Learn about the data science lifecycle;
* Apply Python as a programming language to perform data analysis tasks;
* Become acquainted with the data manipulation process and how to achieve this in Python;
* Get introduced to basic machine learning algorithms and their applications, network science techniques for modeling, analyzing and reasoning about relationships between entities
* Understand and apply data interpretation and visualization tools

Data science is an interdisciplinary field concerning scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. This course is framed around the data science lifecycle to show the techniques for handling a data science project. The data science life cycle is divided in seven steps; 1) defining relevant objectives and data science research questions, 2-3) data acquisition and preparation for investigation (scraping, wrangling, cleaning, handling erroneous or missing values and sampling) to guarantee high quality and quick and reliable access, 4) exploratory data analysis to generate hypotheses, 5) feature engineering to select important and meaningful data features, 6) predictive modelling based on machine learning algorithms and 7) correct communication of the analysis outcomes through visualization, storytelling and reporting.

BENC1002 Calculus
BENC1004 Linear Algebra