Learning Outcomes By the end of this section, you should be able to: Data scientists interested in inferring the value of a population truth or parameter such as a population mean or a population proportion turn to inferential statistics. A data scientist is often…
Figure 4.1 Inferential statistics is used extensively in data science to draw conclusions about a larger population—and drive decision-making. (credit: modification of work “Compiling Health Statistics” by Ernie Branson/Wikimedia Commons, Public Domain) Chapter Outline 4.1 Statistical Inference and Confidence Intervals 4.2 Hypothesis Testing 4.3 Correlation…
Learning Outcomes By the end of this section, you should be able to: Probability distributions are used to model various scenarios to help with probability analysis and predictions, and they are used extensively to help formulate probability-based decisions. For example,…
Learning Outcomes By the end of this section, you should be able to: Probability is a numerical measure that assesses the likelihood of occurrence of an event. Probability applications are ubiquitous in data science since many decisions in business, science, and…
Learning Outcomes By the end of this section, you should be able to: Common measures of position include percentiles and quartiles as well as z-scores, all of which are used to indicate the relative location of a particular datapoint. Percentiles If…
Learning Outcomes By the end of this section, you should be able to: Providing some measure of the spread, or variation, in a dataset is crucial to a comprehensive summary of the dataset. Two datasets may have the same mean but…
Learning Outcomes By the end of this section, you should be able to: Measures of center are statistical measurements that provide a central, or typical, representation of a dataset. These measures can help indicate where the bulk of the data…
Figure 3.1 Statistics and data science play significant roles in stock market analysis, offering insights into market trends and risk assessment for better financial decision-making. (credit: modification of work “That was supposed to be going up, wasn’t it?” by Rafael Matsunaga/Flickr, CC…
Learning Outcomes By the end of this section, you should be able to: Large datasets, also known as big data, are extremely large and complex sets of data that traditional data processing methods and tools are unable to handle. These datasets…
Learning Outcomes By the end of this section, you should be able to: Data cleaning and preprocessing is an important stage in any data science task. It refers to the technique of organizing and converting raw data into usable structures…