Predictive Analytics With R

Name: Predictive Analytics With R
Rating: 3.8 (74 reviews)

Enhance you analytics by Predictive Analytcis with R. Become an Analyst with easy programming code of R.

Last updated 2/2016

English

What you'll learn

Understand the fundamentals of Predictive Analytics in R.
Enhance skills by applying them to Model Data.
Get accustom to Predictive Analytics as career option with practical knowledge on some of the techniques that are currently in demand, such as Hypothesis Testing, Linear Regression, Multiple Regression, Logistic Regression, Correlations, Chi-Square Test etc.
Use Predictive Modelling Techniques on different types of data.
Work with different types of data and perform Data Manipulation and Preparation.
Develop constructive approach to solve business queries with R.
Analyze real time data and perform learnt skills.

7 sections • 44 lectures • 4h 13m total length

Group Manipulation Concept2:07
Master group manipulation in predictive analytics with R by learning to split data into sections by metrics, apply transformations to each group, and recombine using aggregate, filter, and select functions.
Group Manipulation Hands On17:44
Aggregate Function10:36
Dplyr Package23:30
String Manipulation Concept1:10
String Manipulation Hands On14:42
Transform and Split5:49
Learn to transform data in R by modifying an existing column with the transform function, and split data by a column such as age or gender, without adding new columns.
Handling missing values8:28
Assignment Questions1:00

Hypothesis Test Concept14:01
Hypothesis Testing Hands On5:20
Explore hypothesis testing with mean and standard deviation, p-values at 5% significance, and decisions to reject or fail to reject the null hypothesis using a distribution function.
Chi-square Test Concept1:42
Chi-Square Test Hands On10:56
Apply the chi-square goodness-of-fit test in R to compare observed eye-color frequencies to population proportions, using marginal and proportional tables to evaluate distribution.
T-Test Concept0:55
T-Test Hands On7:22
Apply a two-sample t-test in R to compare sleep outcomes between groups using the sleep dataset, and interpret p-values and confidence intervals to assess significance.
Z-Test Concept3:05
Z-Test Hands On9:38
Perform a z-test in R to assess medication effects on intelligence, defining hypotheses, setting a 5% significance level, computing the z-score and p-value, and interpreting the 95% confidence interval.
Assignment Questions2:00

Introduction to Regression Analysis2:29
Linear Regression Concept2:08
Implementing Linear Regression in R13:20
Multiple Regression Concept1:52
Implementing Multiple Regression Concept in R15:56
Logistic Regression Concept5:00
Implementing Logistic Regression Concept in R16:28
Learn to implement logistic regression in R with a graduate admissions example using GPA and prestige, including data prep, fitting a binomial glm, interpreting coefficients, and generating predictive probabilities.
Assignment Questions3:00

You should have some basic idea about R tool and its objective
If you have knowledge about basic statistics previously, it would be an added advantage for you.

In this course you will learn about predictive analytics using R language

It starts with an introduction to the predictive modelling along with its application.
Also you learn about R and and how R is used for Predictive modelling
You will also design statistical experiments and analyze the results using modern methods
You will also learn Data manipulation methods and predictive Modelling techniques in R.
Collectively, this course will help you internalize a core set of practical and effective predictive analytics methods and concepts, and apply them to solve some real world problems.
This course contains lectures as videos along with the hands-on implementation of the concepts, additional assignments are also provided in the last section for your self-practice.