Overview


Data Science is a multi- disciplinary field that uses scientific methods, processes, algorithms to produce knowledge & insights from structured & unstructured data. It utilises techniques & theories derived from many fields such as computer science, mathematics, statistics & information science. Data science is a term used to refer to all the procedures and methodologies that are used to procure, organize, package, and present data in an easily understandable format. Data Science has been proved successful in creating a vast impact on various business industries. It has transformed the working of innumerable sectors & still on its way to explore the remaining untapped areas. Data Science helps the world in the expansion of new technology and benefits one to look at the world in a unique manner. Aspirants can take up the Data Science course and become a Data Scientist or a Professional in the field.

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Course Content

Course Content


  • Business Analytics
  • Analytics Methodology
  • Data Collection
  • Types & Dictionary
  • Business Analytics and Analytics Methodology
  • Problem Definition and Data Collection
  • Types & Dictionary
  • Probability Theory: Probability Distribution - Binomial Distribution-Normal distribution-Poisson distribution
  • Skewness & Kurtosis
  • R Installation & Introduction
  • Measure of Central Tendencies
  • Measure of Spread/Measure of Dispersion and Graphs
  • Process and Cleaning datasets
  • Treating Missing value
  • Transforming variables
  • SAS Installation & Introduction
  • Measure of Central Tendencies
  • Measure of Spread
  • Measure of Dispersion and Graphs
  • Health Care Case study-R & SAS
  • Estimation and hypothesis testing
  • T-Test
  • Internet Survey Case Study-R & SAS
  • Regression: Linear regression-coefficient of determination (R2)-Multi linear regression-Linear regression and Multi linear regression in R & SAS-Logistic regression-Logistic regression in R and SAS-Improving regression model in R
  • Cluster Analysis: K-means and Hierarchical-K-means and Hierarchical in R
  • Machine Learning: K-Nearest Neighbors-Naïve Bayes
  • Text Mining in R
  • Time series & Forecasting: Forecasting in R-Forecasting in SAS
  • Model Testing & Validating(R)
  • Retail Case Study-R
  • Retail Case Study-SAS
  • SPSS Software Practice
  • Advance Excel: F-Test/T-test Correlation
  • Regression
  • Moving Average
  • Exponential Smoothing

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