Class Introduction to Data Science

The Introduction to Data Science course aims to provide the student with essential skills in data analysis, in a multidisciplinary perspective, as is Data Science. Through the presentation of methodologies and fundamental techniques to treat, transform, construct and analyze data, the objective of this curricular unit is to give the student the ability to translate this analysis into knowledge and value in a sustainable way for decision making. The practical component is one of the fundamental aspects of the discipline, so the ability to translate knowledge into practical actions and analysis decisions is particularly valued. The close connection with the business world to answer business questions will be illustraded in this course.

Not applicable

1. Introduction to Data Science: Importance and applications of Data Science Project Workflow: Practical Examples Data types: Structured, Semi-structured, and unstructured Challenges in Data Science   2. Python for Data Science Setup: Jupyter notebook NumPy Pandas ydata-profiling 3. Data pre-processing Cleaning and preparing structured data (data wrangling: slicing, grouby, pivoting, missing values, imputation, duplicates, outliers, etc.) Unstructured data processing - Text (lemmatization, stemming, etc.) 4. Introduction to Machine Learning, supervised and unsupervised models Basic concepts Linear Regression Logistic Regression Dimensionality Reduction (PCA)   5. Micro-Services and APIs Concepts API Definition APIs Design Implementing APIs in Python Operating APIs in a prediction process 

Theoretical concepts are introduced in class, and then they are complemented with real-world examples. For each topic, the students are given a set of exercises that aim to apply the theoretical concepts. Exercises are discussed and solved in class, students are invited to share any doubts they might have.   Support materials and exercises with resolution suggestions will be available on Moodle. It is believed that continuous assessment, adapted according to the evolution of students, is a good practice. Individual monitoring and availability to clarify doubts, whenever necessary, is essential for the student and his/her performance.

Grus, J. (2019). Dafa science from scratch: first princples with Python . O'ReiIIy Media.