Certificate in Data Analysis with R

Course details

Data analysis is the upcoming domain offering huge opportunities. Data analysis is focused on analyzing data sets, applying statistical and analytical techniques for decision making and prediction for business success.

R is a open source programming language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc) and graphical techniques, and is highly extensible.

R is extensively used for statistical computations, data analysis and graphical representation of data. Candidates with certification in Data analysis with R, can tap into massive opportunities of employment and career progression.

Why should one take Data Analysis with R Certification?
The certification is suitable for programmers, software developers, senior and executive management wanting to excel in data analysis. It is also well suited for those who are already working and would like to take certification for further career progression in data analytics.

Earning Vskills Data Analysis with R Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential.

Who will benefit from taking Data Analysis with R Certification?
Job seekers looking to find employment in IT or software development or analysis departments of various companies, role in decision making, performance improvement and optimization in MNCs, corporate, senior management role.

Students who want to learn Data Analysis with R and get recognition of their data analysis skills using R programming language should also take this certification.
 
Benefits of Certification

• Government certification
• Certification valid for life
• Lifelong e-learning access
• Learning Hours: 12+ hrs

 
How It Works

1. Select Certification & Register
2. Receive Online e-Learning Access (LMS)
3. Take exam online anywhere, anytime
4. Get certified & Increase Employability

Test Details

• Duration: 60 minutes
• No. of questions: 50
• Maximum marks: 50, Passing marks: 25 (50%).
• There is NO negative marking in this module.
• Online exam.

 
TABLE OF CONTENT

Introduction

• Evolution and Scope
• Data for Business Analytics
• Decision Models
• Problem Solving and Decision Making

Descriptive Statistical Measures

• Statistical Notation
• Measures of Location
• Measures of Dispersion
• Measures of Shape
• Measures of Association
• Excel Descriptive Statistics Tool

Probability Distributions

• Probability Basics
• Discrete Probability Distributions
• Continuous Probability Distributions
• Distribution Fitting

Sampling and Estimation

• Sampling Methods
• Statistical Sampling
• Sampling Distributions
• Estimation
• Interval Estimates
• Confidence Intervals
• Prediction Intervals

Statistical Inference

• Hypothesis Testing
• One-Sample Hypothesis Tests
• Two-Sample Hypothesis Tests
• ANOVA

R Programming Language Introduction

• What is R?
• Objects and Arithmetic
• Summaries and Subscripting
• Matrices
• Attaching to objects
• Statistical Computation and Simulation
• Graphics

Reading Data from files

• The () function
• The scan() function
• Accessing Builtin Datasets
• Editing data

Probability Distributions

• R as a set of statistical tables
• Examining the distribution of a set of data
• One- and two-sample tests

Statistical Models in R

• Defining statistical models; formulae
• Linear models
• Generic functions for extracting model information
• Analysis of variance and model comparison
• Updating fitted models
• Generalized Linear Models
• Nonlinear least squares and maximum likelihood models
• Some Non-Standard Models

R Graphics Facilities

• High-Level Plotting Commands
• Low-level plotting commands
• Interacting with graphics
• Using graphics parameters
• Graphics parameters list
• Figure margins
• Device drivers

R Data Import/Export

• Imports
• XML
• Spreadsheet-like data
• Importing from other statistical systems
• Relational databases
• Binary files
• Image files
• Connections
• Network interfaces
• Reading Excel spreadsheets

Updated on 19 December, 2023