2024
Data Science: Stages and Data Visualization
Name: Data Science: Stages and Data Visualization
Code: INF15583O
1 ECTS
Duration: 2 weeks/26 hours
Scientific Area:
Informatics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Regime de Frequência: E-learning
Sustainable Development Goals
Learning Goals
This course provides an overview of the essential stages in a Data Science project, as well as in the development of data visualizations.
By the end of this course, students should be able to:
1. Explain the main stages and common challenges in Data Science projects.
3. Apply (Python) libraries for the cleaning, transformation, and efficient analysis of real-world data.
3. Recognize best practices in data visualization.
4. Develop effective charts and visual representations.
5. Formulate and answer exploratory questions based on data visualizations.
By the end of this course, students should be able to:
1. Explain the main stages and common challenges in Data Science projects.
3. Apply (Python) libraries for the cleaning, transformation, and efficient analysis of real-world data.
3. Recognize best practices in data visualization.
4. Develop effective charts and visual representations.
5. Formulate and answer exploratory questions based on data visualizations.
Contents
Presentation of the main phases of the Data Science process: data collection, preparation, modeling, analysis, and communication of results.
Identification of the types of questions to be formulated in Data Science projects.
Introduction to data preprocessing: detecting and handling missing values, outliers, and inconsistencies.
Practical application of Python libraries (Pandas) for the transformation and manipulation of real-world data.
Performing Exploratory Data Analysis (EDA): techniques for identifying patterns and trends.
Distinguishing between correlation and causation in datasets.
Best practices in visualization: selecting appropriate chart types with a focus on clarity and simplicity.
Implementation of effective charts and visual representations using libraries such as Matplotlib.
Analysis and presentation of results based on an exploratory question formulated during the project.
Identification of the types of questions to be formulated in Data Science projects.
Introduction to data preprocessing: detecting and handling missing values, outliers, and inconsistencies.
Practical application of Python libraries (Pandas) for the transformation and manipulation of real-world data.
Performing Exploratory Data Analysis (EDA): techniques for identifying patterns and trends.
Distinguishing between correlation and causation in datasets.
Best practices in visualization: selecting appropriate chart types with a focus on clarity and simplicity.
Implementation of effective charts and visual representations using libraries such as Matplotlib.
Analysis and presentation of results based on an exploratory question formulated during the project.
Teaching Methods
The teaching methodology of this course is fully online, focusing on asynchronous and interactive activities, complemented by synchronous sessions for technical support and Q&A.
The asynchronous theoretical-practical classes consist of short videos introducing the syllabus content, followed by practical activities with interactive exercises that allow students to apply the concepts covered.
Synchronous sessions are offered for technical support, Q&A, and guidance on the final project, providing personalized assistance to students.
Assessment is carried out through a mini-test to verify understanding of theoretical content and a practical project in which students must formulate an exploratory question and answer it using data visualizations, applying the techniques learned throughout the course.
The asynchronous theoretical-practical classes consist of short videos introducing the syllabus content, followed by practical activities with interactive exercises that allow students to apply the concepts covered.
Synchronous sessions are offered for technical support, Q&A, and guidance on the final project, providing personalized assistance to students.
Assessment is carried out through a mini-test to verify understanding of theoretical content and a practical project in which students must formulate an exploratory question and answer it using data visualizations, applying the techniques learned throughout the course.
Assessment
The assessment of this course consists of two components:
- Mini-test (50%), conducted individually;
- Project (50%), completed either individually or in groups.
- Mini-test (50%), conducted individually;
- Project (50%), completed either individually or in groups.
Teaching Staff
- João Pedro Lebre Magalhães Pereira [responsible]
- Mário Jorge Costa Mourão
Certificações
