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.
The process of deciphering meaningful insights from existing databases and analyzing results for consumption by business users.
We are going to start with basic programming using R for data management and data manipulation.
Changing one data type to another if the formatting is not done properly is not difficult at all using R.
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.
The date functions return a Date class that represents the number of days since January 1, 1970.
There are two different types of functions in R, user-defined functions and built-in Functions.
Using a loop, a similar task can be performed many times.
The apply function uses an array, a matrix, or a dataframe as an input and returns the result in an array format.
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.
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.
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.
The relationship or association between two variables is known as bivariate analysis. There are three possible ways of looking at the relationship.
The multivariate relationship is a statistical way of looking at multiple dependent and independent variables and their relationships.
Understanding probability distributions is important in order to have a clear idea about the assumptions of any statistical hypothesis test.
Interpretation of the calculated distribution helps in forming a hypothesis.
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.
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.
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.
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.
This video will let you explore different kinds of charts and plots and their creation. You'll also be able to use geo mapping.
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.
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.
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.
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.
This video will let you understand the use of stepwise regression method to solve complex regression problems.
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.
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.
Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.
With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.
From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.
Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.