Data Science Project Planning

Fundamental Concepts for Beginners

Success of any project depends highly on how well it has been planned. Data science projects are no exception.

What you’ll learn

  • Fundamental concepts underlying core planning activities that are critical for a data science project’s success..
  • PLEASE NOTE: This course will not cover technical topics like programming , statistics and algorithms..

Course Content

  • Introduction –> 5 lectures • 12min.
  • Business Problem Definition –> 7 lectures • 35min.
  • Data Science Problem Formulation –> 24 lectures • 2hr 20min.
  • Situation Assessment –> 8 lectures • 42min.
  • Project Scheduling –> 3 lectures • 23min.
  • Emerging Methods –> 4 lectures • 26min.
  • Conclusion –> 5 lectures • 16min.

Data Science Project Planning


  • Willingness to look beyond the technical aspects and learn about the crucial planning activities involved in a data science project..
  • Familiarity with high school level mathematics.

Success of any project depends highly on how well it has been planned. Data science projects are no exception.

Large number of data science projects in industrial settings fail to meet the expectations due to lack of proper planning at their inception stage.

This course will provide a overview of core planning activities that are critical to the success of any data science project.

We will discuss the concepts underlying  – Business Problem Definition; Data Science Problem Definition; Situation Assessment; Scheduling Tasks and Deliveries.

The concepts learned will help the students in:

A) Framing the business problem

B) Getting buy-in from the stakeholders

C) Identifying appropriate data science solution that can solve the business problem

D) Defining success criteria and metrics to evaluate the key project deliverables  viz;  models, data flow pipeline and documentation.

E) Assessing the prevailing situation impacting the project. For e.g. availability of data and resources; risks; estimated costs and perceived benefits.

F) Preparing delivery schedules that enable early and continuously incremental valuable actionable insights to the customers

G) Understanding the desired team attributes and communication needs



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