How Statistics used in Data Science
As Josh Wills once said,
“Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”
According to above statement I can say data science is a combination of Programming and Statistics.Because Data scientist should know Programming and statistics.In another simple words Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. … In turn, these systems generate insights which analysts and business users can translate into tangible business value.Anyway statistics used in Data Science.
Statistics, as an academic and professional discipline, is the collection, analysis and interpretation of data.So Statistics used in data science field for various things.As such, statistics is a fundamental tool of data scientists, who are expected to gather and analyze large amounts of structured and unstructured data and report on their findings.So To become a data scientist, you must have a strong understanding of mathematics, statistical reasoning, computer science and information science. You must understand statistical concepts, how to use key statistical formulas, and how to interpret and communicate statistical results.data scientists need to understand the fundemental concepts of descriptive statistics and probability theory, which include the key concepts of probability distribution, statistical significance, hypothesis testing and regression. Bayesian thinking is also important for machine learning; its key concepts include conditional probability, priors and posteriors, and maximum likelihood.Descriptive statistics is a way of analyzing and identifying the basic features of a data set and probability theory is a branch of mathematics that measures the likelihood of a random event occurring, according to Encyclopedia Britannica.
Statistics Features in Data Science
Statistical features are often the first techniques data scientists use to explore data.Statistical features include organizing the data and finding the minimum and maximum values, finding the median value, and identifying the quartiles. The quartiles show how much of the data falls under 25%, 50% and 75%. Other statistical features include the mean, mode, bias and other basic facts about the data.
Use Statistics and Data Science
If you are eager to learn more about statistics and how to mine large data sets for useful information, data science might be right for you. Competency in statistics, computer programming and information technology could lead you to a successful career in a wide range of industries. Data scientists are needed almost everywhere, from health care and science to business and banking.
Finally I can say statistics is a essential need for data science. So who likes data science field firstly i suggest you fisrt learn statistics deeply.Then you can understand data science.
see you again later!!.
I am Ashan Tharaka,
Undergraduate Of Faculty Of Information Technology,
University Of Moratuwa.