Course Description
This course ensure quick
learning in a simplified way. It explains the most important aspects of
working on data and conduct analysis through example. You will start by
learning how to install and navigate R studio. Learn Data/Object Types
and Operations, Importing into R, and Loops and Conditions. you will be
introduced to the use of R in Analytics, where you will learn a little
about each object type in R and use that in Data Mining/Analytical
Operations. learn the use of R in Statistics, using R to evaluate
Descriptive Statistics, Probability Distributions, Hypothesis Testing,
Linear Modeling, Generalized Linear Models, Non-Linear Regression, and
Trees. Learn to create 2-dimensional Univariate and Multi-variate plots.
You will also learn about formatting various parts of a plot, covering a
range of topics like Plot Layout, Region, Points, Lines, Axes, Text,
Color and so on. If you want to learn R for use in data analytics,
statistics and data science, you can count on this course to take you
from beginner to an advanced level. Start off with the basics of R and
dive deep into how to use R for statistics, vector arithmetic,
generating regular sequences, logical filters, and more. Data is useless
if you do not have the right tools to build informative graphs. Plots
need to be understood easily while being accurate at the same time. R
Programming for Business Analytic offers a variety of plotting devices,
some of them are whole systems which need to be learned like a new
language.
Curriculum
Working With Data
Arima Steps
Array and Matrix
Basic R Functions
Basics of R
Binomial Distribution Continued
Business Analytics Life Cycle
Business Analytics Using R
Business Example Hotel
Calculating Covariance
Calculating the Z Value
Calculating Variance
Central Limit theorem
Computer Lab Example
Confidence intervals Examples
Confidence intervals for the Mean
Constructing Central Limit theorem
Continuous Case
Control Flow
Course Curriculum
Cumulative Frequency
Data Aggregation
Data Creation
Data for Business Analytics
Data Manipulation and Statistics Basics
Data Types
Deal Or No Deal
Decision Model Example
Descriptive Decison Models
Discrete Example
Discriminant Analysis
Distribution Details
Dnorm, Pnorm, Qnorm
Double Exponentional Smoothing
Downloading the Package
Efficacy Test for New Drugs
Evolution of Business Analytics
Example- Birth Weights
Expected Value
Expected Value From Binomial
Exponential Distribution Practice Problem
Factor
forecasting
forecasting Performance
Gambling Example
Getting Started With R
Head (Faithful)
Hypothesis Generation and Testing
Hypothesis Testing
Importing Data
Importing Data Spss
installing A Package
installing R Studio
Introduction
Introduction to R and Analytics
Kurtosis
Library (Mass)
Logical Conjunctions
Lower Tail Proportion of Population Proportion
Merge Example
Merging
Model Deployment
Normal Pdf
Observation Components
One Sided P Value
Ordinal Data
Parallel Summary Functions
Pasting Strings together
Power and Sample Size
Practice Problem
Probability Distributions Examples
Probability Distributions Examples Continues
Properties of Good Estimators
Quantiles
R Examples
Random Example
Random Variable
Recycling Rule
Repository and Packages
Sample Differences
Sat Example
Scatter Plot
Section 1: Introduction
Section 10: Understanding Estimation
Section 11: Hypothesis Testing and R
Section 12: Forecasting
Section 2: Basic Concepts on R Programming and Business Analytic
Section 3: Ordinal Data and Decision Models
Section 4: Getting Started With R
Section 5: Working with Data
Section 6: Data Statistics
Section 7: Statistics, Probability and Distribution
Section 8: Distribution Details
Section 9: Business Analytics Using R
Software Used in Business Analytics
Special Numerical Values
Standard Error of the Mean
Statistics, Probability and Distribution
Steps in Problem Solving Process
T-Distribution
T-Distribution Continued
Testing Hypothesis Using R
Time Series Analusis Applications
Time Series Analusis Applications Continue
Traditional Approaches
Type Coercion
Understanding Estimation
Uniform Random Variables
Univariate Arima
Variables
What is Normal, Not Normal
What is Statistics
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