How to Manage Your Data Science Project
4 steps for managing a data science project
--
About the Author
Benjamin O. Tayo is a data science educator, tutor, coach, mentor, and consultant. Contact me for more information about our services and pricing: benjaminobi@gmail.com
Dr. Tayo has written close to 300 articles and tutorials in data science for educating the general public. Support Dr. Tayo’s educational mission using the links below:
PayPal: https://www.paypal.me/BenjaminTayo
CashApp: https://cash.app/$BenjaminTayo
INTRODUCTION
Key takeaways
- Executing a data science project requires good planning
- Good planning and preparation will not only improve productivity, but it will help avoid potential pitfalls and roadblocks that could be encountered during project execution
Benjamin Franklin once said: “By failing to prepare, you are preparing to fail.”
This article will discuss the four steps for managing a data science project: Plan, Prepare, Produce, and Publish.
Plan: Before building any machine learning model, it is important to sit down carefully and plan what you want your model to accomplish. Before delving into writing code, it is important that you understand the problem to be solved, the nature of the dataset, the type of model to build, how the model will be trained, tested, and evaluated.
You may start by providing a brief synopsis followed by a step-by-step plan of what you would like to accomplish. For example, before building a model you may ask yourself:
- What are the predictor variables?
- What is the target variable? Is my target variable discrete or continuous?
- Should I use classification or regression analysis?
- How do I handle missing values in my dataset?
- Should I use normalization or standardization when bringing variables to the same scale?
- Should I use Principal Component Analysis or not?
- How do I tune hyperparameters in my model?