Data Science -Business Analytics in R

LIVE ONLINE INTERACTIVE
Attend our Instructor Led Live OnLine Interactive Training
32 Hours

Total : 32 Hours

Price : INR 25000

R Language Training Overview

Educonverge provides the live online interactive classes on Business Analytics course in Delhi NCR.

R programming language is one the most powerful tool for computational statistics, visualization and data science. Data scientists and statisticians use R for solving many complex problems in their industry. R is extensively used in companies like Bing, Google, Facebook, Twitter and Uber. As R is used in various domains like Social media companies, Banks, Insurance companies, Car manufacturers, R is one of the most sought data analytics skill that is in high demand. R Programming is a powerful statistical the programming language which is used for predictive modeling and other data mining related techniques. R programming can be used for data manipulation, data aggregation, statistical Modelling, Creating charts and plots. R programming is becoming the most necessary skill in the field of analytics for its open source credibility.

Objectives of the Course

  • Understand programming fundamentals of R language
  • Understand various data import methods in R
  • Understand the Data Manipulation in R
  • Create visualizations and Plots using R
  • Understand and Implement Linear Regression
  • Perform Text Analysis
  • Understand Machine Learning concepts
  • Real-time implementation of R on a live project and provide Business Insights

Pre-requisites of the Course

  • Programming background like C, C++, Python will be an added advantage but not mandatory to learn R, but introductory statistics is a prerequisite.

Who should do the course

  • Software engineers and data analysts
  • Business intelligence professionals
  • SAS developers wanting to learn open source technology
  • Those aspiring for a career in data science
  • Professionals and Students looking to enter the Data Science industry

Data Science -Business Analytics in R

 

 

COURSE SYLLABUS                                                          Duration -32 hrs.

Objective

To successfully compete in today’s global business environment an organization must constantly monitor, recognize and understand every aspect and every issue of its operations, its industry and the overall business environment. This course focuses on Data Science -Business Analytics – an information technology approach to data collection and data analysis to support a wide variety of management tasks, from performance evaluation to trend spotting and policy making. Students learn analytical components and technologies used to articulate knowledge, data/text/Web mining methods for trend and marketing analytics , HR analytics, sentiment analysis, and artificial intelligence techniques used to develop intelligent systems for decision support. Students will actively participate in this course through class assignments, and project preparation.

Learning Goals

Upon successful completion of this course, students will be able to:

  • Articulate modern concepts, theories, and research in the field of Data Science, machine learning and business analytics.
  • Apply Data Science-Business Analytics enabling technologies in organizational settings.
  • Articulate modern Data Science-Business Analytics practices, including knowledge integration, sourcing and managing BI solutions.
  • Articulate the crucial role that automation and Business Intelligence plays in careers as well as in business and society

Domain Exposure

  • Marketing and Retail Analytics
  • Web & Social Media Analytics
  • Finance & Risk Analytics

Module 1                                                               

  • Introduction to Data Science & Business Analytics
  • Introduction to Data Science& Analytics
  • History of Analytics
  • Generations of Business Analytics
  • Analytics market growth
  • Landscape of Analytics and its importance in Business

Module 2        

  • Intro to Statistics –Descriptive & Inferential statistics
  • Statistical Inference
  • Terminologies of Statistics
  • Descriptive statistics
  • Statistical functions
  • Measures of Centers
  • Mean
  • Median
  • Mode
  • Measures of Spread
  • Variance
  • Standard Deviation
  • Range
  • Histogram
  • Probability
  • Normal Distribution
  • Binary Distribution
  • Poisson distribution
  • Bernoulli’s Distribution
  • Skewness
  • Bell curve
  • Hypothesis Building and Testing
  • Z-Score
  • Critical value
  • t-test
  • p-value
  • Chi-Square Test
  • Correlation Analysis
  • Regression Analysis

Module 3

Basics of R

  • Math, Variables, and Strings
  • Vectors and Factors
  • Vector operations

Operators in R Programming

  • Arithmetic Operators
  • Comparison Operators
  • Logical Operators

Data structures in R

  • Arrays & Matrices
  • Lists
  • Dataframes

R programming fundamentals

Loops and conditions

  • If Statement
  • If Else Statement
  • Nested If Else Statement
  • Else If Statement
  • Switch Statement
  • Break Statement
  • Next Statement
  • While Loop
  • For Loop

Functions in R

  • Inbuilt functions
  • Math funtions
  • Objects and Classes
  • Debugging

 Working with data in R

  • Reading CSV and Excel Files
  • Reading text files
  • Writing and saving data objects to file in R
  • Repeat Loop

R Objects

  • Vectors
  • Matrix
  • Arrays
  • Lists
  • Data Frame

Data Sources

  • Read Data from SQL Server
  • Read Data from CSV File
  • Import Data From Text File using read.table

R Tutorial on Charts & Graphs

  • Bar Chart
  • Stacked & Clustered Bar Chart
  • Pie Chart
  • Scatter Plot
  • Histogram
  • Boxplot
  • Stem and Leaf Plot

R ggplot2 tutorial

  • Boxplot
  • Density Plot
  • Dot Plot
  • Histogram
  • Jitter
  • Line Plot
  • Scatter Plot
  • Violin Plot
  • Save R ggplot

R Lattice tutorial

  • Histogram
  • Scatter Plot
  • Bar Chart

 

Strings and Dates in R

  • String operations in R
  • Regular Expressions
  • Dates in R

Data Cleaning

Loading data into R from various formats,

  • Data inspection (e.g. looking for missing values, checking data types),
  • Calculation of new variables,
  • How to change and manipulate data structures (e.g. merging data frames, long and wide formats).

Descriptive Statistics in R

  • calculate and display basic descriptive statistics,
  • divide and aggregate data in different ways.
  • visualization tools.

Basics of Graphing

  • ggplot package
  • ggplot to make basic graphs and plots

Statistical Models in R

Cran –R

Dplyr package

Module 4

Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning

  • Machine Learning Languages, Types, and Examples
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning

Introduction to UCI Machine Learning Repository  & Kaggle

Module 5

Supervised Learning I

  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees
  • Implementing supervised Machine learning using R

Module 6

  Supervised Learning II

  • Regression Algorithms
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models
  • Implementing supervised Machine learning using R

Module 7

 Unsupervised Learning

  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Implementing unsupervised Machine learning using R

Module 8

 Dimensionality Reduction

  • Dimensionality Reduction: Feature Extraction & Selection

Sentiment Analysis & Web Scraping

  • Social Media Sentiment Analysis
  • Using Twitter data
  • Web scraping
  • Using web scraping data for analysis

Module 9

Time Series Forecasting

  • Importance of TSA
  • Components of TSA
  • Regression-based trend models.
  • The random walk model.
  • Stationarity
  • Autoregressive and moving average models.
  • Exponential smoothing
  • Seasonal models
  • ARIMA
  • SARIMA
  • ACF & PACF

Industrial case study

Capstone Project

GET CERTIFICATION AFTER THE ASSESSMENT TEST CLEARED

Fees

 

Application Fees INR 0
Total Program Fees INR 25,000
The Program Fees can also be paid in installments
Registration  Fees INR5000
First installment of the Program Fees

(Needs To Be Paid Before Batch Start Date)

INR 10,000
Second installment of the Program Fees

(Needs To Be Paid in the 6th Class)

INR 10,000
Scholarship Up to 50% Available

10%  Extra discount on Lumpsum Payment

 

How I can practise?

Detailed installation of required software will be displayed in your LMS. Our support team will help you to setup software if you need assistance. Hardware requirements need to be fulfilled by participants

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