This blog will help you in knowledge hunt of Data science. The below given list will help you to find the blogs that talk about Data science easily. I hope you will find this useful.
Data visualization has become a vital slice of data science arena. Hence, our key tool should have strong capabilities on both the fronts – data analysis as well as data visualization. With this revolution in the landscape, or has extended immense popularity because of its splendid data visualization capabilities. With a few lines of code, you can produce beautiful charts and data stories. R contains superb libraries to create basic and more evolved visualizations like Bar Chart, Histogram, Scatter Plot, Map visualization, Mosaic Plot and various others. Below is the cheat sheet of widespread visualization for representing data. Thanks to my colleague for sharing this.
A very good analysis using R in the field of cricket. Must see ! 🙂
Yet all experience is an arch wherethro’
Gleams that untravell’d world whose margin fades
For ever and forever when I move.
How dull it is to pause, to make an end,
To rust unburnish’d, not to shine in use!
Ulysses by Alfred Tennyson
This is an initial post in which I introduce a cricketing package ‘cricketr’ which I have created. This package was a natural culmination to my earlier posts on cricket and my completing 9 modules of Data Science Specialization, from John Hopkins University at Coursera. The thought of creating this package struck me some time back, and I have finally been able to bring this to fruition.
So here it is. My R package ‘cricketr!!!’
This package uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package only uses data from test cricket. I plan to develop functionality for One-day and…
View original post 2,667 more words
Thinking how to become a Data Scientist? Here we go, the 8 Steps to become a Data Scientist (The Sexiest and the Hot Job of the Decade)
Well, these steps are not so easy but possible if we try. Most of the steps come with no-cost or very low-cost.
Thanks for DataCamp for the nice infographic. Is this info useful? Then please share this info with your circle.
This is to all out there who are wondering which is better language to learn for data analysis and visualization. Whether one should use R or Python when they do their everyday data analysis tasks.
Both Python and R are amongst the most extensively held languages for data analysis, and have their supporters and opponents. While Python is a lot praised for being a general-purpose language with an easy-to-understand syntax, R’s functionality is developed with statisticians in thoughts, thus giving it field-specific advantages such as excessive features for data visualization.
The DataCamp has recently released a new infographic for everyone interested in how these two (statistical) programming languages relate to each other. This superb infographic discovers what the strengths of R over Python and vice versa, and aims to provide a basic comparison between these two programming languages from a data science and statistics perspective.
Not to ignore the new entrant in war field “Julia” language. It is a high-level dynamic programming language designed to address the requirements of high-performance numerical and scientific computing while also being effective for general purpose programming. Influenced by MATLAB, C, Python, Perl, R, Ruby and others.
Soon we expect Julia to join the clash !
One of the common difficulties individuals face in learning R is lack of an organized way. They don’t know, from where to start, how to proceed, which way to choose? However, there is a surplus of good free resources accessible on the Internet, this could be overwhelming as well as puzzling at the mean time.
After mining through infinite resources & archives, here is a comprehensive Learning way on R to learn R from the beginning. This will help you to learn R rapidly and proficiently.
Step 1: Download and Install R
The easy way to proceed is to download the basic version of R and installation instructions from CRAN site. R is available for Windows, Mac and Linux. Windows and Mac users most likely want one of these versions of R. R is part of many Linux distributions, you should check with your Linux package management system in addition to the link above.
You can now install various packages. There are more than 9000 packages in R for different purposes. Here is a link to understand packages called CRAN Views. You can accordingly select the sub type of packages that you want.
To install a package you can just do this
For example, if we want to install a package called “animation” then we use
Normally the package should just install, however:
- if you are using Linux and don’t have root access, this command won’t work.
- you will be asked to select your local mirror, i.e. which server should you use to download the package.
You must also install RStudio. It helps R coding much easier since it allows you to type multiple lines of code, handle plots, install and maintain packages and navigate your programming environment.
Step 2: Learn the basics
You need to start by knowing the basics of the language, libraries and data structure. The R track from Datacamp is the best place to start your journey. See the free Introduction to R course at https://www.datacamp.com/courses/introduction-to-r. After doping this course, you would be comfortable writing basic scripts on R and also understand data analysis. Alternately, you can also see Code School for R at http://tryr.codeschool.com/
If you want to learn R offline on your own time – you can use the interactive package swirl from http://swirlstats.com
Primarily learn read.table, data frames, table, summary, describe, loading and installing packages, data visualization using plot command.
Step 3: Learn Data Management:
You need to use them a lot for data cleaning, especially if you are going to work on text data. The best way is to go through the text manipulation and numerical manipulation assignments. You can learn about connecting to databases through the RODBC package and writing sql queries to data frames through sqldf package.
Step 4: Study specific packages in R– data.table and dplyr Here we go ! Here is a brief introduction to numerous libraries. We need to start practising some common operations.
- Practice the data.table tutorial thoroughly here. Print and study the cheat sheet for data.table
- Next, you can have a look at the dplyr tutorial here.
- For text mining, start with creating a word cloud in R and then learn learn through this series of tutorial: Part 1 and Part 2.
- For social network analysis read through these pages.
- Do sentiment analysis using Twitter data – check out this and this analysis.
- For optimization through R read here and here
Step 5: Effective Data Visualization through ggplot2
- Read Edward Tufte and his principles on how to make data visualizations here . Especially read on data-ink, lie factor and data density.
- Read about the common pitfalls on dashboard design by Stephen Few.
- For learning grammar of graphics and a good way to do it in R. Go through this link from Dr Hadley Wickham creator of ggplot2 and one of the most brilliant R package creators in the world today. You can download the data and slides as well.
- Are you interested in visualzing data on spatial analsysis. Go through the amazing ggmap package.
- Interested in making animations thorugh R. Look through these examples. Animate package will help you here.
- Slidify will help supercharge your graphics with HTML5.
Step 6: Learn Data mining and Machine Learning Now, we come to the most valuable skill for a data scientist which is data mining and machine learning. You can see a very comprehensive set of resources on data mining in R here at http://www.rdatamining.com/ . The rattle package really helps you with an easy to use Graphical User Interface (GUI). You can see a free open source easy to understand book here at http://togaware.com/datamining/survivor/index.html You will go through an overview of algorithms like regressions, decision trees, ensemble modelling and clustering. You can also see the various machine learning options available in R by seeing the relevant CRAN view here. Resources:
- You can learn on time series forecasting from this booklet – A Little Book for Time Series in R .
- Some machine learning in R is here. You can enroll in a free course here.
Step 7: Practice Practice with example data available with you and on the internet. Stay in touch with what your fellow R coders are doing by subscribing to http://www.r-bloggers.com/ , http://stats.stackexchange.com and www.stackoverflow.com. Go through the questions and answers that users come up with. Start interacting by asking questions and providing the answers for the questions which you can ! Happy learning !!! 🙂