Python for Data Sciences (2235.YR.007418.1)

General information

Type:	OPT	Curs:	1	Period:	S semester
ECTS Credits:	2 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 1	Albert Armisen Morell	Operaciones, Innovación y Data Sciences	ENG

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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.

The contents of this course are divided into two different parts. On the one hand, there will be in class theory and discussions focused on providing an overview of several Python aspects such as data types or functions. On the other hand, there will be hands-on practice sessions, where we will introduce how to implement basic data sciences tasks in Python.

At the end of the course, students should:
- Be familiar with Python types
- Code basic python routines for solving data science tasks
- Use algorithmic reasoning to solve simple data science problems
By the end of the course, students will be equipped with a toolbox to handle basic data science task and should be confident to interact with data analytics related positions.

1. Introduction, Basic and Advanced Data Types and Functions - Meet your instructors - Content Description & Evaluation - Classroom Rules - Workspace - Tips & Tricks - Basic Data Types - Advanced Data Types

2. Conditionals, Loops and Numpy - Conditionals - Loops - Numpy - Numpy and its Operations

3. Data Cleaning with Pandas - Pandas Data Types - Descriptive Statistics - Filtering - Group By & Apply - Combine Dataframes

4. Data Wrangling with Pandas and Scikit-Learn - Data Visualization - Outlier Removal - Missing Values Detection - Imputation - Categorical and Ordinal Data - Standarization

5. Machine learning with Scikit-learn - Machine Creation - Machine Evaluation - Train/Test Split

To achieve the objectives, the 5-day course will be based on lectures, class discussions and practice. Lectures and in-class exercises will represent 50% of the workload.The assignments will represent the other 50% of the workload.

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 Google Colab notebooks.

- Lutz, Mark. Learning python: Powerful object-oriented programming. " O'Reilly Media, Inc.", 2013.
- Wes McKinney, Python for Data Analysis, O'Reilly Media, 2017.

Group	Teacher	Department
Year 1	Albert Armisen Morell	Operaciones, Innovación y Data Sciences