Data Science Specialization: Learn Online with Free R Courses & Lectures

The Beginner’s Guide to Reading Tabular Data in R: R is a programming language that is widely used for data analysis and statistical computing. It has a powerful set of data structures, including vectors, lists, and data frames, that allow users to work with data in a flexible and efficient way. Reading Tabular Data in R: R provides two main functions for reading tabular data: read.table() and read.csv(). These functions are very similar, with the only difference being that read.csv() assumes a comma as the separator between columns, whereas read.table() assumes a space. You can specify the separator in read.table() using the sep parameter. Here's an example of how to use read.table() to read a tab-delimited file: # Read a tab-delimited file  my_data <- read.table("my_data.txt", header = TRUE, sep = "\t") And here's an example of how to use read.csv() to read a comma-separated file: # Read a comma-separated file  my_data <- read.csv("my_data.cs...

The Beginner’s Guide to R Data Types: R is a programming language that is widely used for data analysis and statistical computing. It has a powerful set of data structures, including vectors, lists, and data frames, that allow users to work with data in a flexible and efficient way. Matrices A matrix is a two-dimensional array in R that can contain elements of any data type. You can create a matrix using the matrix() function. For example: # Create a matrix with 3 rows and 2 columns  my_matrix <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 3, ncol = 2) Factors A factor is a type of variable in R that represents categorical data. Factors are stored as integers, where each integer corresponds to a level of the factor. You can create a factor using the factor() function. For example: # Create a factor with three levels: "low", "medium", "high"  my_factor <- factor(c("low", "high", "medium", "high", "low")) Missin...

The Beginner’s Guide R Objects and Data Types: "Vectors and Lists" R is a programming language that is widely used for data analysis and statistical computing. It has a powerful set of data structures, including vectors, lists, and data frames, that allow users to work with data in a flexible and efficient way. R Objects Everything in R is an object, which means that it has a type, a value, and possibly some attributes. There are many different types of objects in R, including numbers, strings, and logical values, as well as more complex objects like functions and data frames. Numbers In R, there are two types of numbers: integers and doubles. Integers are whole numbers, while doubles are numbers with decimal places. When you create a number in R, it is automatically assigned a type based on its format. For example, if you type x <- 5, R will create an integer object, while if you type y <- 5.0, R will create a double object. Attributes Objects in R can have attributes,...

The Beginner’s Guide to Assignment operator and input evaluation in R Programming: R is a powerful programming language that is widely used for statistical analysis and data visualization. One of the key features of R is its ability to handle data inputs and outputs in a variety of formats, including text files, spreadsheets, and databases. In this blog post, we'll explore two important concepts in R programming: the assignment operator and input evaluation. These concepts are essential for working with data in R, and mastering them will help you become a more effective and efficient R programmer. The Assignment Operator In R, the assignment operator is represented by the "<-" or "=" symbol. The assignment operator is used to assign a value to a variable. For example, if we want to assign the value 10 to a variable named "x", we can use the following code: x <- 10 This code assigns the value 10 to the variable "x". We can then use the v...

The Beginner’s Guide to the History of R Programming: R Programming is basically the dialect of S Programming. S History: The S programming language was first developed in the late 1970s by John Chambers and his colleagues at Bell Laboratories. It was initially used for data analysis and graphics, and it served as the basis for the commercial software package S-PLUS, which was released in the early 1990s. While S-PLUS was popular in the statistical community for many years, it has since been largely replaced by the open-source software environment R, which was inspired by S and developed by some of the same people who worked on S-PLUS. As for the current version of S, it's not as widely used as R, and there are several different implementations of the S language that are still available today, including: S-PLUS: This is the commercial implementation of S that was developed by TIBCO Software Inc. It is still in use today, although it has been largely supplanted by R in the statisti...

The Beginner’s Guide to R Programming. I'm very excited to start R Programming and I hope you are too. This is the second course in the Data Science Specialization and it focuses on the nuts and bolts of using R as a programming language. The recommended background for this course is the course The Data Scientist's Toolbox . It is possible to take this class concurrently with that class but you may have to read ahead in the prerequisite class to get the relevant background for this class. For a complete set of course dependencies in the Data Science Specialization please see the course dependency chart , that has been posted on our blogpost. The primary way to interact with me and the other students in this course is through the discussion forums which in our case are comments section under the lectures, social media and blogpost . Here, you can start new threads by asking questions or you can respond to other people's questions. If you have a question about any aspect...

The Beginner’s Guide to Data Science Experimental Design: Now that we’ve looked at the different types of data science questions, we are going to spend some time looking at experimental design concepts, in our last lesson of our first Course "The Data Science Toolbox" in our Data Science Specialization using R Programming. As a data scientist, you are a  scientist  and as such, need to have the ability to design proper experiments to best answer your data science questions! Previous lesson, if you haven't watched! What does experimental design mean? Experimental design is organizing an experiment so that you have the correct data (and enough of it!) to clearly and effectively answer your data science question. This process involves clearly formulating your question in advance of any data collection, designing the best set-up possible to gather the data to answer your question, identifying problems or sources of error in your design, and only then, collecting the app...

The Beginner’s Guide to Big Data: A term you may have heard of before this course is “Big Data” - there have always been large datasets, but it seems like lately, this has become a buzzword in data science. But what does it mean? Previous lesson in case you haven't watched. What is big data? We talked a little about big data in the second lecture of this course. As the name suggests, big data are very large data sets. We previously discussed three qualities that are commonly attributed to big data sets: Volume, Velocity, Variety. From these three adjectives, we can see that big data involves large data sets of diverse data types that are being generated very rapidly. Three qualities of big data So none of these qualities seem particularly new - why has the concept of big data been so recently popularized? In part, as technology and data storage has evolved to be able to hold larger and larger data sets, the definition of “big” has evolved too. Also, our ability to collect...

The Beginner’s Guide Types of Question in Data Science: In this lesson, we’re going to be a little more conceptual and look at some of the types of analyses data scientists employ to answer questions in data science. Previous lesson, in case you haven't watched. The main divisions of data science questions There are, broadly speaking, six categories in which data analyses fall. In the approximate order of difficulty, they are: Descriptive Exploratory Inferential Predictive Causal Mechanistic Let’s explore the goals of each of these types and look at some examples of each analysis! 1. Descriptive analysis The goal of descriptive analysis is to  describe  or  summarize  a set of data. Whenever you get a new dataset to examine, this is usually the first kind of analysis you will perform. Descriptive analysis will generate simple summaries about the samples and their measurements. You may be familiar with common descriptive statistics: measures...