This workshop is hands-on and intended for beginners; no previous knowledge of data analysis and/or R is required. This session will cover the following topics for R: data preparation, descriptive ...

The purpose of the course is to introduce the statistical methods that are critical in the performance analysis and selection of information systems and networks. It includes fundamental topics as ...

The purpose of the course is to introduce the statistical methods that are critical in the performance analysis and selection of information systems and networks. It includes fundamental topics as ...

The Statistical & Data Sciences (SDS) Program links faculty and students from across the college interested in learning things from data. At Smith, students learn statistics by doing—class time ...

Let’s start with a definition of Applied Statistics: Applied Statistics is the root of data analysis. The practice of applied statistics involves analyzing data to help define and determine an ...

Statistics is the science of learning from data. The theoretical foundation of statistics lies in probability theory, which is applied to decision-making under uncertainty. Data science consists of ...

April 4, 2024 4:00 pm to 5:00 pm About this event Power BI is a powerful tool for quick data analysis and visualization. Learn how to use this Microsoft tool for integrating different data files and ...

Introduces students to importing, tidying, exploring, visualizing, summarizing, and modeling data and then communicating the results of these analyses to answer relevant questions and make decisions.

Select two courses from the following list. DEPT 0000 - An approved course from student’s home department (3 hours) STAT 4640 - Introduction to Statistical Computing (3 hours) STAT 5850 - Applied Data ...

Statistical data science represents an interdisciplinary field that merges the rigour of probability theory and statistical inference with modern computational tools to draw insights and make ...

Not a week goes by without us publishing something here at HBR about the value of data in business. Big data, small data, internal, external, experimental, observational — everywhere we look, ...