Python for Data Science (2235.YR.007980.1)

General information

Type:	OBL	Curs:	1	Period:	S semester
ECTS Credits:	null ECTS

Teaching Staff:

- Basic computational skills

Lectures and in-class exercises will represent 50% of the workload. The assignments will represent the other 50% of the workload.

Python has emerged over the last couple decades as a first-class tool for data science and scientific computing tasks, including the analysis and visualization of large datasets. Besides, in the modern economy, data is an invaluable resource in any business. However, if you cannot make sense of it, your business data will be useless. To better understand how businesses can take advantage of their data, the main goal of this course is to improve your computational mindset by introducing in a practical way how Python works.

At the end of the course, students should:
1. Be familiar with Python types
2. Code basic python routines for solving data science tasks
3. Use algorithmic reasoning to solve simple data science problems

1. Introduction to Anaconda and basic types

2. Lists, dicts and sets

3. Conditionals and functions

4. Loops

5. Dealing with errors

To achieve the objectives, the 5-day course will be based on lectures, class discussions and practice.

Lecture/Discussion. During the lessons, we will introduce the basic concepts for each topic. These sessions will be devoted to the presentation and discussion of frameworks, concepts, and theories.

Practice. In Practice sessions, we will present the general framework and the background tools. Different practical exercises will then be delivered and discussed in class. For this purpose, we will be using Anaconda Jupyter notebooks.

ASSESSMENT BREAKDOWN

1. Wes McKinney, Python for Data Analysis, O'Reilly Media, 2017.
2. https://runestone.academy/runestone/books/published/thinkcspy/index.html