Course Overview
Description– Data Science with R programming course creates you to associate skilled knowledge analytics victimization with the R programming language. This Data science with R course allows you to require your Data Science skills into a range of firms, serving to analyze knowledge and create additional advised business selections. With dedicated mentoring sessions, this R course incorporates up-to-date information to assist you develop job-ready skills. register in our R coaching and build experience in Data science right now .
Overview– The Data Science with R certification course covers knowledge exploration, knowledge visualization, prophetic analytics, and descriptive analytics techniques with the R language. you'll find out about R packages, a way to import and export knowledge in R, knowledge structures in R, numerous applied math ideas, cluster analysis, and prediction.
Key Features
- Introduction to basics of R programing language.
- Install and configuration of R
- Learn and apply R for information science solutions.
- Guidance for certification and Guidance in resume building for R programming.
Who should take this course?– This R programming course advantages professionals who are already into programming and wish to alter within the R atmosphere. People with data or statistics also can pursue a career in R programming. Beginners will follow this course. People who have an interest in obtaining certification also can try this course. Randstad reports that pay hikes within the analytics business area unit 50-percent on top of the IT business. Learning R will assist you begin a career in Data science.
Pre-requisites– Here are a number of the technical ideas you should know before getting into to learn Data science with R. –
- 1.Machine Learning.
- 2.Modeling
- 3.Statistics.
- 4.Programming
- 5.Databases
Course Syllabus
Intro to Data Science with R Programming
Learning Objectives:
Get an overview of the world of data science. Get acquainted with various analysis and visualization tools used in data science.
Topics
Hands-on: No hands-on
Hands-on:
Probability & Statistics
Learning Objectives:
This module explores basics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range and get basic summaries about data and its measures, together with simple graphics analysis.
Through daily life examples, you will understand the basics of probability, marginal probability and its importance with respect to data science. Learn Baye’s theorem and conditional probability, and alternate and null hypothesis including Type1 error, Type2 error, power of the test, and p-value.
Topics
Advanced Statistics & Predictive Modeling - I
Learning Objectives:
This module analyses Variance and its practical use, covering strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. You will use Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. Further you will learn to enhance model performance by means of various steps like feature engineering & regularization.
Along the way, you will learn about Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis, including methods to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. You will be able to cement the concepts learnt through real life case studies with Linear Regression and PCA & FA.
Topics
Hands-on: