Data Science with R

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Course Description

This course will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it. In this course, you’ll find plenty of required skills to do the Data Science projects.

Just as a gardener grows plants and makes a garden look pleasant, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the interactive graphics, literate programming, and reproducible research to save time.

Course Features:

Data Science with RWho is this course for?

  • Analytics professionals who want to fasten their growth path
  • IT and Software professionals who are looking to get into the field of Analytics.
  • Students and graduates who want to start their career with Analytics, or
  • Anyone who wants to get started with Analytics

Pre-requisites:

  • No prior knowledge of programming is assumed.
  • No prior knowledge of any subject is assumed.

Become a Data Scientist

Data science is the new emerging field in today world with high growth and career perspective. It’s right time to move your career in data science by starting from basics of Business Intelligence, Business Analytics, Information and management to advanced topics like in Machine Learning with industrial case study. This course provides in-depth understanding of how data science integrates in various industrial verticals such as healthcare, banking, telecom, e-commerce, transportation and more.
Introduction to Data Science
Duration
Module 1 2 Hrs
Introduction to Business Intelligence
Definition of Data Science
Data Science involvement in Industries
Introduction to Business Analytics
Understanding Business Analytics and R
Module 2 2 Hrs
Introduction to R programming
Compare R with other software in analytics
Install R
Perform basic operations in R using command line
Learn the use of IDE R Studio
Use the ‘R help’ feature in R
Variables in R
Scalars
Vectors
Matrices
List
Data frames
Using c, Cbind, Rbind, attach and detach functions in R
Factors
Module 3 2Hrs
Data cleaning Functions
Data sorting
Find and remove duplicates record
Cleaning data
Recoding data
Merging data
Slicing of Data
Merging Data
Control structures
Apply functions
Module 4 2 Hrs
Loop functions
Lapply()
Sapply()
vapply
Split()
Splitting a data frame
Col/row sums and Means
Vectorising a function
Module 5 2 Hrs
Primary R functions
Grep()
Grepl()
Regexpr()
Sub() and gsub()
Regexec()
Generating random numbers
Setting the random number seed
Random sampling
Module 5 2 Hrs
Data importing techniques
Reading Data
Writing Data
Basic SQL queries in R
Web Scraping
Module 6 3 Hrs
Managing Dataframes with the dplyr package
Dataframe
Dplyr package
Dplyr grammer
Installing dplyr grammer
Select()
Filter()
Arrange()
Rename()
Mutate()
Groupby()
Module
Packages
Data reshaping
Module 7 3 Hrs
Exploratory Data Analysis
Box plot
Histogram
Pareto charts
Pie graph
Line chart
Scatterplot
Developing Graphs
Ggplot2
Module 8 3 Hrs
Basics of Statistics
Inferential statistics
Probability
Hypothesis
Standard deviation
Outliers
Correlation
Module 9 2 Hrs
Regression
Types
Linear Regression
Logistic Regression
Module 10 3 Hrs
Classification
Data Mining
Clustering Techniques
Introduction to Data Mining
Understanding Machine Learning
Supervised and Unsupervised Machine Learning Algorithms
K- means clustering
Implementation with Case study
Module 11 3 Hrs
Decision Trees and Random Forest
Decision Tree
Concepts of Random Forest
Working of Random Forest
Features of Random Forest
Implementation with Case study
Module 12 4 Hrs
Anova & Sentiment Analysis
Analysis of Variance (Anova) Technique
Time Series analysis
Time Series forecasting
Implementation with Case study
Module 13 2 Hrs
Association rule
Association Analysis
Association Rule Parameters
Apriori Algorithm
Market Basket Analysis
Implementation with Case study
At the End implementation of any one of the projects could be assigned. 4 hrs
1. Customer churn prediction
2. Bank fraud Loan Prediction
3. Wine Type Prediction
4. Marketing channel sales prediction
5. People retention prediction
Course Highlights:
Use case based study
Hands on live projects
Study Material
Interview questions
Mock interview

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Fees

 

Application Fees INR 0
Total Program Fees INR 30,000
The Program Fees can also be paid in installments
Registration  Fees INR 5000 (Non-Refundable)
First installment of the Program Fees

(Needs to Paid One Week Before the Batch Start Date)

INR 12,000
Second installment of the Program Fees

(Needs To Be Paid in the 6th Class)

INR 13,000
 

Discount on Program Fees: 10% Early Bird Discount valid till 10/02/19

10%  Extra discount on Lumsum Payment

Batch Closed:             27th Jan 2019

New Batch Starting:  23rd Feb 2019 (Few Seats Left)

2nd Batch:                   25th Feb 2019

Admission Open Till: 10th Feb 2019 ( If Required Seats Filled, the admissions will be closed even before the Closing Date)

 

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