Do you want to step into the ever-growing field of data science? Do you wish to equip yourself with one of the most widely used language for data science?
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.
Data is on the rise and it’s the need of the hour to process it and make sense out it. Analysts and statisticians need to get this job done. It’s an art to tactfully and efficiently process data. But, as it goes an art becomes a reality only with the help of right tools and the knowledge of using these right. So, it is with data science. R is a powerful language that provides with all the tools required to build probabilistic models, perform data science, and build machine learning algorithms.
With this Learning Path, you’ll be introduced to R Studio and the basics of R. Then, you’ll taken through a number of topics such as handling dates with the lubridate package, handling strings with the stringr package, and making statistical inferences. Finally, the focus will be on machine learning concepts in depth and applying them in the real world with R.
The goal of this course to introduce you to R and have a solid knowledge of machine learning and the R language itself. You’ll also solve numerous coding challenges throughout the course.
This Learning Path is authored by one of the best in the fields.
Selva Prabhakaran Selva Prabhakaran is a data scientist with a large E-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Selva lives in Bangalore with his wife.
The aim of this video is to show how to install R on our system.
To run and write code in R, we first need to focus on how to get and install the IDE.
We have installed R and RStudio. Now let’s check out how to install the packages.
The aim of this video is to teach you what data types and data structures in R are.
In this video, we will see how to work with vectors in R.
The aim of this video is to show how to work with random numbers and do rounding and binning.
Taking vectors a step ahead, let’s see how we can to handle missing values.
We now know a lot about how vectors work, but how do we get specific items from a vector based on any condition? Let’s check out just that in this video.
This video will introduce a new data structure called list and how to work with it.
In this video, our goal is to understand how to perform set operations in R.
What is sampling and sorting and how to do it in R?
Checking conditions is often a requirement for a programmer to write maintainable code. Let’s understand how we can check conditions in R.
You may have come across several instances whilst coding where you need to perform repetitive operations through loops, right? In this video, we’ll see how to do that in R using for loops.
Let’s explore what data frames are and how to work with them.
In this video, we will check out how to import and export data in R.
The aim of this video is to check out how to work with matrices and frequency tables.
Our goal in this video is to use W to merge data frames.
How to do aggregation in R?
In this video, we will look at how to de-aggregate data frames and create cross tabulations.
In this video, we will look at how to handle date variables in R.
The goal of this video is to see how to perform string operations in R.
Let’s learn how to avoid code replication.
The aim of this video is to understand how to debug and handle errors.
We’ll see in this video how to write fast loops with apply().
Sometimes we’d want to iterate through lists. What do we do then? Let’s learn using fast loops with sapply, vapply and lapply to help us achieve this goal.
How to make plots and customize them.
Sometimes, just a single Y axis is not enough. It becomes difficult to depict the variations for two variables on different scales in the same chart. To solve this, we’ll look at how to make a plot with two Y axes.
In this video, we will learn how to make multiple plots and custom layout to get better at our analyzing skills.
The aim of this video is to create different types of plots.
What are the steps and actions one needs to do as part of data analysis before jumping to predictive modeling? Let’s understand this better.
The aim of this video is to teach you what normal distribution, central limit theorem, and confidence intervals are.
In this video, we will understand correlation and Covariance, the concept behind them, and their implementation in R.
What is the chi-square statistic, when is it used, and how to do the chi-sq test?
What is ANOVA, its purpose, when to use it, and how to implement it in R?
What are the other commonly used statistical tests in R and how to implement them?
All knowledge is incomplete without being put to practice. We’ve got a good taste of the core concepts that govern statistical analysis with R. Let’s solve the challenges pertaining to data manipulation in this video.
What is data if not represented visually! We have solved challenges related to data manipulation. Now it’s time to tackle visualization in this video.
Practice solving exercises that involve making statistical inferences.
The aim of this video is to introduce the magrittr package, its significance, and features such as pipe operators.
Understand and use the 7 data manipulation verbs.
How to group datasets by one or more variables using dplyr.
How to join two tables using the two table verbs of dplyr.
How to work with databases with DplyR.
Understand the basics of data.table; do filter and select operations.
Understand the syntax; create and update columns in a data.table.
Learn how to aggregate data.tables. Also learn the .N and .I operators.
Understand and implement chaining, keys, functions, and .SD.
How to write for-loops with set, set keys, and join data.tables?
