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
At the end of this course, you will acquire the necessary skills to program in Python, from basic fundamentals to advanced data handling. You will be able to work with variables, control structures and functions, as well as manipulate large volumes of data using Pandas. You will also master tools for numerical analysis with NumPy and data visualisation with Matplotlib, Plotnine and Plotly, allowing you to tackle programming and data analysis projects with a professional and efficient approach.
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.
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.
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.
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.