\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Data science, as a statistical branch of mathematics, studies probability. The term \u201cprobability\u201d lends itself to the likeliness of events. In the sense of data, it looks into how predictable a particular occurrence of event is. Using computer language of 0 and 1, the likelihood of the presence of an event will be represented by 1 while its absence or unlikeliness will be denoted by 0 (Stuart & Ord, 2009). A good example of such is evident in the Bernoulli trial of tossing a coin with its double outcomes of either a head (1) or a tail (0)…<\/p>\n
Azeez Olanrewaju Shoderu
\nQuantitative Data Analysis Module Task 7 Discussion University of East London, UK through UNICAF Scholarship
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\nIntroduction
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Data science, as a statistical branch of mathematics, studies probability. The term \u201cprobability\u201d lends itself to the likeliness of events. In the sense of data, it looks into how predictable a particular occurrence of event is. Using computer language of 0 and 1, the likelihood of the presence of an event will be represented by 1 while its absence or unlikeliness will be denoted by 0 (Stuart & Ord, 2009). A good example of such is evident in the Bernoulli trial of tossing a coin with its double outcomes of either a head (1) or a tail (0).
\nDistribution and its Types
\nProbability cannot be fully understood without reference to the various kinds of distributions available. It happens to be a crucial element of analyzing datasets showing possible data results and how often they can appear (Research Optimus, 2022). In data analytics, there are three main distributions which are: Normal Distribution, Binomial Distribution and Poisson Distribution. As a competent data scientist, knowing some or all of these distributions and how they work will provide a firm grasp and in-depth knowledge of this field.Bayes theory and its probability
\nBayes theory is a probability theory in form of a mathematical formula that performs inferences or reasoning to ensure the conditional probability of events (Olshausen, 2004). It relates to the probability of an occurrence on the premise of past information about the event\u2019s relevant conditions. Corporate finance institute (2022) states that a common domain of usage of Bayes theory is \u201cmodeling the risk of lending money to borrowers or forecasting the probability of the success of an investment\u201d.
\nDifferentiate between Binomial Distribution and Bayesian probability
\nFrom the term \u2018binomial\u2019, it can be deduced that binomial distribution is the probability distribution wherein its random variable is either one of two outcomes; success or failure (ABK, 2011). On a contrary, Bayesian probability goes beyond probability between a pair of events; it deals with probability about unfamiliar variables that could result to varying outcomes. It is more related to the calculation of a personal belief (Bruno, 2017). That is, Bayesian probability tries to interpret natural phenomenon using statistics techniques. A typical example of a mathematical problem that Bayesian probability can tackle in the environment is the prediction of possible climate change or effect in Africa within a couple of years from now.
\nConclusion
\nProbability, distribution and their types are among the crux of this noble field. In fact, they are mostly applied in various other disciplines and sub-career paths amongst which are science, finance, artificial intelligence, machine learning, computer science, software engineering, game theory, etc, to help draw inferences about the expected frequency of events. In the technologically-driven business world, companies now implement digitize their decision making process through the application of such probabilities in their data analytics to extract value from their large datasets.
\nReference List
\nABK (2011, November 7). Difference Between Binomial and Normal Distribution. DifferenceBetween.com. https:\/\/www.differencebetween.com\/difference-between-binomial-and-vs-normal-distribution\/
\nBruno, D. F. (2017). Theory of Probability: A critical introductory treatment. Chichester: John Wiley & Sons Ltd. ISBN 9781119286370.
\nCorporate finance institute (2022, May 5). Bayes\u2019 Theorem: A mathematical formula used to determine the conditional probability of events. https:\/\/corporatefinanceinstitute.com\/resources\/knowledge\/other\/bayes-theorem\/
\nOlshausen, B. A. (2004, March 1). Bayesian probability theory. NPB 163\/PSC 128 – Information processing models in neuroscience and psychology. Redwood Center for Theoretical Neuroscience. http:\/\/rctn.org\/bruno\/npb163\/bayes.pdf \u00a0
\nResearch Optimus (2022). Difference between Normal, Binomial, and Poisson Distribution. https:\/\/www.researchoptimus.com\/article\/normal-binomial-poisson-distribution.php.
\nStuart, A. and Ord, K. (2009). Volume 1: Distribution Theory. Kendall’s Advanced Theory of Statistics, 6th Ed, Wiley.
\n\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Data science, as a statistical branch of mathematics, studies probability. The term \u201cprobability\u201d lends itself to the likeliness of events. In the sense of data, it looks into how predictable a particular occurrence of event is. Using computer language of 0 and 1, the likelihood of the presence of an event will be represented by 1 while its absence or unlikeliness will be denoted by 0 (Stuart & Ord, 2009). A good example of such is evident in the Bernoulli trial of tossing a coin with its double outcomes of either a head (1) or a tail (0)…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-117","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=\/wp\/v2\/pages\/117","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=117"}],"version-history":[{"count":0,"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=\/wp\/v2\/pages\/117\/revisions"}],"wp:attachment":[{"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=117"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}