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