Daniel Egger

 

 


 

Daniel Egger joined the Duke MEM Program in 2009 after seventeen years of hands-on experience in technology startups and venture capital. Egger has developed his own innovative technologies, founded companies, and raised venture capital as an entrepreneurial CEO; served as a member of the Board of Directors and value-added advisor to numerous successful high-tech companies; and was a founder and Managing Partner of a venture capital fund focused on seed and early stage investing in new technology companies.

More info here.




Customize your search:

E.g., 2017-02-28
E.g., 2017-02-28
E.g., 2017-02-28
Mar 6th 2017

Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.

No votes yet
Feb 27th 2017

This course is an introduction to how to use relational databases in business analysis. You will learn how relational databases work, and how to use entity-relationship diagrams to display the structure of the data held within them. This knowledge will help you understand how data needs to be collected in business contexts, and help you identify features you want to consider if you are involved in implementing new data collection efforts.

Average: 5.5 (19 votes)
Feb 20th 2017

In business, data and algorithms create economic value when they reduce uncertainty about financially important outcomes. This course teaches the concepts and mathematical methods behind the most powerful and universal metrics used by Data Scientists to evaluate the uncertainty-reduction – or information gain - predictive models provide. We focus on the two most common types of predictive model - binary classification and linear regression - and you will learn metrics to quantify for yourself the exact reduction in uncertainty each can offer. These metrics are applicable to any form of model that uses new information to improve predictions cast in the form of a known probability distribution – the standard way of representing forecasts in data science.

Average: 6.8 (4 votes)
Feb 20th 2017

In this course, you will learn best practices for how to use data analytics to make any company more competitive and more profitable. You will be able to recognize the most critical business metrics and distinguish them from mere data. You’ll get a clear picture of the vital but different roles business analysts, business data analysts, and data scientists each play in various types of companies. And you’ll know exactly what skills are required to be hired for, and succeed at, these high-demand jobs.

No votes yet
Feb 20th 2017

One of the skills that characterizes great business data analysts is the ability to communicate practical implications of quantitative analyses to any kind of audience member. Even the most sophisticated statistical analyses are not useful to a business if they do not lead to actionable advice, or if the answers to those business questions are not conveyed in a way that non-technical people can understand. In this course you will learn how to become a master at communicating business-relevant implications of data analyses.

Average: 5.8 (5 votes)