Who should attend
This course has been designed for analytics professionals and managers seeking to learn about structuring a big data project (in any domain), getting an overview of the end to the end development cycle, and analytics model development, aggregation & monitoring.
- Data Analysts
- Data Scientist
- Data Engineers
- Data Product Managers
About the course
The projects dealing with Big Data are not completely tied to one’s unique objectives. These projects are just thought of as scientific with no business goals or metrics. To gain the maximum benefit out of it, you need to point your Big Data to a specific need or problem of your business. To justify your investments for Big Data projects, you would require showcasing your results continuously. The demand is for business needs having rapid and agile data access. Businesses look for very low costs for data-driven discoveries. If operated properly, big data offers a wide range of possibilities to businesses today and in the future. The problem lies in the lack of not only skilled professionals and failure in proper execution but a lesser time in understanding the business case and no end-user research.
The key to success is a problem-first mindset, not the data or model first.
This course has been designed to equip analytics professionals and managers with an understanding of how big data analytics projects can be structured for successful adoption.
The main objective of this course is to provide attendees with a practical understanding of structuring big data projects for analytics, technical aspects of the project, and model development, aggregation, and monitoring.
At the end of the course, the participants will be able to:
- Structure Big Data Projects & Data Readiness Evaluation
- Understand the pre and post-data collection perspective of a Big Data project for analytics.
- Design a suitable strategy for harnessing Big Data for analytics to create business value.
- Classify and contextualise different tools and solutions to deal with the technical landscape.
- Understand the importance of data quality and the importance of pilot programs to check data and business users' readiness.
- Data Pipeline Designing & Feature Engineering
- Understand the importance and need of data pipelines, their components, and how to organise data pipeline components to automate end-to-end data flow.
- Understand the domain-specific variation needed for feature engineering and model development.
- Develop Models & Plan Model Aggregation, and Monitoring
- Some best practices for model development, and monitoring
- Model aggregation for efficient deployment in different scenarios
What Will Be Covered
This course will cover:
- Structuring Big Data Project for Analytics
- Design a strategy to harness big data
- Data Readiness Evaluation
- Data pipelines and their importance
- Feature Engineering
- Model Development & Monitoring
- Model Aggregation
Liu Fan currently lecturers in the Software Systems Practice in the areas of software engineering, big data engineering and data analytics. She received her Ph.D Ph.D. degree from School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Prior to joining ISS, ...
Sarita Singh received her Ph.D. degree for her work done in the area of Information Security (Cryptography). She is the recipient of the prestigious Infosys fellowship for pursuing her Ph.D. Programme. She has more than twenty-five years of work experience in areas including teaching and researc...
Experience National University of Singapore Full-time, Nov 2020 – Present Siemens Advanta Consulting Data Scientist, Jan 2019 – Nov 2020 Dyson Data Insights Analyst Company NameDyson, May 2017 – Jan 2019 IHG Hotels & Resorts Revenue Analytics Intern, Aug 2016 – Mar 2017 Hindustan...
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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.