Tag: statistics

Feed: Recent Announcements. Today, Amazon Elastic Container Registry (ECR) launched the ability to monitor repository pull statistics through Amazon CloudWatch. The new pull statistics helps you to monitor usage patterns or identify anomalous behavior by observing image pull requests per repository. Amazon CloudWatch will now provide you statistics on the number of images pulled from your private ECR repository by default. With additional insights customers can identify commonly accessed repositories, understand adoption of new repositories, or have better insights on repository usage. Customers facing service limits can use this data to understand which repositories are seeing the greatest number of ... Read More

Feed: SAS Blogs. Author: Peter Styliadis. Welcome to the sixth installment in my series Getting Started with Python Integration to SAS Viya. In previous posts, I discussed how to connect to the CAS server, how to execute CAS actions, and how to work with the results. Now it's time to generate simple descriptive statistics of a CAS table. Let's begin by confirming the cars table is loaded into memory. With a connection to CAS established, execute the tableInfo action to view available in-memory tables. If necessary, you can execute the following code in SAS Studio to load the sashelp.cars table into memory. conn.tableinfo(caslib="casuser") The ... Read More

Feed: Top Blog Posts. Author: Vincent Granville. In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one may encounter in any business setting: you might even discover that you are a data scientist yourself, without knowing it. As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our ... Read More

Feed: Top Blog Posts. Author: Vincent Granville. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from ... Read More

Feed: Top Blog Posts. Author: Vincent Granville. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from ... Read More

Feed: Top Blog Posts. Author: Vincent Granville. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from ... Read More

Feed: Top Blog Posts. Author: Vincent Granville. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from ... Read More

Feed: Top Blog Posts. Author: Stephanie Glen. Naming conventions are often quite different in statistics and data science, which causes quite a bit of confusion. Part of the problem with naming conventions is  that "...data science is the child of statistics and computer science” (Blei & Symth, 2017) . In essence, data science then is the child of two parents who speak different languages. In one sense, this makes the job of the data scientist not only to apply the knowledge from both "parents", but to also act as a translator between the two, which is where a dictionary comes ... Read More

Feed: Top Blog Posts. Author: Stephanie Glen. Naming conventions are often quite different in statistics and data science, which causes quite a bit of confusion. Part of the problem with naming conventions is  that "...data science is the child of statistics and computer science” (Blei & Symth, 2017) . In essence, data science then is the child of two parents who speak different languages. In one sense, this makes the job of the data scientist not only to apply the knowledge from both "parents", but to also act as a translator between the two, which is where a dictionary comes ... Read More

Feed: Top Blog Posts. Author: Stephanie Glen. Naming conventions are often quite different in statistics and data science, which causes quite a bit of confusion. Part of the problem with naming conventions is  that "...data science is the child of statistics and computer science” (Blei & Symth, 2017) . In essence, data science then is the child of two parents who speak different languages. In one sense, this makes the job of the data scientist not only to apply the knowledge from both "parents", but to also act as a translator between the two, which is where a dictionary comes ... Read More