Remove 2001 Remove IT Remove Metrics Remove Statistics
article thumbnail

Data Discussion Lessons from Brad Pitt

Juice Analytics

From: Ocean's Eleven (2001) Now imagine yourself giving a pep talk to the next email, PowerPoint slide, or dashboard that you are about to send out. Messages must be clear and focused and eliminate the unnatural, mechanical chart headings and the unnecessarily complex statistical jargon. How exactly is this metric calculated?

Metrics 100
article thumbnail

Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve. What is machine learning?

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Estimating the prevalence of rare events — theory and practice

The Unofficial Google Data Science Blog

Of course, any mistakes by the reviewers would propagate to the accuracy of the metrics, and the metrics calculation should take into account human errors. If we could separate bad videos from good videos perfectly, we could simply calculate the metrics directly without sampling. The PMF depends on how prevalence is defined.

Metrics 98
article thumbnail

To Balance or Not to Balance?

The Unofficial Google Data Science Blog

Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. we drop the $i$ index.

article thumbnail

Data Science, Past & Future

Domino Data Lab

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. Paco Nathan: Thank you, Jon [Rooney]. I really appreciate it. I am honored to be able to present here and thrilled to have been involved in Rev.

article thumbnail

Reclaiming the stories that algorithms tell

O'Reilly on Data

Algorithms tell stories about who people are. The first story an algorithm told about me was that my life was in danger. It was 7:53 pm on a clear Monday evening in September of 1981, at the Columbia Hospital for Women in Washington DC. I was exactly one minute old. You get two points for waving your arms and legs, for instance.)

Risk 356