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Data Science for Business People

Cohort-based Course

Build the knowledge of asking key questions & challenging assumptions so you can lead successful data initiatives

Data Science for Business People

Cohort-based Course

Build the knowledge of asking key questions & challenging assumptions so you can lead successful data initiatives

Hosted by

Howard Friedman and Akshay Swaminathan

Best-selling authors. Decades experience in the public & private sectors.

Howard Friedman and Akshay Swaminathan

Best-selling authors. Decades experience in the public & private sectors.

Course overview

Succeed at using data science: good customers get great data science results

Arm yourself with the knowledge needed to ensure your data science projects get good returns on investment.

Nearly 90% of businesses plan to increase their investments in data, analytics and AI yet many will fail. Why fail? One of the most common issues business stakeholders often don’t know how to be good data science customers.

This course prepares you to be a good customer for data science teams, helping you get the most value out of their expertise and hopefully avoid major pitfalls where money and time are wasted. This course will walk you through many of the key ideas you need to know in order to:

1) introduce data science at your company

2) improve the data science team’s effectiveness and efficiency

3) give you the basic background needed to communicate well with your technical teams.

By the end of this course you will be able to answer key questions including:

- what are some of the main hardware and software tools used to analyze data?

- who are the different players on the data science team?

- which models should be considered for specific projects?

Most importantly, you will also be armed with critical questions that you can use to further probe data analysts, statisticians, data scientists and other technical experts.

Topics covered include:

Session 1: Data Science Tools and Projects

- Tools of the Trade

- Data Science Project

Session 2: Data Science Foundations and Decision-Making

- Data Science Foundations

- Making Decisions with Data

Session 3: Unsupervised ML and Building Models 101

- Unsupervised Machine Learning

- Building Models 101

Session 4: Machine Learning and Ethics

- Machine Learning Practices and Examples

- Ethical Considerations

Every session will be a mix of lecture, practice and Q&A

Business leaders, managers and data scientists

Business people that are working with data science teams and want to increase the chances of success

Data scientists who want to improve the chances of success by understanding their customers better and how to communicate with them

MBA graduates planning to work with data scientists in the near term future and unsure how to begin

Business people that are working with data science teams and want to increase the chances of success

Data scientists who want to improve the chances of success by understanding their customers better and how to communicate with them

MBA graduates planning to work with data scientists in the near term future and unsure how to begin

What you’ll get out of this course

Improve Communication

Learn critical questions to ensure project success
Challenge assumptions inherent in analytic approaches
Master basic data science terminology

Increase chances of success

Understand how to define a project scope
Learn tools used by data scientists
Define success for a data science project

Grow your data science knowledge

Making Good Decisions with Data
Customer Segmentation
Predictive Modeling
Advanced Data Science
Ethics

This course includes

4 interactive live sessions

Lifetime access to course materials

8 in-depth lessons

Direct access to instructor

Projects to apply learnings

Guided feedback & reflection

Private community of peers

Course certificate upon completion

Maven Satisfaction Guarantee

This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.

Course syllabus

Expand all modules

Week 1
May 20—May 26
Week dates are set to instructor's time zone
Events
May
20
Session 1: Data Science Tools and Projects
Session 1: Data Science Tools and Projects
Mon, May 20, 10:00 PM - 11:00 PM UTC
May
22
Session 2: Data Science Foundations and Decision-Making
Session 2: Data Science Foundations and Decision-Making
Wed, May 22, 10:00 PM - 11:00 PM UTC
Modules
Module 1: Data Science Tools and Projects
Module 1: Data Science Tools and Projects
2 Lessons
Module 2: Data Science Foundations and Decision-Making
Module 2: Data Science Foundations and Decision-Making
2 Lessons
Week 2
May 27—May 29
Week dates are set to instructor's time zone
Events
May
28
Session 3: Unsupervised ML and Building Models 101
Session 3: Unsupervised ML and Building Models 101
Tue, May 28, 10:00 PM - 11:00 PM UTC
Modules
Module 3: Unsupervised ML and Building Models 101
Module 3: Unsupervised ML and Building Models 101
2 Lessons
Module 4: Machine Learning and Ethics
Module 4: Machine Learning and Ethics
2 Lessons
Post-Course
Events
May
30
Session 4: Machine Learning and Ethics
Session 4: Machine Learning and Ethics
Thu, May 30, 10:00 PM - 11:00 PM UTC

Testimonials

As experienced, trusted data science advisors, and by providing valuable examples, Friedman and Swaminathan open a new data-driven world that spans every single industry vertical.”

Armen Kherlopian

Chief Data Scientist, Ph.D.

cuts through data science buzzwords and empowers with the knowledge to cultivate thriving data cultures.

Jeff Chen

Former Chief Data Scientist of US Department of Commerce

Friedman and Swaminathan have taken the complex topic of data science and made it accessible to everyone

Melvin (Skip) Olson

Global head, Integrated Evidence Strategy and Innovation, Novartis Pharma AG

Meet your instructor

Howard Friedman

MMS, Ph.D. Chief Data Scientist, Adjunct Professor, Best Selling Author

Adjunct Professor at Columbia University. Data scientist with decades of experience leading analytics projects in the private and public sectors. His books, including Ultimate Price (2020), Measure of a Nation (2012) and Winning with Data Science (2024) have been translated into many languages and featured on national media.

Akshay Swaminathan

Head of Data Science at Cerebral; MD, Ph.D. student

Data scientist who works on strengthening health systems. He has more than forty peer-reviewed publications, and his work has been featured in the New York Times and STAT. Previously at Flatiron Health, he currently leads the data science team at Cerebral and is a Knight-Hennessy scholar at Stanford University School of Medicine.

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