Data Science Tutorials

Git and GitHub - a complete startup guide for a beginner

For anyone who is serious about doing any work with computers needs to understand some basic concept of files and folders and some understanding of command line interface and commands. It is essential to have some hands on to use CLI. Furthermore if you are interested in doing programming or analytics then version control becomes very important. Git and GitHub provide very good toolset to create, maintain and manage your files and folders and their versions as well as share them with others.
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What are the different types of Analysis a Data Scientist would do?

This tutorial discusses some basic concepts of Data Science. Data Scientist's Job always starts with a question that needs to be answered. There are few different kinds of questions a data scientist would ask which defines the goal of the analysis. It starts with descriptive, and then it goes to exploratory, inferential, predictive, causal, and mechanistic
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Data Analytics - Which programming language to learn. R vs Python

Often people ask which one is better to learn? R or Python. Python is better for for data manipulation and repeated tasks, while R is good for ad hoc analysis and exploring datasets. For example, take text analysis, where you want to deconstruct paragraphs into words or phrases and then identify patterns. In this use case R is better suited and makes it simple. On the other hand, take for example, pulling the data, to running automated analyses over and over, to producing visualizations like maps and charts from the results then Python is better suited.
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Overview and History of R

In the world of data science, two popular programming languages are used today. R and Python. For a good comparison of R and Python read this tutorial. Personally I prefer R over Python because of its rich set of freely available packages and its visualization capabilities. R is based on an older language named S. S was originally developed at Bell Labs by John Chambers in 1976 as an internal statistical analysis environment using fortran libraries. In 1988 it was rewritten in C.
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Basic Building Blocks of R

In this tutorial, we will explore some basic building blocks of the R programming language. To complete this tutorial, let us use R Studio to get our hands on experience. If you have not installed "R Studio" yet, download and install it. Open R Studio and just follow the instructions as below. In its simplest form, R can be used as an interactive calculator.
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Discrete Vs Continuous Random Variables in R

Random in statistics does not mean ‘haphazard’ but it means a kind of order that emerges in the long run. For example we term unpredictable things that happen in our life as ‘random’. But we rarely see enough repetition of the same random phenomenon to observe a long term regulatrity that probability describes about ’random’ness.
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File handling commands in R

In this tutorial, you’ll learn how to examine your local workspace in R and begin to explore the relationship between your workspace and the file system of your machine. Because different operating systems have different conventions with regards to things like file paths, the outputs of these commands may vary across machines.
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Generating Sequence numbers in R - seq(), rep() c() etc.

In this tutorial, you’ll learn how to create sequences of numbers in R using functions such as seq(), rep(), c() etc. The simplest way to create a sequence of numbers in R is by using the : operator
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Logical and Character Vectors in R

The simplest and most common data structure in R is the vector. Vectors come in two different flavors: atomic vectors and lists. An atomic vector contains exactly one data type, whereas a list may contain multiple data types. Numeric vectors are one type of atomic vector. Other types of atomic vectors include logical, character, integer, and complex. In this tutorial, we’ll take a closer look at logical and character vectors.
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Missing Values in R

Missing values play an important role in statistics and data analysis. Often, missing values must not be ignored, but rather they should be carefully studied to see if there’s an underlying pattern or cause for their missingness.
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Subset Vectors in R

In this tutorial, we’ll see how to extract elements from a vector based on some conditions that we specify. For example, we may only be interested in the first 20 elements of a vector, or only the elements that are not NA, or only those that are positive or correspond to a specific variable of interest. By the end of this tutorial, you’ll know how to handle each of these scenarios.
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Matrices and Data Frames in R

In this tutorial, we’ll cover matrices and data frames. Both represent ‘rectangular’ data types, meaning that they are used to store tabular data, with rows and columns. The main difference, as you’ll see, is that matrices can only contain a single class of data, while data frames can consist of many different classes of data.
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Functions in R - Creating your first R function

Functions are one of the fundamental building blocks of the R language. They are small pieces of reusable code that can be treated like any other R object. Functions are usually characterized by the name of the function followed by parentheses.
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Handling Date and Time in R

R has a special way of representing dates and times, which can be helpful if you’re working with data that show how something changes over time (i.e. time-series data) or if your data contain some other temporal information, like dates of birth.
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Most Viewed Articles on Data Science Missing Values in R Manipulating Data with dplyr in R Graphics basics in R - Plot() Introduction to logical operations in R Types of Data Science Questions? Using make.names() in R Handling Date and Time in R Functions in R - Creating your first R function Matrices and Data Frames in R Subset Vectors in R What is Scrapy and how to use it. Logical and Character Vectors in R Generating Sequence numbers in R - seq(), rep() c() etc. File handling commands in R Discrete Vs Continuous Random Variables in R

Latest Tutorials What is Scrapy and how to use it. Manipulating Data with dplyr in R Graphics basics in R - Plot() Introduction to logical operations in R Types of Data Science Questions? Using make.names() in R Handling Date and Time in R Functions in R - Creating your first R function Missing Values in R Matrices and Data Frames in R Subset Vectors in R Logical and Character Vectors in R Generating Sequence numbers in R - seq(), rep() c() etc. File handling commands in R Discrete Vs Continuous Random Variables in R