Regression, Data Mining, Text Mining, Forecasting using R

Regression, Data Mining, Text Mining, Forecasting using R, available at $54.99, has an average rating of 4.9, with 192 lectures, 14 quizzes, based on 532 reviews, and has 4672 subscribers.

You will learn about Learn about the basic statistics, including measures of central tendency, dispersion, skewness, kurtosis, graphical representation, probability, probability distribution, etc. Learn about scatter diagram, correlation coefficient, confidence interval, Z distribution & t distribution, which are all required for Linear Regression understanding Learn about the usage of R for building Linear Regression Learn about the K-Means clustering algorithm & how to use R to accomplish this Learn about the science behind text mining, word cloud & sentiment analysis & accomplish the same using R This course is ideal for individuals who are All the IT professionals, whose experience ranges from '0' onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course. It is particularly useful for All the IT professionals, whose experience ranges from '0' onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course.

Enroll now: Regression, Data Mining, Text Mining, Forecasting using R

Summary

Title: Regression, Data Mining, Text Mining, Forecasting using R

Price: $54.99

Average Rating: 4.9

Number of Lectures: 192

Number of Quizzes: 14

Number of Published Lectures: 181

Number of Published Quizzes: 13

Number of Curriculum Items: 206

Number of Published Curriculum Objects: 194

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Learn about the basic statistics, including measures of central tendency, dispersion, skewness, kurtosis, graphical representation, probability, probability distribution, etc.
Learn about scatter diagram, correlation coefficient, confidence interval, Z distribution & t distribution, which are all required for Linear Regression understanding
Learn about the usage of R for building Linear Regression
Learn about the K-Means clustering algorithm & how to use R to accomplish this
Learn about the science behind text mining, word cloud & sentiment analysis & accomplish the same using R

Who Should Attend

All the IT professionals, whose experience ranges from '0' onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course.

Target Audiences

All the IT professionals, whose experience ranges from '0' onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course.

Data Science using R is designed to cover majority of the capabilities of R from Analytics & Data Science perspective, which includes the following:

Learn about the basic statistics, including measures of central tendency, dispersion, skewness, kurtosis, graphical representation, probability, probability distribution, etc.
Learn about scatter diagram, correlation coefficient, confidence interval, Z distribution & t distribution, which are all required for Linear Regression understanding
Learn about the usage of R for building Regression models

Learn about the K-Means clustering algorithm & how to use R to accomplish the same
Learn about the science behind text mining, word cloud, sentiment analysis & accomplish the same using R
Learn about Forecasting models including AR, MA, ES, ARMA, ARIMA, etc., and how to accomplish the same using R
Learn about Logistic Regression & how to accomplish the same using R

Course Curriculum

Chapter 1: Introduction To Data Science

Lecture 1: Introduction

Lecture 2: Data Generation And Information Age

Lecture 3: Big Data And Getting Drenched In Data

Lecture 4: Why Data Science…..?

Chapter 2: Basic Statistics

Lecture 1: Data Types And Preliminaries

Lecture 2: Random Variable

Lecture 3: What Is Probability…?

Lecture 4: Probability Distribution

Lecture 5: Recap Of Concepts And Probability Applications

Lecture 6: Sampling Funnel

Lecture 7: Measures Of Central Tendency

Lecture 8: Measures Of Dispersion

Lecture 9: Measures Of Dispersion Part-2

Lecture 10: Excpected Value And Variance For Discrete Data

Lecture 11: Preliminaries Of R and RStudio

Lecture 12: Various Components And Basics Of RStudio

Lecture 13: Data Visualization Using R-Barplot,Histogram,Skewness

Lecture 14: 3rd And 4th Moment Business Decision

Lecture 15: Recap And Box Plot

Lecture 16: Normal Distribution-Part 1

Lecture 17: Normal Distribution-Part 2 & Standarad Normal Distribution

Lecture 18: Standard Normal Distribution -Part 2

Lecture 19: Calculating Probabilities From Z-Distribution

Lecture 20: Sampling Variation, Sample size & Central Limit Theorem

Lecture 21: Normal Quantile Plot (Q-QPlot)

