# Foundations of Data Science I

## Overview

• Credit value: 15 credits at Level 4
• Module convenor and tutor: Felix Reidl
• Prerequisite: Introduction to Programming
• Assessment: programming exercises (30%) and a data analysis mini-project (70%)

## Module description

This module covers fundamental aspects of data science and analytics. You will develop the basic mathematical knowledge and skills needed for further studies in the BSc Data Science programme, and needed by data scientists/analysts in general. These include basic elements of linear algebra, preliminaries for calculus, as well as discrete probability theory and fundamentals of statistics.

The module will show you how to use the popular and powerful language Python to solve computational tasks from these mathematical subjects. In particular, this module will get you acquainted with popular Python libraries and packages for programming to solve problems arising from linear algebra, probability theory and statistics.

### Indicative module syllabus

• Taxonomy of data
• Data representation (histograms, box plots)
• Measures of central tendency (mode and the modal class, mean, median)
• Measures of dispersion (range, interquartile range and percentiles, variance and standard deviation)
• Counting and combinatorics (factorial, binomial coefficient)
• Discrete probability (random variables, expectation, variance, and correlation)
• Conditional probability and Bayes’ Rule
• Common discrete distribution families (binomial, geometric, poisson)
• Vector spaces (vector operations, scalar product)
• Matrix algebra (matrix product, linear transformations)
• Metrics
• Tools: Python, Jupyter notebooks, pandas, matplotlib

## Learning objectives

By the end of this module, you will be able to:

• demonstrate satisfactory knowledge of basic linear algebra and matrix theory, basic discrete probability theory and statistics, and relevant Python libraries and packages
• demonstrate satisfactory skills of programming in Python to solve computational tasks from linear algebra and discrete probability theory
• understand the link between the basic knowledge acquired from the module and data science/analytics applications.