have any experience in computer programming, starting from the very basics of computation. However, the course will rapidly evolve towards advanced programming techniques and concepts. In this way, at the end of this course unit, the students will be able to effectively approach complex problems, typically characterized by vast amounts of data, programming efficient strategies to extract information and support decision-making processes.
Classes will involve a mix of lectures and practical exercises. Moreover, the course will have a strong active learning component, as such students are expected to actively participate in the class and read the recommended materials prior to each class. A short introduction to Python will be delivered in the first weeks of the course to enable students to explore and practice many of the theoretical concepts taught in the classes.
Intended Learning Outcomes
- Explain why python is the preferred programming language for Data Scientists;
- Understand the basics of python programming language;
- Use the most adequate libraries for your needs;
- Perform the extraction, manipulation, analysis, modeling, and reporting of data using Python
- Feel Comfortable using Python as a tool for your data science projects!
General characterization
Code
400090
Credits
3.5
Responsible teacher
Flávio Luís Portas Pinheiro
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
None
Bibliography
Lubanovic, Bill. Introducing Python: modern computing in simple packages. " O'Reilly Media, Inc.", 2014;
VanderPlas, Jake. Python data science handbook: essential tools for working with data. " O'Reilly Media, Inc.", 2016.
McKinney, Wes. Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.", 2012.
Grus, Joel. Data science from scratch: first principles with python. " O'Reilly Media, Inc.", 2015
Additionally, students will be able to find a rich online documentation for each of the Libraries covered during the course, and suggested readings will be share in the Moodle page
Teaching method
Theoretical and practical classes
Evaluation method
- Practical exam (40%): consists of an exercise that will need to be solved during the last class. Students will have two hours to develop in their computers the analysis of a data set provided by the instructors, and answer a few analytical questions;
- Final Project (60%): The final project consists of the elaboration of a report that details the process of acquisition, transformation, and analysis of a dataset. The project is to be developed in groups of at least three and up to four elements. More details about the project will be shared during the first couple of weeks in the Moodle page;
Subject matter
|
Week |
Instructor |
Content |
|
1 February 12th |
FLP |
Chapters B1, A0 and C |
|
2 February 19th |
FLP |
Chapters C2 and C6 |
|
3 February 26th |
FLP |
Chapters A5 and C |
|
4 March 12th |
FLP |
Chapters A6 |
|
5 March 19th |
FLP |
Chapters B2, C4, and C14a |
|
6 March 26th |
FLP |
Chapters B3, C5, and C6 |
|
7 April 2nd |
FLP |
Chapters C12 and C10 |
|
8 April 9th |
FLP |
Chapters C13 |
|
9 April 23rd |
JA |
Chapter B9 and C4 |
|
10 April 30th |
JA |
Chapter B9 and C4 |
|
11 May 7th |
JA |
Chapters C14 |
|
12 May 14th |
JA |
|
|
13 May 21st |
JA |
|
|
14 May 28th |
JA |
|
Programs
Programs where the course is taught: