Data Science Tutorials

Git and GitHub - a complete startup guide for a beginner

  • 2018-04-11
  • Comments
  • Karthik Janar

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?

  • 2018-04-12
  • Comments
  • Karthik Janar

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

  • 2017-09-24
  • Comments
  • Ashley J

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

  • 2018-04-12
  • Comments
  • Karthik Janar

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

  • 2018-04-13
  • Comments
  • Karthik Janar

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

  • 2018-04-30
  • Comments
  • Karthik Janar

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

  • 2018-04-30
  • Comments
  • Karthik Janar

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.

  • 2018-04-30
  • Comments
  • Karthik Janar

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

  • 2018-05-01
  • Comments
  • Karthik Janar

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

  • 2018-05-01
  • Comments
  • Karthik Janar

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

  • 2018-05-01
  • Comments
  • Karthik Janar

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

  • 2018-05-01
  • Comments
  • Karthik Janar

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

  • 2018-05-06
  • Comments
  • Karthik Janar

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

  • 2018-05-07
  • Comments
  • Karthik Janar

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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Related Tutorials

Using make.names() in R

Handling Date and Time in R

Functions in R - Creating your first R function

Logical and Character Vectors in R

Missing Values in R

Subset Vectors in R

Matrices and Data Frames in R

Discrete Vs Continuous Random Variables in R

File handling commands in R

Generating Sequence numbers in R - seq(), rep() c() etc.

Basic Building Blocks of R

What are the different types of Analysis a Data Scientist would do?

Overview and History of R

Git and GitHub - a complete startup guide for a beginner

Data Analytics - Which programming language to learn. R vs Python

Archived Comments

1. plse can u send me study material or sample questi
View Tutorial          By: sneha at 2015-03-12 09:40:49

2. I am getting this exeception when trying to send m
View Tutorial          By: praveen at 2012-12-27 11:15:14

3. Can u please tell me how to compile and execute a
View Tutorial          By: RJ at 2010-04-20 01:30:10

4. I writing a main class in the eclipse IDE and call
View Tutorial          By: adam lim at 2013-03-11 04:13:51

5. super explanation
View Tutorial          By: pree at 2013-12-30 16:41:32

6. Nice explanation. And a good example to back it up
View Tutorial          By: fazil at 2009-07-29 00:32:03

7. This is really a tremendous tutorials for the begi
View Tutorial          By: Raghvendra Dwivedi at 2010-09-01 10:22:42

8. any body who have a full source code of the progra
View Tutorial          By: progra at 2013-02-11 12:37:34

9. Hi Blackpanther, the mode is either 0 or 1. If you
View Tutorial          By: Ramlak at 2008-04-01 02:24:14

10. very simple explanation......
easily unders

View Tutorial          By: saurabh at 2009-04-17 16:02:30