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Certificate Course on DataScience With R








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Certificate
Course
on
DataScience
With
R

Admission open now

Admission open now

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Short - Term
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Online / Classroom

Overview
Learning Objectives
FAQ
Data Science with R

Become an expert in data analytics using the R programming language in this data science certification training course. you will master data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. With this data science course, you will get hands-on practice by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, and many more.

This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc

  • According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
  • Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
  • Randstad reports that pay hikes in the analytics industry are 50% higher than the IT industry

Data Science with R

The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies.

Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.

Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.

What Skills you will Learn

    This data science training course will enable you to:

  • Gain a foundational understanding of business analytics
  • Install R, R-studio, and workspace setup, and learn about the various R packages
  • Master 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 DPYR functions
  • Understand and use the various graphics in R for data visualization
  • Gain a basic understanding of 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
Frequently asked Questions
1. Who should attend this course?

There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience. We recommend this Data Science training particularly for the following professionals:

  • IT professionals looking for a career switch into data science and analytics
  • Software developers looking for a career switch into data science and analytics
  • Professionals working in data and business analytics
  • Graduates looking to build a career in analytics and data science
  • Anyone with a genuine interest in the data science field
  • Experienced professionals who would like to harness data science in their fields
2. What type of Jobs we can expect after this training?

Upon completion of the Data Science with R course, you will have the skills required to help you land your dream job, including:

  • IT professionals
  • Data scientists
  • Data engineers
  • Data analysts
  • Project managers
  • Program managers
3. What are the Pre-requisites for this Training?

There are no prerequisites for learning this course. However, knowledge of any one programming language and SQL will be beneficial

4. Who will provide the certification?

Upon successful completion of the Data Science with R certification training, you will be awarded the course completion certificate from Kigyan

5. What are my system requirements?

The tools you will need to attend training are:

  • Windows: Windows XP SP3 or higher
  • Mac: OSX 10.6 or higher
  • Intel i3 with minimum 8 GB RAM
  • Internet speed: Preferably 512 Kbps or higher for online training
  • Headset, speakers, and microphone: you will need headphones or speakers to hear instructions clearly, as well as a microphone to talk to others. You can use a headset with a built-in microphone, or separate speakers and microphone
6. What are the training modes offered for this course?

We offer this training in the following modes:

  • Live Classroom training in our Training Centre
  • Live Virtual Classroom or Online Classroom: Attend the course remotely from your desktop via video conferencing to increase productivity and reduce the time spent away from work or home
7. Any Group Discount offered in this classroom training?

Yes, we have group discount options for our training programs. Contact us using the form on the right of any page on the website or send am mail with your requirement and the student count to shailaja@kigyan.com. Our customer service representatives can provide more details.

8. What payment options are available?

Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.

  • Any Credit or Debit Card
  • Bank Transfer using NEFT / RTGS
  • Direct payment in our centre through cash / cheque
  • Online payment wallet like Google Pay, PayTM….
Curriculam
Data Science with R
Introduction to Business Analytics
  • Types of Business Analytics
    • Descriptive
    • Diagnostic
    • Predictive
    • Prescriptive
  • Areas of Analytics
  • Business Decision Making
  • Business Intelligence (BI)
  • Data Science
  • Big data & Analytics
Introduction and Overview of R-Studio
  • Introduction to R
  • Downloading and Installing R-Studio
  • Coding Practices
  • Packages
Programming in R
  • Operator
  • Condition statements& loops
  • Running a R script
    • R Functions
    • Data Structures in R
    • Importing Data to R from various sources
    • Exporting Data from R
    • Apply Functions
Data Visualization in R
  • Graphics in R
    • Bar Chart
    • Pie Chart
    • Histogram
    • Kernel Density Plot
    • Line Chart
    • Box Plot
    • Heat Map
    • Word Cloud
  • File Formats for Graphic Outputs
  • Exporting Graphs in RStudio
Implementing Statistics in R
  • Introduction to Basic Statistics
  • Data Analysis & Concept
  • Scale of Measurement
  • Normal Distribution
  • Distance Measures
  • Determination of statistical techniques
  • Correlation
  • PPMC
Hypothesis Testing
  • Hypothesis
  • Null hypothesis
  • Alternate hypothesis
  • Error in statistical Decisions
  • Types of Statistical hypothesis test
    • One Tailed
    • Two Tailed
  • Parametric Tests
    • Z-Test or T-test
    • ANOVA
  • Types of Hypothesis Tests
    • Population Means
    • Population Proportions
    • Population Variances
  • Chi-Square Test
  • Degree of Freedom
Regression& Classification Analysis:
  • Introduction to Regression Analysis
  • Simple & Multiple Regression
  • Linear Regression Models
    • Simple Linear
    • Method of Least Squares
    • Coefficient of Multiple Determination
    • Standard error of the Estimate
    • Dummy Variable
    • Interaction
  • Non-Linear Models
    • Polynomial
    • Logarithmic
    • Square root
    • Reciprocal
    • Exponential
  • Introduction to Classification Analysis
  • Classification Process
    • Model construction
    • Model Usage
    • Data Preparation
    • Evaluating the model
  • Classification Techniques
    • Decision Tree
    • Naive Bayes Classifier
    • Nearest Neighbour
    • Support Vector Machines (SVM)
Clustring:
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN Clustring
Association:
  • Association Rule Mining
  • Strength of Association Rules
    • Support
    • Confidence
  • Apriori Algorithm
  • Master Basket Analysis
Text Analytics:
  • Natural Language Processing
  • Text Mining & Word Cloud
4.6
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  • I'm very happy to be the student of this institution, and the guide we got is very experienced and they trying to teach us the things from ground level.

  • More complex things are made to solve in an easier and understandable ways by Kiran Sir...Iam extremely happy to be the part of the internship...Thank you!

  • Internship was really good and we learnt many new things of current technology.