Statistics Concepts Every Data Scientist Should Know

By POOJA BISHT |Email | Apr 15, 2019 | 9309 Views

Statistics plays a very important role in Data Science. Whether it is visualizing data in Statistical models to find insights or to present the insights using Statistics to the different groups within your organization, Statistics is the part every Data Scientist should have a command at and should have flair in that.  The article draws out some statistical concepts that every Data Scientist should know and must have a clear understanding.

Basics of Statistics 
The Basics of Statistics like Mean, Median, Mode, and Variance are some of the basics that every Data Scientist must know about. The Mean is the average of the large set of numbers in a dataset and often shows the central value in an observation set.  A Median denotes the middle value in a large number of datasets such that an equal number of observations lies above and below it. It is of central importance in Statistics and found its utmost understanding by the Data Scientist. The value that repeats itself, again and again, is called mode. These basic Statistical features often become complex to be found out while handling complex datasets and must be analyzed deeply.

Probability Distribution
The Probability Distribution will provide you the probability of a different set of outcomes while performing an experiment. While handling complex datasets the Probability Distribution function will depict clearly which of the events are most likely to occur and which are not. Probability is of central importance in Data Science and to analyze the Probability Distribution curves of data is one of the desired quality found in Data Scientists. Uniform Distribution, Gaussian distribution, Poisson distribution are various topics that should be known by every Data Scientist.  Because the datasets are complex and statistics and probability is the most important part, a good understanding of these concepts helps a lot while working in finding useful information out of the data.

Bayes Theorem
Named after famous Statistician Thomas Bayes, Bayes Theorem finds its importance in Probability and Statistics in the way that it describes the probability of occurrence of an event with the help of some conditions that it uses. Using Bayes Theorem the probability of the desired event in your dataset (which can be anything from finding the number of adults prone to a particular disease or anything) could be found. For a complete understanding of the theorem and its application in probability and statistics, one can take help from various online free PDFs or videos readily available on YouTube.

I hope this article takes you a bit closer in realizing the importance of Statistics and also some concepts that you will need while developing a career in Data Science. There are various books available online and various good platforms providing online courses in developing various skills of Data Scientists. A learner is required to explore more and more and gain a deeper understanding of various concepts because it is the knowledge and skill that counts in the world. 

Source: HOB