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Business Analytics With R Programming

Business Analytics With R Programming Training in Pune-Mumbai-Thane-online-classroom-classes. R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years. Why learn R ? R is a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities. R has an effective data handling and storage facility, R provides a suite of operators for calculations on arrays, lists, vectors and matrices. R provides a large, coherent and integrated collection of tools for data analysis. R provides graphical facilities for data analysis and display either directly at the computer or printing at the papers. As a conclusion, R is world’s most widely used statistics programming language. It's the # 1 choice of data scientists and supported by a vibrant and talented community of contributors. .

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Business Analytics With R Programming Training in Pune: Introduction to the Course

 How to use R to explore and visualize data
 How to use randomization and simulation to build inferential ideas
 How to effectively create stories using the ideas to convey information to audience
 Introduction to data sources in R (built-in)
 What is a data science pipeline?
Exposure to data collection, data manipulation, data viz, data modelling, inference, correlation and regression, interpretation & convey.

Pre-requisite/Requirements

We assume no prerequisites: no algebra, no calculus and no prior programming experience. This course is intended to be a gentle and nice introduction to the practice of statistics in terms of how data scientists, statisticians, data journalists and other scientist analyse data and write inference about data.

Introduction and Installation

 Downloading and Installing R
 Getting Help on a function
 Viewing Documentation
 General issues in R
 Packages Management
 Data Inputting in R
Data Types, Sub setting, writing data, reading tabular data files, reading from csv files, creating a vector and vector operations, initializing a data frame, Control structures, selecting data frame columns by position and name, Changing directories, Re-directing R output.

Data Structures in R

 Inserting, Appending Data to a Vector
 Combining Multiple Vectors into One Vector
 How to create a list
 Selecting List Elements and Removing Elements
 Flatten a List into a Vector
 Removing NULL Elements from a List
 Removing List Elements Using a Condition
 How to Initializing a Matrix and Perform Matrix operations
 How to Initializing a Data Frame from Column Data & Row Data
 Editing a Data Frame
 Combining Two Data Frames
 Merging Data Frames by Common Column
 Converting One Atomic Value into Another
 Converting One Structured Data Types

DESCRIPTIVE STATISTICS

 – Data Exploration & Data Transformations (Bi-variate data, multi-variate data)
 – PCA (Principal Component Analysis)
 – Frequency Charts, Data crosstabulation
 – An introduction to packages needed in R to tidy the data
 – Exploring the R packaged datasets
 – Normal Forms of data
 – Data visualization
– Creating different types of charts (Bar, XY, histogram, box-plot) using ggplot2
– Plotting functions in R
– Plotting and colouring in R
– Working with R datasets
 – Basic data management / Advanced data management using dply r package
 – Splitting a Vector to Groups
 – Applying a Function to Elements, Row, Column, Group of Data, Vectors or Lists
 – String Concatenation, String Extraction, String Splitting, Replacing Substrings, data type conversions
 – Extracting Date parts
Conclusion: Get familiar with statistical terminology, represent data graphically, understanding basic frequency distribution, Measures of central tendency (grouped or un-grouped data), Measures of dispersion & Skewness (grouped or un-grouped data)

INFERENTIAL STATISTICS

 – Different Statistical tests of significance (Chi-square test, T tests, compare Mean (one sample Independent sample, Paired samples)
 – Correlation using Karl’s Pearson (Graphs, Scatter, Dot charts), analyse, correlate, Bivariate
 – Regression (Simple, Bi-variate and multi-variate)
 – General Linear Model (GLM)
 – One sample test of difference/One sample
 – hypothesis test
 – Confidence Interval
 – Contingency Tables and Chi Square Statistic
 – T-test or Anova

Conclusion: Get familiar with different statistical tests of significance, types of correlations, Regression methods and hypothesis testing using R for business applications.
CASE STUDY

 Company Valuation using Discounted Cash Flows (technique is widely used in investment projects on projected cash flows and then discounting it to Present Value finally to get the Net Present Value).

 

Business Analytics With R Programming Training in Pune

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