Data Science

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Users Enrolled: 33


Created By: Ankita Pandey


Last Updated: 2020-03-26 15:24:04

Classroom Traning

Duration of class: 2 hrs/day Days: - Regular Batch- Monday- Friday (First 8classes will be theoretical and then theoretical –practical alternate)

In this course we cover all the basic and advanced topics of Data Science.

About DataScience
In this course, part of our Professional Program in Data Science,you will learn valuable concepts in probability theory. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.
What you'll cover & learn the topic
  • Important concepts in probability theory including random variables and independence
  • How to perform a Monte Carlo simulation
  • The meaning of expected values and standard errors and how to compute them in R
  • The importance of the Central Limit Theorem
Description Of Data Science
We will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance. Probability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, part of our Professional Program in Data Science, you will learn valuable concepts in probability theory. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.

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