Data Analytics (with R) Practice Exam Questions

Data Analytics (with R) Practice Exam Questions

R analytics (or R programming language) is amongst the free, open-source software used by professionals for all kinds of data science, statistics, and visualization projects. R programming language is amongst the most powerful, versatile languages with the ability to be integrated into BI platforms like Sisense, to assist with handling business-critical data.

Target Audience

• Software engineers
• Web developers
• Programmers
• Bachelor’s in science and CS

Skills Required

• Microsoft Excel.
• Critical Thinking
• R or Python–Statistical Programming.
• Data Visualization.
• Presentation Skills.
• Machine Learning.
• SQL

Career Prospects

• Data Scientist
• Data Engineer
• Data Analyst
• Machine Learning Engineer
• Data Journalist
• Database Admin
• Financial Analyst
• Business Analyst

Course Outline

The Data Analytics (with R) Practice Exam covers topics including -

Module 1 - Introduction

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

Module 2 - Descriptive Statistical Measures

• Learning
• Measures of Location
• Learning Measures of Dispersion
• Measures of Shape
• Learning Measures of Association
• Learning Excel Descriptive Statistics Tool

Module 3 - Probability Distributions

• Learning probability basis
• Discrete Probability Distributions
• Continuous Probability Distributions
• Learning Distribution Fitting

Module 4 - Sampling and Estimation

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

Module 5 - Statistical Inference

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

Module 6 - R Programming Language Introduction

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

Module 7- Reading Data from files

• The read.table() function
• Learning the scan() function
• Accessing Builtin Datasets
• Editing data

Module 8 - Probability Distributions

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

Module 9 - Statistical Models in R

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

Module 10 - R Graphics Facilities

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

Module 11 - R Data Import/Export

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