• Planned, installed, and supported operating systems, software, and databases including Software as a Service (SaaS) solutions, to ensure the system's integrity, security, backup, and performance are optimized.• Configured and set up backup software on customers' devices to enable seamless and automated cloud backup.• Tested, evaluated, and maintained the core functionalities of a software suite, including product design, technical documentation, software architecture, and functional modules.• Provided technical support for SaaS-based solutions products, including planning upgrades, implementing smart storage solutions, and assisting with data restoration.

• Data collection and wrangling data for analysis. • Model building to identify the risk associated with trade jobs.

Project: Digitization of Cultural Qualities Initiative.Task: Develop a chatbot for La Trobe staff to share culture practice stories.Tools used: Google Dialogflow, Python and Facebook messenger.

Project : Exploring nutrition trends and consumer attitudes in popular home cooking video tutorials: a data analytics exploration using YouTubeI participated in this internship program where Analysing trending video tutorials associated with “How to cook healthy” allows dietitians to track consumer dietary behaviour and nutrition trends and identify potential gaps in cooking skills and advice sought by consumers. It was an individual project supervised by industry experts.Tools: Python, PowerBI, ExcelI was involved in following tasks:• Harvest videos from YouTube trending video tutorials associated with “How to cook healthy”• Mine the number of views and likes/dislikes from each video post to enable the segmentation into popular categories or topics and trends over time• Generated descriptive analysis on the data• Perform text analysis to generate word clouds, bigrams, sentiment analysis and topic modelling• Develop a dashboard using PowerBI which includes all the above analysis• Challenges faced:- While harvesting videos there were many ad videos and irrelevant videos to filter for our analysis- Youtube API was not providing all the data which the client was expecting so I had to use web scraping along with Python to harvest all the data required