How Data Science Play a Significant Role in Indian Railways

Byrihangowda

Indian Railways uses artificial intelligence and big data analytics to increase operational efficiency. The Indian Railways primarily employs artificial intelligence and big data analytics in train operations, ticketing, system maintenance, freight operations, and railway assets such as 121,407 track kilometers, 34,319 route kilometers of electrified track, 12,000 locomotives, 74,000 coaching stock, 7,500 stations, and so on. The Indian railway system is massive and extensive, evidenced by the 1.2 billion tonnes of freight and 8.4 billion passengers in 2018-2019. The Indian railway system is the world’s largest passenger and fourth-largest freight carrier.

 

With the help of big data and artificial intelligence, India’s national railway is exploring new possibilities and achieving new heights. According to the official statement, Indian Railways analyzes data using artificial intelligence and big data analytics before using it in their passenger reservation system (PRS). This can help with the introduction of new trains and predictive asset maintenance.

 

There are numerous myths surrounding data science, and it is critical to dispel them. Here are some of the myths we’ve discovered while working with customers:

  • Big Data with Deep Learning is a Data Science project.

Most individuals agree that having more data is beneficial. More essential than having a large amount of data has the appropriate data. We have, for example, dealt with modest data sets, such as log files that describe the behavior of systems and equipment at Thales. We’ve also worked with systems that generate terabytes of data daily. Both have offered helpful information. So it’s not so much about the data collection size as it is about selecting the correct data. The algorithm used is also determined by the goal of the data science project. Deep learning is crucial for many applications but is not appropriate or even necessary for all of them. For example, at Thales, we’ve worked on data engineering projects that translated binary code into the human-readable format and then processed this data set into interesting statistical KPIs – this was sufficient to provide value to the client. The goal should be to provide value to the client through data. Explore Data Science Courses in Delhi for more information on deep learning and ML techniques. 

  • Simple to implement

The tools used to implement data science projects have evolved in recent years, as have supporting technologies such as storage and processing. For those who have worked with data science for a few years, the software applications used in data science projects are more user-friendly than ever before. Machine-learning libraries are expanding, and technique applications are getting considerably simpler. On the other hand, a successful data science project necessitates subject matter expertise and data science understanding. According to CRISP-DM, the beginning point of a data science project is developing a knowledge of both the business and the data. In this context, one of the most significant components of a data science project is defining the problem. As a result, just applying a few algorithms to a data set does not enable businesses to develop the potential value associated with the data. The ease with which data science may now be used may be detrimental to the value-creation process. To summarize, data science is about providing value rather than applying a variety of methods.

  • Data Science is a self-contained process.

Data science is sometimes seen as “simply applying that algorithm”; however, this is not how value is created from data! A data science project involves both early input from subject matter experts to frame the problem and ongoing oversight to interpret the results based on the assumptions made and expert domain knowledge. For example, just using an algorithm may uncover patterns of problems on train lines in the early hours of the morning; without additional input, these patterns will be integrated into its learning. However, a subject matter expert would understand that this information must be removed because it could result from maintenance activities.

 

The evidence in the data is the ground truth.

Many data science initiatives begin without establishing the truth. Data scientists cannot steer their data science activity without the ground truth. This implies they will need help to assess trends, insights, and information gleaned from data and determine whether a trend is actually valuable. In the railway industry, ground truth can be extracted in various ways. For example, a data scientist may create a mathematical model that simulates the behavior of a system or piece of equipment, while evidence of failures (ground truth) may be extracted from maintenance systems such as the Computerised Maintenance Management System.

 

Planning to pursue a career in data science and AI? Visit the Data Science Certification Course in Delhi for more information on how to successfully deploy techniques. 

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