Data Analysis with R Practice Exam Questions

Data Analysis with R Practice Exam Questions


Data analysis is the upcoming source of numerous opportunities. It is focused on analysing data sets and then applying statistical techniques for making decisions and predicting the success of businesses. R software is widely used for data analysis and graphical representation of data. Thus, getting certified in Data analysis and R will create huge opportunities for career progression for you.


Uses of R

Linear and nonlinear modelling

Statistical tests

Classification

Time-series analysis

Clustering


Benefits

This exam has the several benefits such as-

1. Broadening your skill-set.

2. Enabling you to stand out in the competitive job environment.

3. Enhancing the value of your CV.


Target Groups

This exam is best-suited for-

Working Professionals

Programmers

Software developers

Graduates

Executive managers


Course Outline

The topics covered in this exam are as follows-

Introduction

Scope and Evolution

Data for Business Analytics

Understanding Decision Models

Problem Solving

Decision Making

Descriptive Statistical Measures

Understanding Statistical Notation

Measures of Location, Dispersion, Shape and Association

Excel Descriptive Statistics Tool

Probability Distributions

Basics of Probability

Understanding Discrete Probability Distributions

Knowledge of Continuous Probability Distributions

Distribution Fitting

Sampling and Estimation

About Sampling Methods

Statistical Sampling and Sampling Distributions

Estimation

Knowledge of Interval Estimates

Confidence and Prediction Intervals

Statistical Inference

Hypothesis Testing

One-Sample and Two-Sample Hypothesis Tests

ANOVA

R Programming Language Introduction

Introducing R

Objects and Arithmetic

Summaries

Matrices

Subscripting

Attaching to objects

Statistical Computation and Simulation

Graphics

Reading Data from files

Understanding theread.table() function

Knowing the scan() function

Accessing Builtin Datasets

Editing data

Probability Distributions

Understanding R as a set of statistical tables

Examination of the distribution of a set of data

One and two-sample tests

Statistical Models in R

Introduction to statistical models and formulae

Linear models

Generic functions for the extraction of model information

Analysing variance and model comparison

Updating fitted models

Generalized Linear Models

Nonlinear least squares 

Understanding maximum likelihood models

Some Non-Standard Models

R Graphics Facilities

Understanding High-Level Plotting Commands

Knowing Low-level plotting commands

Interaction with graphics

Use of graphics parameters

Graphics parameters list

Figure margins and Device drivers

R Data Import/Export

Imports

Spreadsheet-like data

XML

Importing from different statistical systems

Understanding Relational databases

Binary and Image files

Connections

Knowledge of Network interfaces

Reading Excel spreadsheets


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