Introduction to Programming in Python

Aimed at professionals who want to work in data analytics and are interested in adding Python programming to their job skills.

5 Weeks (64h.) | No schedules

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Academic information

Why study this course?

Most widespread languagePython is, according to the TIOBE (The Importance of Being Earnest) index, the most widely used programming language today, making it a key skill in the job market.
Versatility and clarityIt is one of the most versatile, cross-platform and easy-to-use programming languages. IEEE Spectrum defines it as a multi-paradigm, dynamic, multi-purpose language, designed to be fast to learn, use and understand, with a clean and uniform syntax.
Interpreted languagePython is an interpreted language, which means that it does not require compilation, unlike other languages such as Java or C/C++, which facilitates real-time development and execution.
Learning curvePython is easy to read and write due to its high similarity to the human language. Moreover, being cross-platform and open source, it is free and accessible, allowing unrestricted software development.

Program aims

Understand the Python framework and its configuration.
Identify and use the main variables and data types in Python.
To introduce the concepts of object-oriented programming.
Apply functions, loops and control structures to solve problems in Python.
Become familiar with the main Python libraries, such as Pandas, datetime and NumPy, for data analysis.
Learn how to create effective visualisations with tools such as Plotnine, Matplotlib and Plotly.

Methodology

Practical activities from minute 1
Each unit including a questionnaire type test
Al final from course you will solve a case practical guided

Study plan

1. Python basics

This unit is designed to facilitate the learning and understanding of fundamental programming concepts using the Python language. Python is a versatile and easy-to-learn language that has become a popular choice for both beginners and experienced developers due to its clear and readable syntax.

Variables
Types of objects
Control structures
Functions

2. Intermediate Python

This unit covers a variety of key concepts and techniques in data analysis using the Pandas library in Python. From manipulating DataFrames to performing advanced operations such as merging and aggregating datasets, the content covers a broad spectrum of essential skills for any data professional.

Basic Pandas
Pandas intermediate
Advanced Pandas

3. Advanced Python

This unit explores two fundamental tools for data processing in Python: the datetime library and NumPy. The datetime library provides functionality for handling dates and times accurately, allowing arithmetic operations and comparisons between them. NumPy, on the other hand, stands as a pillar of scientific computing, providing support for multidimensional arrays and high-performance mathematical functions.

Datetime in Python
The NumPy library

4. Visualisation in Python

This unit covers various aspects related to the creation and customisation of graphics using tools such as Plotnine, Matplotlib and Plotly. It starts with the installation of key libraries and the loading of data for further manipulation. Different types of charts are explored, from line and bar charts to box, histograms and scatter plots. In addition, methods for adding titles, customising axes and modifying the appearance of graphs are highlighted.

Plotnine
Matplotlib
Plotly

*The academic program may be subject to changes in line with the changing demand for specific skills in the market. Your employability is our goal.