Lecture 22: Recap Confidence Interval

Lecture 23: Confidence Interval Z-Distribution Part-1

Lecture 24: Confidence Interval Z-Distribution Part-2

Lecture 25: Confidence Interval Interpretation

Lecture 26: Confidence Interval T-Distribution

Lecture 27: Recap Basic Statistics

Lecture 28: Confidence Interval In Data Science

Lecture 29: EXPLORATORY DATA ANALYSIS IN DATA SCIENCE

Chapter 3: Hypothesis Testing Introduction

Lecture 1: Hypothesis Testing Introduction

Lecture 2: Hypothesis Testing Formulation

Chapter 4: Hypothesis Testing- Parametric

Lecture 1: 2 Sample T-Test Part-1

Lecture 2: 2 Sample T-Test Part-2

Lecture 3: 2 Sample T-Test Part-3

Lecture 4: 1 Sample Z Test Part-1

Lecture 5: 1 Sample Z Test Part-2

Lecture 6: 1 Sample T Test

Lecture 7: One Way ANOVA Part-1

Lecture 8: One Way ANOVA Part-2

Lecture 9: One Way ANOVA Part-3

Lecture 10: ANOVA-Intuition Part 1

Lecture 11: ANOVA-Intuition Part 2

Lecture 12: ANOVA-1,2 Multiple Way

Lecture 13: Tukey Pairwise Comparisons Part-1

Lecture 14: Tukey Pairwise Comparisons Part-2

Lecture 15: 2 Proportion Test

Lecture 16: Chi Square Test

Chapter 5: Hypothesis Testing-Non Parametric

Lecture 1: 1 Sample Sign Test

Lecture 2: Mann Whitney Test

Lecture 3: Paired T Test Assumption

Lecture 4: Paired T Test

Lecture 5: Moods Median Test

Chapter 6: BASICS OF R-PROGRAMMING

Lecture 1: Basic Programing Using R Part 1

Lecture 2: Basic Programing Using R Part 2

Lecture 3: Basic Programing Using R Part 3

Lecture 4: Basic Programing Using R Part 4

Lecture 5: R Programming Case Study Part 1

Lecture 6: R Programming Case Study Part 2

Lecture 7: R Programming Case Study Part 3

Lecture 8: R Programming Case Study Part 4

Lecture 9: Stastical Packages In R

Lecture 10: R Programming Case Study Using Inbuilt Datasets

Lecture 11: Case Study On Data Visualization Using R Part 1

Lecture 12: Case Study On Data Visualization Using R Part 2

Lecture 13: Case Studay On Data Visualization Using R Part 3

Lecture 14: Case Study On Data Visualization Using R Part 4

Lecture 15: Recap Exploratory Data Analysis

Lecture 16: Must Know Packages For a Successful Data Scientist

Chapter 7: Predictive Analytics

Lecture 1: Scatter Diagram

Lecture 2: Correlation Coefficient

Lecture 3: Simple Linear Regression Introduction

Lecture 4: Simple Linear Regression Using R -Part 1

Lecture 5: Simple Linear Regression Using R – Part 2

Lecture 6: Simple Linear Regression Using R – Part 3

Lecture 7: Simple Linear Regression Using R – Part 4

Lecture 8: Multiple Linear Regression Introduction

Lecture 9: Multiple Linear Regression Using R – Part 1

Lecture 10: Multiple Linear Regression Using R – Part 2

Lecture 11: Multiple Linear Regression Using R – Part 3

Lecture 12: Recap Linear Regression

Lecture 13: Understanding Logistic Regression Concepts

Lecture 14: Logistic Regression Part 1

Lecture 15: Logistic Regression Part 2

Lecture 16: Logistic Regression Part 3

Lecture 17: Logistic Regression Part 4

Lecture 18: Logistic Regression-Logistic Function Representation

Instructors

ExcelR Solutions
Pioneer in professional management trainings & consulting

Rating Distribution

1 stars: 20 votes
2 stars: 34 votes
3 stars: 98 votes
4 stars: 152 votes
5 stars: 228 votes

Frequently Asked Questions

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