Page 139 - Data Science class 10
P. 139

DISTRIBUTIONS IN DATA

                SCIENCE


                                                                                       02








                       Learning Outcome



                     2.1.  What is Distribution in Data Science?        2.2.  Types of Distributions
                     2.3.  Statistical Problem-Solving Process
                     2.4.  Activity - Choosing Groups for School Dance Programs



            In  the  previous chapter,  we  have  learnt  about  various  statistical terminology that  are  frequently  used  in  data
            science. We have also learnt about two-way frequency table and its application in fitting data points in each
            category. The statistical terms included the central tendencies like Mean, Median and Mode. Ultimately, you were
            introduced about mean Absolute Deviation, Variance and Standard Deviation.

            In this chapter, we will learn about distribution of data in statistics. We will also learn about different types of data
            distributions and characteristics of each distribution in detail.


            2.1. WHAT IS DISTRIBUTION IN DATA SCIENCE?

            A distribution is a simple way to visualise a set of data. It can be shown either as a graph or a list, revealing which
            values of a random variable have lower or higher chances of happening. In data science, the word "distribution"
            typically refers to a probability distribution. Probability distribution is a mathematical approach that displays the
            likely values for a variable and how frequently they occur. Probability is one of the main building blocks of data
            science and machine learning.
            Distribution assists us in truly visualising what is going underneath, while the concept of probability provides the
            mathematical computations.





              Probability refers to the possibility of something happening. It is a mathematical concept that predicts how likely
              events are to occur.


            For example, consider a coin which has two sides, head and tail.










                                                Tail                    Head



                                                                                 Distributions in Data Science  137
   134   135   136   137   138   139   140   141   142   143   144