Professional Certificate in

Aviation Analytics Course

Step into a whole new level to empower your career with Certification in Aviation Analytics. Witness global transformation with technical skills in analytics. Gain deep domain experience with this truly comprehensive course in Aviation Analytics.

Get Trained by Trainers from ISB, IIT & IIM
16 Hours of Intensive Classroom & Online Sessions
24+ Hours of Practical Assignments
Receive Certificate from Technology Leader - IBM
2 Capstone Live Projects
100% Job Placement Assistance

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Academic Partners & International Accreditations

Calendar-for-Virtual Interactive Classes

January

Start Date

01^st

January
TBC

Timings
Show all sessions

Aviation Analytics

Total Duration

1 Month

Aviation analytics course pre-requisite - 360digitmg

Prerequisites

Computer Skills
Basic Mathematical Concepts
Finance Basics

Overview of Aviation Analytics Course

Explore the most important methodologies, algorithms, and ideas in aviation analytics. Learn Statistical Analysis and Forecasting Techniques. Get acquainted with the Aviation System Components, Aviation Statistical Principles, Aviation Data Processing. Learn Forecasting Time Frame & Techniques, Regression Analysis, and how to collect and present data in Tables and Charts. This course will expose you to Feature Engineering, Fundamentals of Network Analysis, Data Exploration, Aviation Data Acquisition, Model Training, Assessment, and Comparison. Learn the integrities of Airport Management and Operations and also learn to assess and analyze data to optimize the performance of the airline and airport operations. Learn to examine the various security practices to contain threats and mitigate future occurrences.

What is Aviation analytic?

The airline traffic volume worldwide has grown exponentially and so has the big data generated and stored by airlines. Aviation analytics brings together data assets and provides world-class airport analysis and reporting to the airport management in a well-organized and coordinated way. Most of the organizations dealing with some aspects of aviation and flight management have adopted analytics as it helps them to develop their operational presentation and performance, asset maintenance, and profitability. It offers a solution to maximize the operating revenue of the organization. The aviation market is divided into several verticals and end-users. Given the verticals, the market is segmented into operations, finance, marketing, maintenance, and supply chain. On the other hand, the end-use market consists of airports and airlines.

Learning Outcomes of Aviation Analytics Course

Analytics has drastically changed the way the aviation sector connects with its customers, makes data-driven decisions, and builds the entire workflow to ensure quality is maintained from pre-flight to post-flight operations. This course will help you gain an understanding of how big data analytics is being used in aviation to respond to current and future market demands of this extremely complex landscape. The course aims to give you an overview of the fundamentals of Aviation Analytics and its applications and use cases. Learn from which data source the most important information is coming, to increase the operational efficiency and provide details like airport routes, predicting expected delay minutes, navigation paths, and gate arrival time. You will also learn the techniques, skills, and modern tools used for data acquisition in the aviation industry. Learn the modern approaches used for working with various data sources and the significance of big data tools in aviation. Explore the various machine learning case studies used in aviation and understand how data is generated and collected from the IoT sensors installed on the airports and flights. This course has been specially curated for students who wish to.

Examine and explore the challenges in Aviation Data Acquisition

Applications and benefits of AI and Data Science in the Construction Industry

Work with various data sources used in the Aviation sector

Advantages of Big Data tools in Aviation

Learn how to take actionable decisions using Data

Data Visualization techniques to derive insights into Aviation Data

Machine learning case studies applied to Aviation Data

Understand IoT sensors and Data generation from sensors of Flight & Airport

Learn about video analytics for the Crew Safety Management

Block Your Time

16 hours

Classroom Sessions

24 hours

Assignments

16 hours

Live Projects

Enroll Now

Who Should Sign Up?

