This course is the seventh of eight. In this project, we will tackle a prediction problem: forecasting the number of bicycles that will be rented on a given day. Using historical data, we will consider factors such as weather conditions, the day of the week, and other relevant variables to accurately predict daily bicycle rentals. This will help ensure that our bicycle rental service is prepared with the appropriate number of bicycles each day. We will learn specifically about data acquisition and correlation.
Data Science Project Capstone: Predicting Bicycle Rental
Data Science Project Capstone: Predicting Bicycle Rental
This course is part of Data Science Foundations Specialization
Instructor: Robert Zimmer
Sponsored by ARS SCINet/AI-COE
Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
In this course you will tackle a prediction problem: forecasting the number of bicycles that will be rented on a given day.
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Assessments
10 assignments
Taught in English
Recently updated!
October 2024
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This course is part of the Data Science Foundations Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
Welcome to an exhilarating week in our Data Science journey, where we transition from theory to practice with the commencement of our capstone project. We will immerse ourselves in the intricate world of predictive modeling using linear regression, aiming to forecast bicycle rentals. You will learn specifically about data acquisition and correlation.
What's included
6 videos3 readings5 assignments1 discussion prompt2 ungraded labs
This week, we will complete the Data Science Project. You will learn about single and multiple linear regression and apply these models to solve the bicycle rental problem.
What's included
6 videos1 reading5 assignments4 discussion prompts4 ungraded labs
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