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Statistics for Data Science using R

Statistics is one of the most important work horses for Data Science. In the Data career you will have to derive insights out of complex and noisy data. Uncertainty is inherent in noisy data. Statistics and probability gives us the mathematical frame work to navigate through this data. R-Programming is a Statistical tool and has become the tool of choice for many Data scientist. We will learn Statistical concepts by doing it on R. Check out the learning objectives below.

StatisticsusingR Introduction

Mithun Radhakrishnan

Course Overview

Statistics is the science of

  • Collecting,
  • Presenting,
  • Analysing and
  • Interpreting data.

Therefore, data scientist/analyst need to know statistics. Post this course you should develop conceptual understanding of business statistics, its applications. You would perform statistical inference and modelling to understand data and make decision based on data.

You should be able to understand the statistical results effectively and in the context of business problem without getting overwhelmed by the statistical jargon. You would learn to analyse and visualize data in R.

You should be able to demonstrate mastery over statistical data analysis from exploratory data analysis to inference to modelling, suitable for applying for business analyst or data scientist positions.

R (Programming language) is free programming language and software environment for statistical computing and graphics. The R language is widely used among data scientist, statisticians and data miners for developing data models and data analytics. R has become one the most sought-after skills in 2018.


Statistics learning objectives:

  • Introduction to Statistics for Data Science.
  • Measuring spreads.
  • Calculating probabilities.
  • Discrete Probability distributions.
  • Permutation & Combinations.
  • Geometric, Binomial and Poisson distribution.
  • Normal distribution.
  • Statistical sampling.
  • Estimating for population..
  • Constructing confidence intervals.
  • Hypothesis tests for business decisions.
  • The chi-square distribution.

R- (programming language) learning Objectives:

  • Getting to know the R environment.
  • Reading data.
  • Basic programming.
  • Visualization.
  • Data munging.


Related Certifications

Exam 70-773 Analysing Big data with Microsoft R


The course assumes that the student has no prior statistical or programming. However, comfort with college level math is must. Student need not know the math but should be comfortable with it.


1 Modules 2.5 Hours of Videos Access from Mobie, PC or app

Course Completion Certificate

Trainer Profile

Mithun Radhakrishnan, is a Freelance Data Enthusiast and Analyst.

He Primarily consults in the area of Digital Marketing. Mithun has consulted for multiple data projects as a Freelancer. Prior to this he was heading a team of stock analyst with a leading Bank in India.

In terms of Qualification, he has PG in Business Analytics and Business Intelligence from Illinois Institute of Technology. He is also a Chartered Market Technician (CMT) which is the highest certification in the field of Technical Analysis of stock market data. He also has ......

Subject-matter expert

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