AI & Commutation in Japan

Data Science is shaping almost every industry that generates any kind of data. It's adding values to business intelligence, assisting marketers in creating strategies & campaigns and  adding AI features to mobile applications. When Data Science is making our lives better then why not use it to make our daily commutation more comfortable.

Every day railways generates enormous data at various points. At each tap in or tap out of stations, data gets generated. If this data is collected & merged with data from other sources such as Weather Data or Holidays, then it is possible to create number of useful applications or solutions.

In railways, lot of efforts have already been made or ongoing in disaster management or infrastructure maintenance with data science. However, Rush hours are something that almost everybody has to go through but still there are no leads of work in this area.

In Japan, almost everybody uses local train for commuting to schools, colleges or offices. Due to which trains get over crowded in mornings & evenings, which is called RUSH HOURS or 'Rush Aawa' in Japanese.

Nobody likes these rush hours but there is no other option so everybody has to travel in these PUSHY boggies. There are some initiatives from railway industry such as morning campaigns.

In this situation, if we use data science not only this problem can be solved but it will also generate new business opportunities. All that has to be done is collect data & predict the exact rush hours & delays on each line. This information can be further used to prepare station staff in advance or can be delivered to users through mobile application. Also it can be used to re-evaluate pricing strategy.

ABC's of Data Science

The words like Big Data (BD), Machine Learning (ML), Data Mining (DM), Cognitive Applications (CA), Artificial Intelligence (AI) etc. keep coming up in many discussions or conversations these days but what exactly they are and how are they interrelated? Here is the explanation of some of these terms in a very simple manner. Let’s start with Big Data.

1. What is Big Data? When exactly the data is termed as “Big Data”? Is it the size of elephant or dinosaur?

A: Actually there is no particular limit defined for data after which it becomes big data. The data is big or small in reference to the application in which we are using it. When the particular application is not able to handle or respond to data then that data becomes big for that particular application. For example same amount of data can be termed as Big Data for Excel but not for SAS or SPSS. Hence, the term Big Data in itself is not appropriate and it is better to refer such data as Large Data.

2. What is the difference between Data Analysis & Data Science?

A: Data Analysis is simply analyzing data. From ages, data has been analyzed for various techniques like MIS Reporting, Six Sigma, Lean etc. However, Data Science is little different from Data Analysis. Data Science is also data analysis but here we analyze large data sets with the help of Machine learning and/or Data Mining techniques. Data Analysis can be done with tools like Excel, however for Data Science tools like SAS, SPSS, Python, R etc. are required. A Data Scientist needs to have statistical knowledge along with programming skills.