Data Science using r

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Data Science Using R Course Overview

This course is designed to master techniques like data exploration, data visualization, and predictive analytics and descriptive analytics with the R programming language. The course covers the import and export of data in R, data structures in R, different statistical concepts, cluster analysis, and forecasting. Student will gain an understanding of analyzing data to help companies make more effective business decisions.

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Course Fees Rs.12000

  • Duration : 2 months
  • No Cost EMI : INR 6000 x 2

NEFT Payment :Bank Account details-Account Name – KLMS Hands-On Systems Private Limited, Account No – 50200042627525, IFSC – HDFC000027

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Course Description!

Data Science Using R Course Curriculum

This course covers Introduction to R programming, R programming essentials, Fundamental of R Language, Data Visualization & Manipulation, Linear Algebra with R-Language- Exploratory Factor Analysis, Supervised Machine Learning Algorithm, Machine Learning with R-language, Unsupervised Machine Learning Algorithm, Decision tree, Random forest and Clustering, Deployment, Web app with shiny dashboard, Time Series Analysis, Hands-On Project.

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Course content

  • What is analytics & Data Science?
  • Common Terms in Analytics
  • Analytics vs. Data warehousing, OLAP, MIS Reporting
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • Overview of analytics tools & their popularity
  • List of steps in Analytics projects
  • Identify the most appropriate solution design for the given problem statement
  • Project plan for Analytics project & key milestones based on effort estimates
  • Build Resource plan for analytics project
  • Why R and Python for data science?
  • What is R
  • What is S
  • History of R
  • Features of R
  • SAS versus R
  • Installing R
  • Packages
  • Input/output
  • R interfaces
  • R Library
  • Basic operations in R
  • Different data types and data structures in R
  • Sub setting in R
  • Additional topics on data structures
  • Importing data sets in R
  • R loops and special functions
  • Calculation of commission and simple interest
  • Plots and charts in R
  • Merging and sorting functions in R
  • Summarising Data
  • Calculations of the measures of central tendency and measures of variability
  • Concept of hypothesis
  • Null hypothesis
  • Alternative hypothesis
  • Type-I error
  • Type-II error
  • Level of Significance
  • Confidence Interval
  • Parametric Tests and Non Parametric Tests
  • One Sample T test
  • Two independent sample T test
  • Paired Sample T test
  • Chi square Test for Independence of Attributes.
  • One Way Anova
  • Two Way Anova
  • Principal Component Analysis
  • Estimating the Initial Communalities
  • Eigen Values and Eigen Vectors
  • Correlation Matrix check and KMO-MSA check
  • Factor loading Matrix
  • Diagrammatic Representation of Factors
  • Problems of Factor Loadings and Solutions
  • Types of Clusters
  • Metric and linkage
  • Ward’s Minimum Variance Criteria
  • Semi-Partial R-Square and R-Square
  • Diagrammatic Representation of clusters
  • Problems of Cluster Analysis
  • Concept of Regression and features of Linear line.
  • Assumptions of Classical Linear Model
  • Method of Least Squares
  • Understanding the Goodness of Fit
  • Test of Significance of The Estimated Parameters
  • Multiple linear Regression with their Assumptions
  • Concept of Multocollinearity
  • Signs of Multicollinearity
  • Concept and Applications of Logistic Regression
  • Principles Behind Logistic Regression
  • Comparison between Linear probability Model and Logistic Regression
  • Mathematical Concepts related to Logistic Regression
  • Concordant Pairs, Discordant Pairs and Tied Pairs
  • Classification Table
  • Decision Tree
  • Concept of Time Series and its Applications
  • Assumptions of Time Series Analysis
  • Components of Time Series
  • Smoothening techniques
  • Stationarity
  • Random Walk
  • ARIMA Forecasting
  • Box Jenkins Technology
  • Merits and Demerits of BJ Technology
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