In-Person
Online

Managing AI and Data Science Projects

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Length
1 day
9 a.m. to 5 p.m.

    Contact us to find out upcoming dates executiveeducation@hec.ca

  • Downtown

  • 100% online

  • Downtown

  • Migration de donnée

Rate
$1,195
$1,195
Language

French

Formations sur mesure pour les organisations

Program can be

Customized for

your organization
Formations internationales

Delivered anywhere

in the world
Contact an advisor
Nadia Uria-Fernandez
Nadia Uria-Fernandez
Program Manager

Email | Phone

Discover the issues related to artificial intelligence (AI) projects and the major differences with the traditional management of IT and software development projects.

Implementing AI and data science (in production, Proof-of-Concept or MVP mode) projects is an intricate process fraught with uncertainty. In general, the quality of the data, the technological complexity of AI implementation, the level of maturity of organizations and the access to resources can make the development and execution of AI solutions especially difficult.

This program will present the basics of project management, focusing on data mining and the use of AI technologies. Inspired by the agile-hybrid methodology and the instructor’s experience in the Montreal and international AI industry, the content is aimed at accelerating your learning curve and providing you with a guide to implementing AI projects within your organization.

GOALS

This program will help give you the tools to:

  • Apply project management and product ownership techniques to your data mining or AI projects.
  • Gain an in-depth understanding of the roles, responsibilities, steps and results of AI projects.
  • Understand how to apply agile and hybrid principles, and have a better grasp of their advantages and potential challenges within the scope of an AI project.
  • Discover the key factors to the successful implementation of your AI projects.

IS THIS FOR YOU?

This program is intended primarily for (but is not limited to):

  • Project managers and product owners
  • Executives and managers wishing to learn more about AI and data science project management
  • Visionary entrepreneurs seeking to incorporate AI into their organization
  • Technical team members interested in the tasks involved in AI and data science project management.

A technical background (e.g., engineering, computer science or data science, among others) is an asset but not a prerequisite.

SPECIAL FEATURES

By the end of this course, you will have an in-depth understanding of AI and data science project management best practices. The practical approach focused on the realities of today’s business environment will enable you to apply the course content to your field.

Participants will receive a certificate of completion from Executive Education HEC Montréal.

This multifaceted program is built around expertise in a wide range of topics, including:

  • Agile project management and the role of the product owner
  • Innovative solution design
  • Data science and machine learning
  • Collaboration between technical and science teams
  • Plain-language results and expectations management
  • Lessons learned from past projects
  • Insights into the AI project lifecycle.

TRAINING APPROACH

  • Presentation of theoretical concepts.
  • Numerous case studies.
  • Group discussions.
  • Additional reading.
Adrian Gonzalez Sanchez

MSc (mobile communications), MBA

Telecom & IT Engineer, Microsoft

Delphine Le Serre

PhD (Human and Social Sciences), Engineering degree in microelectronics and nanotechnology

Founder and President, EdHu2050

In-Person
Online

Managing AI and Data Science Projects

Discover the issues related to artificial intelligence (AI) projects and the major differences with the traditional management of IT and software development projects.

Implementing AI and data science (in production, Proof-of-Concept or MVP mode) projects is an intricate process fraught with uncertainty. In general, the quality of the data, the technological complexity of AI implementation, the level of maturity of organizations and the access to resources can make the development and execution of AI solutions especially difficult.

This program will present the basics of project management, focusing on data mining and the use of AI technologies. Inspired by the agile-hybrid methodology and the instructor’s experience in the Montreal and international AI industry, the content is aimed at accelerating your learning curve and providing you with a guide to implementing AI projects within your organization.

Presentation Program Instructors

GOALS

This program will help give you the tools to:

  • Apply project management and product ownership techniques to your data mining or AI projects.
  • Gain an in-depth understanding of the roles, responsibilities, steps and results of AI projects.
  • Understand how to apply agile and hybrid principles, and have a better grasp of their advantages and potential challenges within the scope of an AI project.
  • Discover the key factors to the successful implementation of your AI projects.

IS THIS FOR YOU?

This program is intended primarily for (but is not limited to):

  • Project managers and product owners
  • Executives and managers wishing to learn more about AI and data science project management
  • Visionary entrepreneurs seeking to incorporate AI into their organization
  • Technical team members interested in the tasks involved in AI and data science project management.

A technical background (e.g., engineering, computer science or data science, among others) is an asset but not a prerequisite.

SPECIAL FEATURES

By the end of this course, you will have an in-depth understanding of AI and data science project management best practices. The practical approach focused on the realities of today’s business environment will enable you to apply the course content to your field.

Participants will receive a certificate of completion from Executive Education HEC Montréal.

This multifaceted program is built around expertise in a wide range of topics, including:

  • Agile project management and the role of the product owner
  • Innovative solution design
  • Data science and machine learning
  • Collaboration between technical and science teams
  • Plain-language results and expectations management
  • Lessons learned from past projects
  • Insights into the AI project lifecycle.

TRAINING APPROACH

  • Presentation of theoretical concepts.
  • Numerous case studies.
  • Group discussions.
  • Additional reading.
Adrian Gonzalez Sanchez

MSc (mobile communications), MBA

Telecom & IT Engineer, Microsoft

Delphine Le Serre

PhD (Human and Social Sciences), Engineering degree in microelectronics and nanotechnology

Founder and President, EdHu2050
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