In this series of liveProjects, you’re a data analyst hired to visualize data for two different companies. In the first two liveProjects, you’ll help ABC Bikes Inc. make informed decisions about its dynamic bike rental service by providing important insights about bike rental trends. Using Matplotlib, you’ll create 2D and 3D plots to determine the busiest hours for rental bikes and how weather, time of day, and month of year impact rentals.
In the third liveProject, you’ll gather insights for the Global Consensus Bureau company to analyze data from a survey conducted by the U.S. Census Bureau to determine how factors like education, hours worked per week, gender, and age relate to citizens’ incomes. Using Python’s seaborn library, which provides functions for customizing visual elements, you’ll create visually appealing charts that identify the statistical relationships you’re interested in. When you’ve completed this series, you’ll have the experience and knowledge to use Python’s Matplotlib and seaborn libraries to create attractive visualizations that provide useful insights.
I think that for people that need to do quick visualization and analytics of data, this series will be a useful training tool.
ABC Bikes Inc. is considering launching a bike rental service in your area. As the company’s data analyst, your task is to provide decision-driving insights on bike rental trends. You’ll extract the relevant data from a publicly available dataset into a pandas data frame. Then, using Python plotting library Matplotlib, you’ll create line plots to visualize changes for bike rentals on a single day as well as over a specific time period, create a grouped bar plot to visualize a comparison of renting data for two years, create subplots with different chart types, and create a violin plot to visualize renting patterns over four seasons. When you’re done, you’ll know how to use Matplotlib to create accurate and informative 2D visualizations.
ABC Bikes Inc. is considering expanding its bike rental service to a new county. Your job, as the company’s data analyst, is to determine factors that impact the demand for rental bikes in that area. You’ll extract the relevant data from a publicly available dataset into a pandas data frame. Then, using Matplotlib’s mplot3d toolkit, you’ll plot 3D graphs to simultaneously visualize more than two data features, enabling you to determine useful patterns including how temperature, time of day, and month of year impact bike rentals. When you’re finished, you’ll know how to use Matplotlib to create, customize, and rotate your 3D plots to gather useful and interesting insights.
You’re a data analyst at Global Consensus Bureau, and your manager has asked you to determine how factors like education, hours worked per week, gender, and age relate to citizens’ incomes. Using pandas, you’ll pre-process data from a survey conducted by the U.S. Census Bureau. Then, you’ll create different charts to identify statistical relationships in data, using Python’s seaborn library, which provides custom functions for visual elements. When you’re done, you’ll know how to use seaborn to create visually appealing charts that reveal accurate and useful insights.
The instructor put finished diagrams to give us an idea of what we were supposed to generate. This was very helpful.
I can definitely apply what I have learned as I need to create some graphs for usage of our archive.
These liveProjects are for early career data analysts. To begin these liveProjects you’ll need to be familiar with the following:TOOLS
In this liveProject series, you’ll create useful visualizations of interesting insights from data using Matplotlib and seaborn Python libraries.
geekle is based on a wordle clone.