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Railway Analytics Course

With our Railway Analytics course, you will emerge as a professional ready to grab innumerable opportunities in the current Industry that is focusing heavily to leverage on Data.
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  • 16 Hours of Intensive Online Sessions
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Railway Analytics course - 360digitmg
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Academic Partners & International Accreditations
  • Railway Analytics Course with Microsoft
  • Railway Analytics Course certification with NASSCOM certificate
  • Railway Analytics Course with INNODATATICS certificate
  • Railway Analytics Course with TUV
  • Railway Analytics Course with SUNY
  • Railway Analytics Course with NEF

Railway Analytics

Program Cost

INR 12,000/-

Overview of Railway Analytics Course

By harnessing the power of Big Data Analytics, the railway industry can have a clear picture of the progress being made in the Railway industry and related businesses. With 360DigiTMG's Railway Analytics course analysts can leverage data-driven insights and strategies to effectively reduce costs and improve profit along with customer experience. 360DigiTMG course graduates can easily develop optimized solutions and help the railway industry make informed decisions.

Railway Analytics Course Training Learning Outcomes

Railway Analytics is a special branch of Data Analytics that uses employee-related data with analytical tools to improve railway performance. The Certification Program in Railway Analytics is a sui generis attempt to blend analytics solutions for better railway practices and the development of new strategies for efficient train performance. They are specifically designed to suit railway professionals and data professionals who wish to understand the application of Big Data Analytics, Machine Learning, Neural Networks, and Deep Learning to railway industry data. Our Railway Analytics course is meant for professionals from the Railway domain as it provides a comprehensive picture of how Data Science and Artificial Intelligence can be leveraged to increase the efficiency of Railway practices and strategies. Understanding the applications of Data Science, Machine Learning to the Railways will be the prime objective of this content-rich program.

Work with various information sources specific to railway analytics.
Analyze structured and unstructured data using different tools and techniques in the context of railways.
Develop an understanding of descriptive and predictive analytics as applied to the railway industry.
Apply data-driven, machine learning approaches for railway functions such as predicting maintenance needs or optimizing train schedules.
Understand how surveys work in the railway context and perform analytics on survey data.
Utilize machine learning techniques to create a personalized experience for railway employees.
Apply data visualization concepts to represent railway data for easy understanding and decision-making.
Block Your Time
Railway Analytics course - 360digitmg

16 hours

Classroom Sessions

Railway Analytics course - 360digitmg

20 hours

Programming Videos

Who Should Sign Up?
  • Railway professionals (engineers, operations managers, planners, analysts)
  • Data analysts and scientists interested in the railway sector
  • Transportation and logistics experts
  • Researchers and academics in transportation or railway-related studies
  • Students and aspiring professionals in railway engineering, operations, transportation planning, or data analytics
  • Professionals from related industries (urban planning, infrastructure management, smart cities).

Railway Analytics Course Modules

The digital world is built on the walls of data and the Railway Analytics course is peering into the future. Put simply, Railway Analytics is bringing in all the data and combining it to provide strategic as well as predictive insights that can be used to improve one’s business strategy. In this module on the Railway Analytics course, you will learn about the various systems and models of the Railway Analytics course and how Machine Learning is helping to predict employee turnover. You will learn about the various techniques used during the screening and recruiting of candidates. This module also throws light on how employee performance can be predicted through analysis and designing appropriate surveys. You will also learn how Deep Learning is mining the emotional state of the workforce.

  • The introduction to Railway Analytics includes a brief discussion of the technological advancements in the industry from past decades
  • Network expansion of the mode of transport
  • Logistics Businesses opting for Railway in terms of cost efficiency and reliability
  • Discuss the features of Machine Learning and its capabilities for Railway Industries
  • Applications of Machine Learning in the Railway Industry
  • Stages of Analytics
  • Detail explanation of Stages of Analytics from the Railway perspective
  • Machine Learning Primer - Unsupervised and Supervised Learning
  • Detail explanation of training, validation, and testing
  • Domain-specific examples of 4 stages of Analytics
  • Discuss CRISP-ML(Q) flow and detailed explanation of the 6 phases
  • Analyze the data to summarize the characteristics using statistical and data visualization techniques
  • Data understanding, load the data, features in the data, data types
  • Steps on EDA - Univariate Analysis
  • 4 Business moment decision
  • Univariate, bivariate, and multivariate analysis
  • Data preprocessing, Cleaning, Organizing of data
  • A brief introduction to unsupervised learning
  • Clustering/segmentation objectives
  • K-Means clustering in Railways
  • A brief introduction to supervised learning
  • KNN algorithm and distance metrics
  • Early stopping point in the training phase
  • Choose the best K value
  • Application of KNN in Railways
  • Introduction to decision-making using evidence
  • Decision Tree (DT) - rules-based approach
  • Entropy and Information gain
  • Use case: Application of DT in Railways
  • Prediction with Base Equation
  • Correlation and the significance in linear regression
  • Discuss on OLS
  • LINE Assumptions
  • Use case: Application of linear regression for numeric value prediction
  • Introduce AI and the importance of AI in Business
  • Intro to neurons and neural networks (NN)
  • Introduction to perceptron algorithm
  • Calculation of weights in NN
  • Introduction to a multilayer perceptron (MLPA)
  • Error surface
  • Gradient Descent Algorithm
  • Application of NN in Railway industries
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