Statistics for Data Scientists

• Published in: SAS for R Users pp. 159 - 182
• Main Author: Ohri, Ajay
• Format: Book Chapter
• Language: English
• Published: United States John Wiley & Sons, Incorporated 2019; John Wiley & Sons, Inc
• Subjects: COMPUTER PROGRAMMING; data scientists; descriptive statistics; frequency distribution; inferential statistics; MATHEMATICS; problem‐solving operations; SAS
• ISBN: 1119256410; 9781119256410
• DOI: 10.1002/9781119256441.ch11

Data Scientists need to study statistics as well as computer science to be effective in their analytical journey. Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g. observational errors, sampling variation). A frequency distribution is an overview of all distinct values in some variable and the number of times they occur. That is, a frequency distribution tells how frequencies are distributed over values. Unlike descriptive analytics which describe data of the past predictive analytics deals with forecasts for the future. An algorithm is a set of rules to be followed in calculations of other problem‐solving operations, especially by a computer. SAS has many data mining algorithms in SAS Enterprise Miner. R has a caret package that has many such modeling functions.