The world is emitting data at a very high pace and everyone wants to gain insights from the huge number of data coming their way. Data mining provides a way of finding these insights and R has become the go-to-tool for it among the data analysts and data scientists. If you're looking forward to working on complex data mining projects and gaining deeper insights of data, then go for this Learning Path.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The highlights of this Learning Path are:
Let’s get on this data mining journey together! This Learning Path starts with a brief introduction to R and setting up the development environment. Get a firm hold on the fundamentals of R and gradually build your skill level for data science. This Learning Path will then teach you various data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects. Moving ahead, you will build your own recommendation engine. You will then implement dimensionality reduction and use it to build a real-world project. You will be also introduced to the concept of neural networks and learn how to apply them for predictions, classifications, and forecasting. Finally, you will implement ggplot2, plotly and aspects of geomapping to create your own data visualization projects.
After completing this Learning Path, you will have a solid understanding of all data mining techniques and how to implement them using R, in any real-world scenario.
About the Author:
We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:
Dr. Samik Sen is a theoretical physicist and loves thinking about hard problems. After his PH.D. in developing computational methods to solve problems for which no solutions existed, he began thinking about how to tackle math problems while lecturing. He developed algorithms to generate problem sets and solutions and learned how to create video lessons. He has since developed a large Facebook community teaching school math around Ireland, with associated e-learning products and a YouTube channel. He has a YouTube channel associated with data science, which also provides a valuable engagement with people round the world who look at problems from a different perspective.
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. 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 aim of the video is to introduce the section and overview of the language R.
We need to have the core programs before we can begin and in this video,we show where to get them.
In this video, we look at where to begin, so that we can get started.
In this video, you will learn how RStudio has packages which avoid the problems and how we'll work on them.
In this video, you will learn how similar R is to other languages.
In this video, we see more familiar things in R.
In this video, we are now ready to write programs.
In this video, we will look at R data types which are new.
In this video, we will introduce some key commands to study data.
In this video, we will introduce various commands to help us pick out elements in which we are interested in.
In this video, we will investigate the Titanic dataset to see what it says.
In this video, we willadd a value by processing our data.
In this video,we will download football results from a web page.
In this video, we will use R to do some statistics.
In this video, we will work with distributions using R.
In this video, we will see some of R's graphical power.
In this video, we will use the plotting package, ggplot2.
In this video, we will see another plotting technique known as Facets.
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.
Market Basket Analysis is the study of relationships between various products and products that are purchased together or in a series of transactions.
Implementing market basket analysis.
It is important to classify objects according to their similarities or dissimilarities so that their study becomes easier. We use clustering techniques for that purpose.
There are many clustering methods available. Out of them, we will learn about two methods, K-means and hierarchical, in this video.
In this video, we will go a step further and learn about model-based and other clustering algorithms. We will also compare the algorithms.
Recommendation is a technique by which the algorithm detects what the user is buying. You would always like to be recommended things similar to your interest or things you have bought before. Recommendation engine helps in doing that.
There are different types of methods for building recommendation engine. You need to know which method to use depending on what type of product shopping you do. Also, there are certain limitations to these methods.
As we are armed with the theory of recommendation, we will now build a recommendation engine.
When there are a lot of variables, it becomes difficult to extract data. We need to devise something that will let us gather data in less number of variables. Dimensionality reduction provides you with that solution.
In order to understand dimensionality reduction, we need to work with it. Here, we will apply dimensionality reduction procedure, both the model-based and principal component-based approaches.
We can also try some other approaches to perform dimensionality reduction according to the need of the dataset. Let's look at that in this video.
Before working on neural networks, we need to understand the theory behind neural networks.
To understand and implement the neural networks, we need to understand the maths behind it. This video will do just that!
After knowing about neural networks, we need to see how to implement neural networks in R.
Prediction is an important aspect of data mining. In this video, we will create a prediction model using neural network to predict the auction average price.
We need to form clusters or groups of data so that performing actions on them becomes easier. Here we are going to classify customers based on marketing.
We will also perform forecasting using neural networks. In this video, we will forecast a time series.
After working with neural networks, we should also know the merits and demerits of the famous technology.
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