Digitization and huge data proliferation in every domain has created an immense opportunity to apply Data Analytics and Machine Learning to make data-driven decisions that helps to increase efficiency in operations, service delivery, cost optimization and improve several other business metrics. Data Analytics solutions that use Machine Learning and Data Science techniques along with advanced visualizations are becoming very important for the success of any business.
In this Track, we are looking for technical submissions in the broad field of Data Science and Machine Learning, describing the tools and techniques used to solve real-world problems.
Topics of interest include, applied machine learning in various domains such as:
- Retail and E-commerce
- Health Care and Bio-informatics
- Logistics and operations
- Smart Cities
- Public Safety
- Marketing and Market predictions
- Smart Energy
- Analytics for Internet of Things
In addition, submissions describing pure data science methodologies will be accepted, in the below categories:
- New machine learning techniques
- Novel data analytics platforms
- Graph algorithms
- Temporal and Spatial Analysis
Submissions should describe the author’s novel work assuming minimal mathematics pre-requisites from the audience and should cater to an audience who is a general computer science engineer.
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