Data Science with R Programming

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Data Science R programming Course Singapore

R Programming Course in Singapore

Why Should I Learn Data Science with r programming course in Singapore from Inspizone?

This r programming course in Singapore 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

What are the course objectives?

The Data Science with R programming course 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 you will learn in this data science course?

This data science training course will enable you to:
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  • 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 in-depth understanding of data structure used in R and learn to import/export data in R
  • Define, understand and use the various application 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

Who should take this Online Data Science Training 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

Prerequisites: There are no prerequisites for this data science online training course. If you are new in the field of data science, this is the best course to start with.

Duration: 1 Days

Venue: 10 Anson Road, 26-08A International Plaza, Singapore 079903

   Data Science R Programming Certification Course Outline:

Overview

  • History of R Programming
  • Advantages and disadvantages
  • Downloading and installing

Introduction

  • Using the R console
  • Learning about the environment
  • Writing and executing scripts
  • Object oriented programming
  • Installing packages
  • Working directory
  • Saving your work

Variable types and data structures

  • Variables and assignment
  • Data types
  • Numeric, character, boolean, and factors
  • Data structures
  • Vectors, matrices, arrays,
  • Assigning new values
  • Viewing data and summaries

Base graphics system in R

  • Scatterplots, histograms, barcharts, box and whiskers, dotplots
  • Labels, legends, titles, axes
  • Exporting graphics to different formats
  • eneral linear regression
  • Linear and logistic models
  • Regression plots
  • Interaction in regression
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