R Data Mining Projects
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R Data Mining Projects

Discover the versatility of R for data mining with this collection of real-world dataset analysis techniques
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
23 students enrolled
Created by Packt Publishing
Last updated 2/2017
English
Curiosity Sale
Current price: $10 Original price: $125 Discount: 92% off
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Includes:
  • 3.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Make use of statistics and programming to understand data mining concepts and their application
  • Use R programming to apply statistical models to data
  • Use various libraries available in R CRAN (comprehensive R archives network) in data mining
  • Apply data management steps to handle large datasets
  • Get to know various data visualization libraries available in R to represent data
View Curriculum
Requirements
  • You should have prior knowledge of basic statistics and a little bit of programming language experience in any tool or platform.
  • This fast-paced video tutorial will help you solve predictive modeling problems using the most popular data mining algorithms through simple, practical cases. It’s your step-by-step helping hand to developing complex data mining projects.
Description

The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis. It enables you to create high-level graphics and offers an interface to other languages. This means R is best suited to producing data and visual analytics through customization scripts and commands, instead of the typical statistical tools that provide tick boxes and drop-down menus for users.

This video course explores data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields. We will teach you about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects.

About The Author

Pradeepta Mishra is a data scientist, predictive modeling expert, deep learning and machine learning practitioner, and an econometrician. He is currently leading the data science and machine learning practice for Ma Foi Analytics, Bangalore, India. Ma Foi Analytics is an advanced analytics provider for Tomorrow's Cognitive Insights Ecology, using a combination of cutting-edge artificial intelligence, proprietary big data platform, and data science expertise. He holds a patent for enhancing planogram design for the retail industry. Pradeepta has published and presented research papers at IIM Ahmedabad, India. He is a visiting faculty at various leading B-schools and regularly gives talks on data science and machine learning.

Pradeepta has spent more than 10 years in his domain and has solved various projects relating to classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures, spanning across domains such as healthcare, insurance, retail and e-commerce, manufacturing, and so on.


Who is the target audience?
  • This course is ideal for data analysts from novice to intermediate level.
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Curriculum For This Course
31 Lectures
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Data Manipulation Using In-Built R Data
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This video provides an overview of the entire course.

Preview 03:52

The process of deciphering meaningful insights from existing databases and analyzing results for consumption by business users.

What Is Data Mining?
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We are going to start with basic programming using R for data management and data manipulation.

Introduction to the R Programming Language
14:44

Changing one data type to another if the formatting is not done properly is not difficult at all using R.

Data Type Conversion
02:11

While working on a client dataset with a large number of observations, it is required to subset the data based on some selection criteria and with or without replacement-based sampling.

Sorting, Merging, Indexing, and Subsetting Dataframes
09:45

The date functions return a Date class that represents the number of days since January 1, 1970.

Date and Time Formatting
03:01

There are two different types of functions in R, user-defined functions and built-in Functions.

Types of Functions
02:24

Using a loop, a similar task can be performed many times.

Loop Concepts
02:30

The apply function uses an array, a matrix, or a dataframe as an input and returns the result in an array format.

Applying Concepts
03:17

In typical data management, it is important to standardize the text columns or variables in a dataset because R is case sensitive and it reads any discrepancy as a new data point.

String Manipulation
02:14

The R programming language, missing values are represented as NA. NAs are not string or numeric values; they are considered as an indicator for missing values.

NA and Missing Value Management and Imputation Techniques
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Exploratory Data Analysis with Automobile Data
8 Lectures 43:02

To generate univariate statistics about a dataset, we have to follow two approaches, one for continuous variables and the other for discrete or categorical variables.

Preview 09:18

The relationship or association between two variables is known as bivariate analysis. There are three possible ways of looking at the relationship.

Bivariate Analysis
01:48

The multivariate relationship is a statistical way of looking at multiple dependent and independent variables and their relationships.

Multivariate Analysis
00:57

Understanding probability distributions is important in order to have a clear idea about the assumptions of any statistical hypothesis test.

Understanding Distributions and Transformation
04:53

Interpretation of the calculated distribution helps in forming a hypothesis.

Interpreting Distributions and Variable Binning
05:14

Contingency tables are frequency tables represented by two or more categorical variables Frequency table is used to represent one categorical variable; however, contingency table is used to represent two categorical variables.

Contingency Tables, Bivariate Statistics, and Checking for Data Normality
06:17

The null hypothesis states that nothing has happened; the means are constant, and so on. However, the alternative hypothesis states that something different has happened and the means are different about a population.

Hypothesis Testing
11:58

When a training dataset does not conform to any specific probability distribution because of non-adherence to the assumptions of that specific probability distribution, the only option left to analyze the data is via non-parametric methods.

Non-Parametric Methods
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Visualizing Diamond Dataset
5 Lectures 37:45

This video will walk you through the basics of data visualization along with how to create advanced data visualization using existing libraries in R programming language.

Preview 16:06

This video will let you explore different kinds of charts and plots and their creation. You'll also be able to use geo mapping.

Visualizing Charts, and Geo Mapping
03:39

By the end of this video, you will be able to use some amazing data visualization techniques which are widely used for smart Data representation.

Visualizing Scatterplot, Word Cloud and More
10:51

This video will teach you how to take the plotting to a new level. Here, you will learn to use the plotly library, which is designed as an interactive browser-based charting library built on the JavaScript library.

Using plotly
04:49

This video will let you explore the Geo mapping which is a type of chart, used by data mining experts when the dataset contains location information.

Creating Geo Mapping
02:20
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Regression with Automobile Data
5 Lectures 38:56

How could you predict the future outcomes of a target variable? Regression is the answer to this. Let's have a brief introduction and understand regression.

Preview 04:08

This video will let you explore about Linear regression model which can be used for explaining the relationship between a single dependent variable and independent variable.

Linear Regression
14:04

This video will let you understand the use of stepwise regression method to solve complex regression problems.

Stepwise Regression Method for Variable Selection
02:19

What could we do in those scenarios where the variable of interest is categorical in nature, such as buying a product or not, approving a credit card or not, tumor is cancerous or not, and so on? Logistic regression is the best solution to these.

Logistic Regression
09:39

Let's dive into another form of regression where the parameters in a linear regression model are increased up to one or two levels of polynomial calculation.

Cubic Regression
08:46
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Market Basket Analysis with Groceries Data
2 Lectures 28:08

Market Basket Analysis is the study of relationships between various products and products that are purchased together or in a series of transactions.

Preview 12:29

Implementing market basket analysis.

Practical project
15:39
About the Instructor
Packt Publishing
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