Computation and Visualization for Analytics Part 2

Northeastern University via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

2 days 2 hours 43 minutes
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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Offers students an opportunity to learn how to use visualization tools and techniques for data exploration, knowledge discovery, data storytelling, and decision making in engineering, healthcare operations, manufacturing, and related applications. Covers basics of Python and R for data mining and visualization. Introduces students to static and interactive visualization charts and techniques that reveal information, patterns, interactions, and comparisons by focusing on details such as color encoding, shape selection, spatial layout, and annotation.

Syllabus

Module 8: Data Visualization with Matplotlib - Part 3

Building on the fundamentals of Matplotlib, this module will equip you with the skills to master various types of plots, from line plots with logarithmic scaling to customized bar plots, pie charts, scatter plots, and histograms. Through a series of practical exercises and hands-on examples, you'll gain proficiency in creating and customizing plots to enhance their visual appeal and clarity. By the end of this module, you'll be equipped with the skills and knowledge to effectively communicate data insights using Matplotlib, transforming raw data into meaningful visualizations that drive informed decision-making. Let's dive in and enhance your data visualization proficiency!

Module 9: Data Visualization with Matplotlib - Part 4

This module advances your data visualization proficiency by introducing the creation of complex plots with Matplotlib. You will explore box plots for summarizing numerical data, violin plots for detailed distribution insights, stack plots for illustrating cumulative contributions, and heatmaps for visualizing correlations. Through hands-on demonstrations, you will refine your Matplotlib skills, gaining expertise in creating and customizing complex plots. By the end of this module, you will know more about how to make detailed plots for effective data communication and analysis. Get ready to elevate your data storytelling with advanced and complex visualization techniques in Matplotlib.

Module 10: Seaborn Library - Part 1

In this module, we will explore the creation and customization of data visualizations using Seaborn, a powerful Python library. Seaborn's simplicity and aesthetic appeal make it easy to create visually compelling and informative plots that enhance data understanding and decision-making. Through hands-on exercises, you will learn to transform data into insightful visualizations using histograms, bar charts, and line plots. You will also discover how to decode patterns in time-series data using various visualizations. By mastering Seaborn, you will gain valuable technical skills in creating impactful visualizations, facilitating data interpretation, and improving decision-making. Get ready to elevate your data visualization skills!

Module 11: Data Visualization with Seaborn Library - Part 2

In this module, we will advance our exploration of data visualization, focusing on Seaborn, a powerful Python library. This module will refine your skills in creating impactful visualizations using Seaborn, emphasizing heatmaps, scatter plots, joint plots, box plots, and violin plots. Through hands-on demonstrations, we will uncover intricate patterns and relationships within the data. As we progress, you will gain a deeper understanding of Seaborn's capabilities and learn how to leverage its features for more sophisticated and insightful visualizations. Get ready to elevate your data visualization proficiency to a more advanced level!

Module 12: Interactive Data Visualization with Plotly Library – Part 1

In this module, we will transition into the interactive realm of data visualization, building upon your proficiency in creating static visualizations with Matplotlib and Seaborn. Now, we will introduce Plotly, an advanced tool that elevates the interactive aspect of data representation. In this module, we will explore a variety of interactive visualizations, ranging from histograms and insightful line plots to engaging pie charts and dynamic scatter plots. Additionally, we will dive into advanced interactive visualizations such as violin plots and heatmaps. Through hands-on demonstrations and interactive exercises, this module is designed to immerse you in the practical application of Plotly. Get ready to elevate your storytelling skills and transform data into interactive visualizations.

Module 13: Interactive Data Visualization with Plotly Library – Part 2

This module expands your data visualization skills by exploring advanced capabilities within Plotly. Throughout the module, we will delve into the intricacies of Linear Regression with Plotly, showing how to transform data into comprehensive regression plots. Through hands-on demonstrations, we will guide you in creating dynamic choropleth maps with Plotly, enabling you to explore animated trends and reveal distinct regional patterns within the data. Additionally, we will elevate your visualizations by integrating images and launching Plotly Dash apps, providing an interactive environment for exploration. Get ready to enhance your interactive data visualization skills using Plotly.

Module 14: Network Graph

In this module, we will learn the concepts of Network Science. We will think about why we need network science to address and visualize academic research and real-life phenomena and how network science can be used. First, we will learn terms such as Vertices (Nodes), edges (Connections), Rank, Degree, and Hub. With the visualizations in the Lab, we learn how to plot graphs with some customizations. Then, we also learn how to interpret those visualizations.