- Level Intermediate
- Duration 3 hours
- Course by Coursera Project Network
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Offered by
About
In this project, you'll help a bike rental company enhance its fleet management and pricing strategy by building a daily bike rental forecasting model using time series analysis techniques in R. Your objectives include loading, cleaning, processing, and analyzing daily rental transaction data, and developing and evaluating time series models for the most accurate predictions. The company will use your validated forecasting model to determine the optimal number of bikes to keep in each station and set dynamic pricing based on predicted demand. Upon completion, you'll be able to demonstrate your ability to perform a comprehensive data analysis project that involves answering critical business questions, extensive data visualization, and model selection. There isn't just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers. ROLE: Data Analyst SKILLS: R, RStudio, Data Analysis, Data Modelling, Time Series Modelling, Data Interpretation PREREQUISITES: Load, clean, explore, manipulate, and visualize data using R Write code in RStudio and R Markdown Knowledge of time seriesModules
Part 1: Overview
3
Readings
- Getting Started
- The Project Scenario
- See Example Projects
Part 2: Build Your Project
1
Labs
- Option A: Using Coursera's RStudio Environment
4
Readings
- Choose Your Platform
- Option B: Working Off-Platform
- Feeling stuck? Here are some helpful resources
- Review Your Project Checklist
Part 3: Sharing with Employers
1
Readings
- Learn how Coursera helps you showcase your work to employers
Auto Summary
Enhance your data science skills with the "Forecast Bikeshare Demand using Time Series Models in R" course. Guided by Coursera, you'll assist a bike rental company in optimizing fleet management using time series analysis. This intermediate-level course involves loading, cleaning, and processing rental data to develop accurate forecasting models. Covering R, RStudio, and time series modeling, it spans 180 minutes and is available for free. Ideal for data analysts, it offers practical experience in data visualization and model selection, helping you stand out to employers.

Instructor
Arimoro Olayinka Imisioluwa