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Role of Project Manager in Data Science

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Role of Project Manager in Data Science

A look at how various big data techniques can be leveraged by project managers to track the schedule, budget, etc., of their projects.

· Big Data Zone ·
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Synopsis:

Whether you're a project manager, program manager, or portfolio manager, one of the key responsibilities is to guide project execution as well as managing and communicating changes by balancing project constraints. With emerging trends in data science, it’s always good to know what and how much a project manager needs to learn and adapt.

Role of Project Manager:

Project manager’s role is becoming complex with so many conflicting challenges to deal with. The word cloud below summarizes a few of them:

Project Management Word Cloud

Before we discuss the vision for the future, let us understand the project management stream:

Project Management Process

The role of the project manager is to:

  • Guide project execution.
  • Manage the risk and quality of a project.
  • Deliver based on requirements, on time and on budget.
  • Manage and communicate changes by balancing project constraints.
  • Ensure the business is prepared to adopt the change.

Project Constraints:

Image title

Project Management and Data Science:


"One of the true tests of leadership is the ability to recognize a problem before it becomes an emergency." ~ Arnold Glasow


Let's draw a parallel between project management and data science streams, and reap the benefit from the best of both worlds in achieving value delivery in minimal predictable cycle time with better quality.

Data Science Stream

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Use Cases:

Modeling and algorithms in data science can be, mainly, divided into:

  1. Regression

  2. Classification

  3. OPptimization.

It’s important to understand the basics before we implement these use cases.


Project Manager: Is my project on track?

Data Science: Binary Logistic Regression - A technique to predict the relationship between the predictors (example: completed points, remaining capacity, and other independent variables) and a predicted variable (on track: yes or no).


Project Manager: Let me know the potential deviation from the plan if my project is not on track.

Data Science: Linear Regression - A technique to predict an outcome (number of days beyond they plan) based on a set of predictor variables (example: completed points, remaining capacity, and other independent variables).


Project Manager: Too many risks for me to handle. Can I have a genie that helps me with possible mitigation plans?

Data Science: Decision Tree - A classification technique to map the possible outcomes of series of related choices (in this use case, the impact of the risks).


Project Manager: I have several constraints on cost, resources, time, and scope. Can you suggest how I can meet customer requirements through these constraints?

Data Science: Linear Programming - An optimization technique to achieve the best possible outcome in a mathematical model whose constraints on the independent variables are represented by linear relationships.


Project Manager: My organization has executed many projects in the past and there are several projects in the pipeline. How can I leverage this vast knowledge in delivering successful projects in the future?

Data Science: Machine Learning – A technique to provide the computer with the ability to progressively learn.


Of course, some of the complex scenarios can’t be represented in a linear format and they must be tackled with a mix of tools and techniques.

The project manager doesn’t need to be a data scientist, but, becoming familiar with some big data tools and techniques (some of the references are given below) can help in gaining additional insights:

Summary:

The project manager role can be aided data science by:

  • Guiding project execution.
  • Managing the risk and quality of a project.
  • Delivering based on requirements, on time and on budget.
  • Managing and communicating changes by balancing project constraints.
  • Ensuring the business is prepared to adopt the change.

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Topics:
data science ,artificial inteligence ,project management ,big data ,machine learning

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