In this video, we will take a look at how to perform univariate analysis.
The goal of this video is to perform bivariate analysis in R using three cases.
In this video, we will see how to detect and treat outliers.
The goal of this video is to see how to treat missing values in R.
In this video we'll see what is linear regression, its purpose, when to use it, and how to implement in R.
We'll see how to interpret regression results and Interaction effects in this video
In this video we will discuss what is residual analysis and detect multivariate outliers using Cook's Distance
The goal of this video is to understand how to do model selection and comparison using best subsets, stepwise regression and ANOVA.
In this video we will see how to do k-fold cross validation in R.
The goal of this video is check out how to build non-linear regression models using Splines and GAMs.
Our goal in this video would be to understand logistic regression, evaluation metrics of binary classification problems, and interpretation of the ROC curve.
In this video, we will understand the concept and working of naïve Bayes classifier and how to implement the R code.
In this video, we will look at what k-nearest neighbors algorithms, how does it works and how to implement it in T.
The goal of this video is to understand how decision trees work, what they are used for, and how to implement then.
The goal of this video is know what the various features of the caret package are and how to build predictive models.
The goal of this video is to know how to do feature selection before building predictive models.
In this video, we will look at how support vector machines work.
In this video, we will look at the concept behind bagging and random forests and how to implement it to solve problems.
Let's understand what boosting is and how stochastic gradient boosting works with GBM.
In this video, we will look at what regularization is, ridge and lasso regression, and how to implement it.
Let's look at how XG Boost works and how to implement it in this video.
Our goal in this video would be to reduce the dimensionality of data with principal components, and understand the concept and how to implement it in R.
In this video, we will understand the k-means clustering algorithm and implement it using the principal components.
In this video, we will analyze the clustering tendency of a dataset and identify the ideal number of clusters or groups.
The goal of this video is to understand the logic of hierarchical clustering, types, and how to implement it in R.
How to use affinity propagation to cluster data points? How is it different from conventional algorithms?
How to build recommendation engines to recommend products/movies to new and existing users?
The goal of this video is to understand what a time series is, how to create time series of various frequencies, and the enhanced facilities available in the xts package.
The goal of this video is to understand the characteristics of a time series: stationarity and how to de-trend and de-seasonalize a time series.
In this video, we will introduce the characteristics of time series such as ACF, PACF, and CCF; why they matter; and how to interpret them.
Our goal in this video would be to understand moving average and exponential smoothing and use it to forecast.
In this video, we will understand how double exponential smoothing and holt winter forecasting works, when to use them, and how to implement them in R.
Let's look at what ARIMA forecasting is, understand the concepts, and learn how ARIMA modelling works in this video.
In this video, we'll take a look at how to scrape data from web pages and how to clean and process raw web and other textual data.
Our goal in this video is to know how to process texts using tm package and understand the significance of TF-IDF and its implementation. Finally, we see how to draw a word cloud in R.
Let's see how to use cosine similarity and latent semantic analysis to find and map similar documents.
In this video, we will see how to extract the underlying topics in a document, the keywords related to each topic and the proportion of topics in each document.
Let's check out how to perform sentiment analysis and scoring in R.
How to classify texts with machine learning algorithms using the RTextTools package?
The goal of this videos is to understand what is the basic structure of to make charts with ggplot, how to customize the aesthetics, and manipulate the theme elements.
In this video, we will see how to manipulate the legend the way we want and how to add texts and annotation in ggplot.
The goal of this video is to understand how to plot multiple plots in the same chart and how to change the layouts of ggplot.
How to make various types of plots in ggplot such as bar chart, time series, boxplot, ribbon chart,and so on.
In this video, we will understand what the popular ggplot extensions are, and where to find them, and their applications.
We will discuss the best practices that should be followed to minimize code runtime in this video.
Let's tackle the implementation of parallel computing in R.
The goal of this video is understand how to work with DplyR and pipes.
In this video, we will discuss how to manipulate data with the data.table package, how to achieve maximum speed, and what the various features of data.table are.
Our main focus in this video is to understand how to write C++ code and make it work in R. Also leverage the speed of C++ in R, interface Rcpp with R, and write Rcpp code.
We'll take a look at the components of an R package in this video.
In this video, we will look at how to create an R Package so that it can be submitted to CRAN.
We will understand the mandatory checks and common problems faced by developers when creating R packages in this video.
The goal of this video is to show how to submit an R package to CRAN.
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