Data Analytical With R

About Course

This course gives an introduction to the field of business intelligence along with business analytics, which extensively use data, statistical and quantitative analysis, exploratory and predictive models, and fact-based management to take decisions and actions. Knowledge of R, SPSS and Excel is all the more important for interacting with Consultants should their help be needed for the organization. And, to facilitate statistical analysis of data to support the research.

IT Skills Training Services Provides the most advanced Data Science & Analytics training course that includes the various components like Basics of Statistics , machine learning, cluster analysis, data mining, cleansing, transformation, deploying data visualization among other things.

These professionals are skilled in automating methods of collecting and analyzing data and utilizing inquisitive exploring techniques to discover previously hidden insight from this data that can profoundly impact the success of any business.

The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R for practice.

Objectives of the Data Analytics with R Course

* Understand and use the various graphics in R for data visualization

* Install R, R-studio, and workspace setup. You will also learn about the various R package

* Master the R programming and understand how various statements are executed in R

* Gain an in-depth understanding of data structure used in R and learn to import/export data in R

* Define, understand and use the various apply functions and DPLYP functions

* Gain a basic understanding of the various statistical concepts

* Understand and use hypothesis testing method to drive business decisions

* Understand and use linear, non-linear regression models, and classification techniques for data analysis.

* Learn and use the various association rules and Apriori algorithm

* Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering

* Gain a foundational understanding of business analytics

Data Science Certification

This course is designed for clearing the CCP Data Scientist Cloudera Certification. The entire training course content is in line with this certification program and helps you clear it with ease and get the best jobs in the top MNCs. As part of this Data Science training course you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.

Prerequisites for Learning Data Science Certification Course

There is no specific pre-requisite for the course but exposure to core Java and mathematical ability are going to be helpful. IT Skills can give you complimentary self-paced courses covering necessities of Hadoop, R and driver to brush up the basics needed for the course.

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    Module 1: Introduction to Business analytics

    • Using variables in R
    • Using vectors
    • Performing multiple calculations with vectors
    • Taking input from the user
    • Storing and calculating values
    • Using the functions of R
    • Vectorizing the functions
    • Putting the arguments in a function
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    Module 2: Getting Started with Ariethmetic

    • Working with numbers, infinity and missing value
    • Doing basic arithmetic
    • Using mathematical functions
    • Organizing the data in vectors
    • Discovering the properties of the vector
    • Creating vectors
    • Combining vectors
    • Repeating vectors
    • Getting values in and out of vectors
    • Understanding the indexing in vectors
    • Changing the values inside the vector
    • Working with logical vectors
    • Comparing values
    • Using logical vectors as indices
    • Combining logical statements
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    Module 3: Reading and Writing with R

    • Using character vectors for data set Assigning values to character vector
    • Creating character vectors with more than one element
    • Extracting subset from the vector Naming the value in the vectors Manipulating text
    • Combining and splitting strings Sorting text
    • Finding text within the text Substituting text
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    Module 4: Adding more dimensions to R

    • Defining the matrix in R
    • Combining the vectors into a matrix
    • Using the indices with matrices
    • Extracting values from a matrix
    • Replacing values in a matrix
    • Naming the rows and columns in matrix
    • Calculating with matrices
    • Creating an array
    • Using dimensions to extract values
    • Combining different types of values in a data frame
    • Manipulating values in a data frame
    • Combining different objects in a list
    • Extracting elements from a list
    • Changing the elements in lists
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    Module 5: Functions

    • Moving from scripts to functions
    • Using the arguments in the functions
    • Controlling the scope of the functions
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    Module 8:Data processing and manipulations

    • Deciding the appropriate data structure
    • Creating subsets of the data
    • Sub setting the data frames
    • Adding calculated fields to the data
    • Combining and merging data sets
    • Sorting and ordering data
    • Traversing the data with the apply functions
    • Understanding the data in long and wide format
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    Module 11: Working with qualitative data

    • Frequency distribution of the quantitative data
    • Relative frequency
    • Cumulative frequency distribution
    • Cumulative distribution graph
    • Steam and leaf plot
    • Scatter plot
    • Central tendency- mean, median, mode
    • Variation- range, standard deviation, variance, inter-quartile range
    • Shape- skewness, kurtosis
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    Module 13: Interval estimates and predictions

    • Point estimates of mean
    • Interval estimate with known variance
    • Interval estimate with unknown variance
    • Sampling size for the population mean
    • Point estimate of population proportion
    • Interval estimate of population proportion
    • Sampling size for the population parameter
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    Module 14: Hypothesis Testing

    • Lower and upper tail test of population mean with known variance
    • Lower and upper tail test of population mean with unknown variance
    • Two tail test with known variance Two tail test with unknown variance
    • Lower and upper tail test of population proportion
    • Two tail test of population proportion
    • Type-I and Type-II errors in all above tests
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    Module 18: Simple linear regression

    • Estimated simple regression equation
    • Coefficient of determination
    • Confidence and prediction interval for linear regression
    • Significance test for linear regression
    • Residual pot and normal probability plot of residuals
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    Module 20: Logistic Regression

    • Estimated logistic regression equation for binomial logistic regression
    • Significance test for binomial logistic regression
    • Estimated logistic regression equation for ordinal logistic regression
    • Significance test for ordinal logistic regression
    • Estimated logistic regression equation for nominal logistic regression
    • Significance test for nominal logistic regression

Key Features

40 Hours of training with Excercises & Hands On assignments

Trainers are Industry Experts

Comprehensive up-to date contents

100%_Money-Back-Guarantee*(Refund in case of non-satisfaction on the first day of the class)

Pre-Assessment Text, Quizzes, assignments & projects, Mock Test, Practice test,

Batch Size will be not more than 15 Candidates.

Lifetime support will be given to the candidate through Whatsapp discussion forum, Mock tests, by sharing study material etc.

Course completion certificate

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    Who are the Instructors?

    • We believe in quality & follow a rigorous process in selecting our trainers. All our trainers are industry experts/ professionals with an experience in delivering trainings
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