| Sumario: | Master data science, ML, and analytics with powerful visualizations using Matplotlib, Seaborn, and Bokeh. About This Video The art of presenting data in the form of powerful, innovative, and intuitive visualizations In-depth coverage of Matplotlib, Seaborn, and Bokeh visualization libraries Use of data analytics techniques/Exploratory Data Analysis (EDA) using several data generations and manipulation methods In Detail If you are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course takes a holistic approach to teach visualization techniques. We will be taking real-life business scenarios and raw data to go through detailed Exploratory Data Analysis (EDA) techniques to prepare the raw data to suit the appropriate visualization needs. You will learn about data analytics and exploratory data analysis techniques using multiple different data structures with NumPy and Pandas libraries. You will also learn various chart/graph types, customization/configuration, and vectorization techniques. We will look at advanced visualizations using business applications such as single and multiple bar charts, pie charts, and bubble charts with the vectorization of properties. We will further explore Seaborn Boxplot, Violin plot, Categorical Scatterplot, and how to create heat maps. By the end of the course, you will learn the foundational techniques of data analytics and deeper customizations on visualizations. You will be able to confidently use Python visualization libraries such as Matplotlib, Seaborn, and Bokeh in your future projects.\ Audience This course is for Python and machine learning developers, data scientists, data analysts, and business analysts. This course will also be beneficial to leaders, managers, and anyone whose job involves presenting data in the form of visuals, which include developers, architects, and system analysts. A basic understanding of Python will be helpful, but not mandatory.
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