The ease of doing business index is an index which is created by the World Bank Group.A low numeric value that means high ranking which means better and simpler regulations for business and stronger protection of property rights.According to the World Bank, India registers its highest ever increase in ease of doing business.Currently, India is in 100th position with ease of doing business after jumping 30 places which is the best ranking ever.PM Modi who wants our country on top 50 called the achievement 'historic'.By this India is closing the gap with China but still, it is far away from world's topmost economies.The World Bank provides these rankings based on the ten parameters: Starting a business, Dealing with construction permits, Getting electricity, Registering property, Getting credits, Protecting investors, Paying taxes, Enforcing contracts, Trading across borders and Resolving failures.Each one among these is given equal priority.

There is a huge increase in start-up businesses these days which we do not get to see earlier.According to NASSCOM report, India is the home for 3100 startups each year and it is being said that if such growth continues with same peace India will create more than 2 lakh job opportunities in the coming five years.

Till now everybody came to know about the Mumbai-Ahmedabad Bullet Train project which failed to justify itself.In recent days I came across many people who are having a question that "Does India really need a bullet train?"
Only certain countries have a facility for High-Speed Rail(HSR), while many have tried but failed.India's approach to having a Bullet Train by 2023 is said to be a huge step for becoming a developed country and thus removing a tag of 'developing country'.This step leads to the various question in our mind.

• Do we really need bullet trains now?
• What about the other failed and struggling rail projects?
• How this will help the majority of the country?
• How this will help poor people of our country, they can't afford to pay the fare of bullet train?
• Unable to maintain the basic train service, How it will maintain bullet train services?
• Do we have a proper economy and technological advancement?

This post will clearly explain the importance of planning.Different people have different opinions and definition of 'success' leading to a meaningful, happy and fulfilling life.But what I believe is that one of the key factors which lead to success is 'Planning'.Working hard, having proper discipline and sacrificing, all these are worthless without proper planning.
Planning things is equivalent to having a roadmap of your destination, where you want to reach and roadmap helps you to reach the desired destination.Think about a situation where you clearly know your destination but don't have a roadmap to follow so you will get stuck in between, become panic and frustrated which will lead to poor performance of yours.Despite this, so many people in the world today failed to plan their things and end up lost or stuck where they are.
Most of the people think that giving time in planning is totally a waste of time, but they don't know that by planning things they actually save a lot of time.When you decide to plan things prior to doing it, you are taking important steps not only in identifying and reaching your goal but also to do that in a most efficient manner.While planning things you actually choose right things and leave the wrong one's.Planning can be implemented in your small or big aspects of your life.It may be about waking up early in the morning or planning a foreign trip.I have used the power of planning in waking up early,making this blog and many more things.If you really develop the habit of planning things before doing, you are more likely to achieve success in same.With organisation point of view planning is very important and it plays a very vital role in success of any projects.

It is already explained that how you can download R and R Studio in your system and you all know the importance of R programming in Data Science.But when you download R from the Comprehensive R Archive Network (CRAN) you get a base R system.That means what you have downloaded in your system has limitations.It will perform all the basic things, that is it will implement the R language, but for using the R for Data Science purpose you have to be familiar with R packages and Tools.You need to install the tools and package in order to extend the functionality of R.
Packages and tools are the biggest advantages of R programming as it has the large collection of the packages which was developed and published by R Community.You all get all the packages at CRAN as it is the primary location for obtaining R package.There are more than 5200 packages on CRAN covering a wide range.
There are also some packages which come with base R when you download and install.To check the information about available package just give in command available.packages() in the command line.However, there are so many packages that it's not possible to see all and you have to give the command head(rownames(a),3) which will show the first three package.


x<-available.packages()   #all available package is assigned to x
head(rownames(x),3)       #shows the first three package

Type the above command in R Studio and it will display the first three package.

"<-" is the assignment operator in R and is used to assign the values to the variable.Same way head() which is used to display the values of the top and rownames() which return the name of the row is also a function.Similarly tail() used to display the value from the back and colnames() returns the name of the columns.

Installing R Packages

Packages can be installed by using the command install.packages("package name").

install.packages("slidify")  #install the slidify package from CRAN in your system

You can also install multiple packages at the same time.

install.packages(c("slidify","ggplot2","devtools")) #Install all the three named packege

c() is a character vector in which all the names of the packages to be installed is given.

You can also directly install the packages by just going to the tools in R Studio and then select install packages.Then select the source from where you want to install the package and the package name and destination where you want to install that package in your local system.

Loading R packages

It is very necessary to load the package every time before you use that.The library() functions are used to load the package.

library(slidify)   #load the slidify package

Rtools

Be kind to your body

Sleep early, Get up early

Never skip the Breakfast

Time for pleasure

The first and the foremost thing any Data Scientists enthusiast should understand is that it's not as easy as they think, Yes Data science is not an easy thing, You need lots of motivation and dedication to go to the depth of the Data Science course.Because there are many things inside the Data Science, it's not only the word "Data Science".Both "Data" and "Science" are very big terms among themselves and when combined, you can imagine what are the challenges and problems one can face dealing with it.It's not all new but as the technology is growing challenges to grows with it.

Data Science Problems according to Difficulties

Descriptive Analysis: The main goal of this is to describe the Data.You are not going to make any prediction or any decision or performing any kind of analysis.This is the first kind of Data analysis performed, however, it is not generalized without adding statistical modeling.Commonly applied to the Census Data.

Exploratory Analysis: Exploratory analysis on data will show you different relationships between data which you don't know earlier.It is very much useful in defining future studies and defining new connections.Although it is not the final stage and prediction and decision making can't be done with this alone.

Inferential Analysis: Inferential Analysis uses a very small data, which is sample data and predicts something on larger Data that is predicting or making a decision on bigger population.It is the goal of statistical models and heavily depends on the sample or population.

Predictive Analysis: This Analysis refers to using the data on some objects for predicting the values of other objects.Doing correct prediction is very tough and heavily depends on measuring the right values.There are many models for prediction but if the Data size is more than with the simple model prediction really works well.

Casual Analysis: By this, you will find out that, what happens with one variable when you make a change to other variables.Casual Analysis is considered to be the "Gold Standard" for Data Analysis and is usually identified as average effects but may not apply to every individual.

Tools used by Data Scientists

Git and GitHub