Class Introduction to Data Science

The course unit Introduction to Data Science is part of the Master's in Teaching Informatics, within the scientific area coded AD, taught in the 1st semester. It aims to equip future Informatics teachers with fundamental data-science competences, from understanding the nature of data to Python programming, data pre-processing, exploratory analysis and machine learning. It also covers API concepts and microservice design, including implementing APIs with machine learning in Python. Taught in the 1st semester, it gives students a solid technical foundation in data science, relevant to teaching Informatics and to integrating data and artificial-intelligence content throughout the study cycle.

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CP1 - Introduction to Data Science: importance and applications; project workflow; data types (structured, semi-structured and unstructured); key challenges. CP2 - Python for Data Science: Jupyter Notebook setup; NumPy; Pandas; ydata-profiling. CP3 - Data pre-processing: cleaning and preparing structured data; processing unstructured data (text). CP4 - Introduction to Machine Learning, supervised and unsupervised models: basic concepts; linear regression; logistic regression; dimensionality reduction (PCA). CP5 - Microservices and API fundamentals: defining, designing and implementing APIs in Python; operating APIs in a prediction context.

ME1 - Interactive lectures with debates and case studies (LO1). ME2 - Interactive activities with infographics and diagrams to explore the nature of data (LO2). ME3 - Programming labs with practical Python exercises (LO3). ME4 - Workshops and case studies for data preparation and pre-processing (LO4). ME5 - Guided projects, debates and presentations on exploratory data analysis (LO5). ME6 - Problem-based learning, with real challenges on the data scientist's workflow (LO6). ME7 - Controlled experiments and debates on machine-learning methods (LO7). ME8 - Seminars, debates and practical exercises on model evaluation metrics (LO8). ME9 - Practical examples and collaborative activity on APIs and microservices (LO9). ME10 - Live coding and peer review implementing APIs with ML (LO10).

Bruce, Bruce & Gedeck (2020). Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. O'Reilly Media. Foreman, Jennings & Miller (2014). Data Smart: Using Data Science to Transform Information into Insight. Wiley. Grus (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Knaflic (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. John Wiley & Sons. Provost & Fawcett (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly Media. Russell & Norvig (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson. Spiegelhalter (2019). The Art of Statistics: Learning from Data. Penguin UK. Theobald (2019). Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy.