Description:
In this Skillsoft Aspire course on data science, learners can explore hypothesis testing, which finds wide applications in data science. This beginner-level, 10-video course builds upon previous coursework by introducing simple inferential statistics, called the backbone of data science, because they seek to posit and prove or disprove relationships within data. You will start by learning steps in simple hypothesis testing: the null and alternative hypotheses, s-statistic, and p-value, as ach term is introduced and explained. Next, listen to an informative discussion of a specific family of hypothesis tests, the t-test. Then learn to describe their applications, and become familiar with how to use cases including linear regression. Learn about Gaussian distribution and the related concepts of correlation, which measures relationships between any two variables, and autocorrelation, a special form used in the concept of time-series analysis. In the closing exercise, review your knowledge by differentiating between the null and the alternative hypotheses in a hypothesis testing procedure, then enumerating four distinct uses for different types of t-tests.
Target Audience:
Duration: 01:02
Description:
Data science is an interdisciplinary field that seeks to find interesting generalizable insights within data and then puts those insights to monetizable use. In this 8-video Skillsoft Aspire course, learners can explore the first step in obtaining a representative sample from which meaningful generalizable insights can be obtained. Examine basic concepts and tools in statistical theory, including the two most important approaches to sampling—probability and nonprobability sampling—and common sampling techniques used for both approaches. Learn about simple random sampling, systematic random sampling, and stratified random sampling, including their advantages and disadvantages. Next, explore sampling bias. Then consider what is probably the most popular type of nonprobability sampling technique—the case study, used in medical education, business education, and other fields. A concluding exercise on efficient sampling invites learners to review their new knowledge by defining the two properties of all probability sampling techniques; enumerating the three types of probability sampling techniques; and listing two types of nonprobability sampling.
Target Audience:
Duration: 00:47
Description:
Along the career path to Data Science, a fundamental understanding of statistics and modeling is required. The goal of all modeling is generalizing as well as possible from a sample to the population of big data as a whole. In this 10-video Skillsoft Aspire course, learners explore the first step in this process. Key concepts covered here include the objectives of descriptive and inferential statistics, and distinguishing between the two; objectives of population and sample, and distinguishing between the two; and objectives of probability and non-probability sampling and distinguishing between them. Learn to define the average of a data set and its properties; the median and mode of a data set and their properties; and the range of a data set and its properties. Then study the inter-quartile range of a data set and its properties; the variance and standard deviation of a data set and their properties; and how to differentiate between inferential and descriptive statistics, the two most important types of descriptive statistics, and the formula for standard deviation.
Target Audience:
Duration: 01:11