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Statistics Fundamentals For Data Science Career Enthusiasts
If you are a Data Science career enthusiast then you must surely be aware of the prominence of Statistics in Data Science. Proficient knowledge of Statistics will make your Data Science training process hassle-free.
Most of the people are still having the misconception that with exceptional skills in coding & mathematics, skills in statistics can be made exceptional. However this isn’t the right outlook to become a skilled expert in Data Science since statistics is one of the core skills in the inventory of a Data Scientist.
Statistics For Data Science: Distributions
Having knowledge of various distributions is very crucial in Statistics. These include
Normal Distribution is usually used for representing medical findings. If you represent normal distribution in the form of a graph that it would be assuming a bell-shaped curve. For instance, a single variable can be observed in a large group over a period.
Applications for this type of distribution include
- Finding the birth weight of all the newborns around the world
- With normal distribution it will also become easy to predict stock returns based on their history of performances
Poisson Distribution is used in the case where we have to forecast the number of events that are likely to occur over a specific period of time. It is very much helpful for the industries which deal with enormous levels of discrete data wherein the probability of occurrence of an individual event is small.
Binomial Distribution is used in the case where likelihood of pass or fails outcome in a survey or experiment that’s repeatedly done in succession. In this type, there will always be two possible outcomes either True/False or Yes/No!
Statistics For Data Science: Theorems & Algorithms
Bayes Theorem is used in the cases where we need to find the conditional probability. It helps in the betterment of the predictions based on the available evidence. Speaking of real-time, Bayes Theorem is being used by banks to rate the extent of risk involved while lending money to potential borrowers by analyzing their previous records of defaulted payment or account activities.
K-Nearest Neighbor Algorithm
It is quite simple & easy to implement. It is employed in the regression problems & is best trusted for its competitive results.
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