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Forecasting is one of the most useful techniques a data scientist can bring to an organization. By making predictions from complex data, they can guide policy and resource management and allow businesses to plan and prepare for the future.
In this liveProject, you’ll take on the role of a data scientist who’s been tasked with forecasting the future consumption of an energy company’s customers. Energy consumption is seasonal and complex, and so you’ll be using the powerful Python ecosystem to tame your data and make your predictions. Your challenge is to use regression and time series techniques to forecast and visualize hourly energy usage for millions of customers, and present your findings in a clear report to allow your bosses to oversee resource allocation.
In this liveProject, you’ll take on the role of a data scientist who’s been tasked with forecasting the future consumption of an energy company’s customers. Energy consumption is seasonal and complex, and so you’ll be using the powerful Python ecosystem to tame your data and make your predictions. Your challenge is to use regression and time series techniques to forecast and visualize hourly energy usage for millions of customers, and present your findings in a clear report to allow your bosses to oversee resource allocation.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
book resources
When you start your liveProject, you get full access to the following books for 90 days.
prerequisites
This liveProject is for intermediate Python programmers who know the basics of time series and forecasting techniques. To begin with this liveProject, you will need to be familiar with:TOOLS
- Basics of pandas
- Basics of matplotlib
- Basics of statsmodels
- Basics of linear regression
- Basics of time series
you will learn
In this liveProject, you’ll master the lucrative skill of forecasting. This liveProject is focused on forecasting the energy industry, and the skills you develop can easily be transferred to finance, consumer product demand, and more.- Visualizing and exploring the time series characteristics of hourly energy data
- Exploring relationships between temperature and energy usage across different times of year
- Building a linear regression model to forecast energy usage
- Expand the linear regression model to become more dynamic
- Compare the dynamic time series model using ARIMA with an exponential smoothing model
- Incorporate holiday effects into the time series forecast
features
- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
