Data Science Certification Course using R

Integrated approach to data science with R programming language

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Key Highlights

  • 30 hours of Instructor-led online live sessions
  • 10 sessions of 3 hours each (Weekend)
  • 15 sessions of 2 hours each (Weekday)
  • Complimentary Self-paced course of ?R Essentials?
  • Case Studies on Real-life Scenarios
  • Lifetime Access to Learning Management System
  • Practice Assignments
  • 24X7 Expert Support
  • Online Forum for Discussions

Course Price Range

$479.00 - $479.00 $900.00 - $900.00

Contact Us

1866-216-7898

(Toll Free)

Available Courses Delivery

This course is available in the following formats:

Virtual Live

Access live online training from anywhere taught by expert instructors
Search and study from listed class recordings and materials
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Upcoming Batches


Nov 22nd
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Delivery: Online
Access: Lifetime
Fri - Sat (5 Weeks)
Timings - 08:30 PM to 11:30 PM (EST)

Weekend Batch (Evening)
$900  $479
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Course Overview

Data Science certification course using R is meant for professionals who want to gain expertise in data science using R programming language. The application of R language in data analysis has a role in machine learning algorithm and statistical inference. Learners are equipped with the tools and techniques for examining, cleaning, and converting data to gather useful information.

Course Objectives

  • Train in R language, R-studio and R packages
  • Teach the entire process of examining, cleaning, and converting data with the application of R programming
  • Acquaint learners with the Data Life Cycle and Machine Learning Algorithms
  • Make learners adept at advanced statistical concepts
  • Teach various tools and techniques for data transformation
  • Equip learners with real-life projects and cast studies to impart practical knowledge

Career Benefits

  • Excellent opportunity to build a successful career in Data and Analytics
  • Be considered as the maven of business analytics and R programming
  • Higher paycheck
  • Remain in demand to work on multiple disciplines
  • Become the master in dealing with real-life data-related issues

Prerequisites

  • None

Who should take up?

  • IT professionals with a keen interest in Data and Analytics
  • Analytics Managers
  • Business Analysts
  • Information Architects
  • R professionals
  • Professionals who wish to enter Data Science field

Course Content

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Big Data and Hadoop
  • Introduction to R
  • Introduction to Spark
  • Introduction to Machine Learning
  • What is Statistical Inference?
  • Terminologies of Statistics
  • Measures of Centers
  • Measures of Spread
  • Probability
  • Normal Distribution
  • Binary Distribution
  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Supervised Learning algorithm: Linear Regression and Logistic Regression
  • What are classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • What is Navies Bayes?
  • Support Vector Machine: Classification
  • What is Clustering & its use cases
  • What is K-means Clustering?
  • What is C-means Clustering?
  • What is Canopy Clustering?
  • What is Hierarchical Clustering?
  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it?s working?
  • Types of Recommendations
  • User-Based Recommendation
  • Item-Based Recommendation
  • Difference: User-Based and Item-Based Recommendation
  • Recommendation use cases
  • The concepts of text-mining
  • Use cases
  • Text Mining Algorithms
  • Quantifying text
  • TF-IDF
  • Beyond TF-IDF
  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting
  • Deep Learning
  • Reinforced Learning
  • Reinforcement learning Process Flow
  • Reinforced Learning Use cases
  • Deep Learning
  • Biological Neural Networks
  • Understand Artificial Neural Networks
  • Building an Artificial Neural Network
  • How ANN works
  • Important Terminologies of ANN's

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