Data science is gaining momentum in various applications becoming one of the most exciting fields used in high-tech industries. It is an interdisciplinary field that uses scientific methods algorithms and visualization methods to extract knowledge and insights from data. As a result a growing number of companies are looking for data scientists data engineers and machine learning experts to develop products features and projects that will help them unleash the power of data in practical data science projects.Sometimes a picture is worth a thousand words or one might say ‘a chart is worth a thousand data points’. When handling and processing data the first and most important step is to know your data in detail. Therefore it is essential to apply an Exploratory Data Analysis (EDA) method across the available datasets by summarising their main characteristics using statistical and data visualization methods. Using this approach we will be able to understand the features in the dataset. Check the fill-rate of relevant features for testing data quality and understand each feature's scale and unit to decide if any normalisation or transformation actions will be needed. We will also test how they are correlated to each other to better select the most relevant features and identify data patterns that will be useful for the subsequent steps.In Python there are a variety of data science libraries being used for performing data visualization. This training will focus on two prevalent libraries called ‘Matplotlib’ and ‘Seaborn’. Using those libraries we will be able to create various visualization charts including bar charts pie charts tree-map charts line and area charts histograms density curve charts and many more. We will learn to select the most relevant chart for the needed job and adjust each chart's parameters to get a pixel-perfect result for our project's needs. Enrol today and start your journey to become a data scientist! This course has not been updated with the use of Generative AI models like ChatGPT.