Aviation Analysts
CFO/Director/Head of Finance
Business Analysts
Data Analysts
Risk Managers
Certified Aviation Analysts
Credit Analysts
Aviation Advisors
Math, Science and Commerce Graduates

Modules of Aviation Analytics Certification Course

These modules aim at providing the techniques, skills, and modern tools used in the aviation industry to improve operational performance and maintenance. The objective of the course is to develop an understanding of how raw data is collected from IoT sensors in the flight and airports, how it is then analyzed to maintain quality by using the applications AI and Data Science. Through these modules, you will also understand the various challenges in the Aviation industry and how actionable decisions are taken using insights from the data collected. These modules will also help students to understand how data analytic solutions influence the various aspects of aviation including revenue, maintenance, costs, and customer satisfaction.

1. CRISP ML (Q) Project Management Methodologies

CRISP-DM is a widely-used project management methodology for machine learning (ML) projects. It covers the entire ML process and is designed to be flexible and adaptable to various types of projects. The methodology includes steps such as business understanding, data understanding, data preparation, modelling, evaluation and deployment.

2. EDA and Preprocessing

Exploratory Data Analysis (EDA) and preprocessing are important steps in analyzing aviation data. EDA is used to understand the characteristics of the data and identify patterns, while preprocessing is used to clean, transform and prepare the data for further analysis. These steps are crucial to ensure accurate and reliable results in aviation data analysis.

3. Airline customer segmentation

Airline customer segmentation is the process of dividing customers into distinct groups based on demographics, behaviour, and preferences. This helps airlines to understand and target specific market segments, create personalized marketing campaigns and provide tailored service to improve customer loyalty and increase revenue. Segmentation using K means algorithm

4. Fight cancellation prediction using the Machine Learning

Machine Learning can be used to predict the likelihood of a fight being cancelled by analysing factors such as past cancellations, fighter injuries, and weather conditions. This can help event organizers plan for contingencies and make more informed decisions about event scheduling.

5. Flight ticket price prediction using linear regression

Linear regression can be used to predict flight ticket prices by analysing factors such as time of booking, destination, and historical prices etc. This can help passengers make more informed decisions about when to book their flights.

6. Predicting the customer satisfaction

Machine learning can be used to predict customer satisfaction on flights by analysing factors such as flight delays, seating arrangements, and in-flight amenities. This can help airlines improve customer satisfaction and make data-driven decisions to enhance the overall flight experience for customers.

7. Fight delay prediction using ANN/MLP

Artificial Neural Networks (ANN) or Multi-layer perceptron (MLP) can be used to predict flight delays by analysing factors such as past flight records, weather conditions, and maintenance issues etc.. This can help airlines to improve flight scheduling and reduce delays.

8. Sentiment analysis on Airline data

Natural Language Processing (NLP) can be used to analyse customer sentiment from reviews of airline data. This involves cleaning and preprocessing text data, and then using techniques such as sentiment analysis and topic modeling to identify patterns and understand customer opinions and perceptions of the airline. This can be used to improve customer service and enhance the overall customer experience

Trends in Aviation Analytics Course

Currently, the aviation analytic market is focusing to boost profitability from intelligence and analytics solutions for their business. The major factors driving the aviation analytics industry are jet fuel management, demand for real-time analytics, and increasing centricity in the aviation industry are the driving forces. Some of the significant players in the aviation analytics market include IBM, Oracle, Aviation analytics Ltd. SAS Institute, General Electric, etc. All aspects of aviation today are leveraging the benefits of using analytics for various ground and flight management operations, maintaining business aircraft services, and engineering airport profitability.

Airport Congestion is a global trend that is increasing by the day and airports across the world are expanding their operations and are using various parameters like terminal capacity, types of aircraft, travel price, etc. to identify patterns to counter the losses and to avoid accommodating overcapacity in future. Due to big data analytics a wide range of customer and operational needs are seeing the initiatives in the field of digitizing several operations in terms of real-time performance dashboards, automation in vehicle-to-vehicle communication, and predictive maintenance as many gifts of analytics to aviation. Many companies are deploying flexible and scalable cloud and digital architecture to develop an ecosystem that will support data-based decision making and help reduce costs and the ability to scale their data up or down as needed.