Using Graph Networks to Recommend Books to Customers

prerequisites
basics of Python • basics of pandas • basics of Jupyter Notebook
skills learned
data manipulation and analysis with pandas • building networks with Python • visualizing and analyzing graph networks
William Davies
4 weeks · 4-6 hours per week · BEGINNER

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In this liveProject, you’ll jump into the role of a data scientist working for an online bookstore. Your boss wants you to build a new recommendation system to help the marketing team match customers with a book that suits their interests. As the backbone of this new system, you’ve decided to create a graph network that will plot and analyze the relationship data of your platform’s users. To do this, you’ll need to import your data into pandas and transform it into an edge list, then build a network from the list. Once that’s accomplished, you’ll visualize and analyze the list to check its accuracy before you present your results as a web application that’s easy for the non-technical marketing team to use.

For this liveProject, you’ll be provided with real customer data from manning.com!
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

project author

William Davies
Dr. William Davies is a Postdoctoral research fellow working at the center for pluralist economics. His Ph.D. was focused on utilizing machine data and traditional econometrics techniques to capture the drivers of success for crowdfunding, focusing specifically on the crowdfunding platforms Kickstarter and Kiva. After completing his Ph.D. he has begun to work as Postdoctoral research, where he is responsible for identifying data techniques and tools to assist 50 small to medium-size enterprises as part of an EU-wide initiative. He has also successfully published in the journal of technology forecasting and social change (impact factor 5.846, 2019) in a paper entitled "Signalling experience & reciprocity to temper asymmetric information in crowdfunding evidence from 10,000 projects" (https://www.sciencedirect.com/science/article/pii/S0040162516303900".

prerequisites

This project is designed (using multiple help levels) to be accessible to those of a wide skill range, the following pre-requisites will enable smoother usage of the course. Having said this the course should be doable for all skills levels.

TOOLS
  • Basics of Python
  • Basics of pandas
  • Basics of Jupyter notebook
TECHNIQUES
  • Ability to read and learn from Python documentation
  • Ability to install modules in Python
  • Utilizing virtual environments

you will learn

In this liveProject you’ll learn to create a graph network that’s perfect for data with rich relationships. The skills for interpreting complex platforms and connection data are easily transferable to business, eCommerce, and scientific analysis.

  • Uploading data into pandas DataFrames and rearranging the data
  • pandas data manipulation and analysis
  • Building networks with Python
  • Visualizing and analyzing graph networks
  • Presenting results to potential clients

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.
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