A Critical Evaluation of a Real-Time Data Analytics Adoption: Data Science Report, UOW
|University||University of Washington(UOW)|
On successful completion of this module students will be able to demonstrate:
- A critical understanding of the knowledge base in Data Science and its inter-relationship with other modules in the program such as Big Data and Analytics;
- An ability to work with ideas developed in Data Science at a level of abstraction, arguing from competing perspectives, and identifying the possibility of new concepts within existing knowledge frameworks and relevant approaches;
- An ability with and confidence in identifying and defining complex problems, and selecting and using investigative strategies and techniques to undertake a critical analysis of machine learning models, and evaluating the outcomes of this analysis.
An ability to analyze the new, novel, and/or abstract data using an appropriate range of established techniques relevant to machine learning algorithms; as well as judging the reliability, validity, and significance of evidence to support conclusions and/or recommendations relevant to the subject covered by this module.
This is an individual assignment. You are required to write a critical report using the following title:
“A Critical Evaluation of a Real-Time Data Analytics Adoption”
In assignment 1 of this module, you have developed a ‘data analytics engine’ using machine learning and data analytics approaches. After reviewing the benefits of the ‘data analytics engine’, you are asked to implement your analytics engine for performing a real-time data analysis implementation.
The aim of this assignment is to write a critical reflection report after developing a ‘real-time data analysis adoption strategy. This report is divided into the following tasks:
Task-1:Critically evaluate the significance of the following aspects in your real-time data analytics adoption strategy:
- Data Analytics Adoption Planning
- Marketplace dynamic and business motivation
- Automated data acquisition and processing
- Challenges of real-time data analysis
You are also required to provide examples of the above aspects in your critical evaluation. The suggested word limit for this task is 600 words.
Task-2:Critically evaluate and compare the following data processing options for your enterprise-wide data analytics strategy:
- In-memory Data Processing
- Data Stream Processing
- Complex Event Processing
Task-3: Interactive dashboards play a pivotal role in data analysis and especially for real-time data analysis. In this task, provide at least 6 examples for developing interactive dashboards to perform Business Intelligence (BI)in the user organization for the given scenario. It is also required to provide justification for the example dashboards.