Dynamic and Enthusiastic business analyst with hands on experience of business analyst intern seeking to drive impactful changes in leading companies with my business analyst knowledge.
User stories and Acceptance Criteria were assisted by understanding the requirements of product owner.
Acceptance criteria and Business value (BV) provided by product owner and complexity values by scrum team.
Added all the user stories to the product backlog.
Prioritized all the user stories using MoSCoW technique.
Added user stories to sprint backlogs based on prioritization order.
Assisted DOD and DOR checklist.
Assisted in planning the sprint meetings which are sprint planning meeting, sprint retrospective meeting, sprint review meeting and daily standup meeting accordingly.
Generated product burns down charts and sprint burn down charts.
Assisted a business case document by understanding the problem of the client.
Gathered requirements from the clients using Elicitation techniques.
Prioritized the requirements using Moscow techniques.
Documented all the requirements (BRD).
Performed stakeholder analysis and identified the stakeholders involved.
Also performed GAP analysis, SWOT analysis to analyse the project.
Worked on MS Visio to draw use case diagrams and activity diagrams.
Documented epics and user stories based on priority.
Created user story acceptance criteria.
Worked on process flow diagram.
Worked on Balsamiq to make prototypes and wireframes.
Process Modeling
Drawio
Sprint Backlog
Sprint Planning
Product Backlog
sprint ceremonies
Requirement Gathering
Requirement elicitation technique
Requirement prioritization
epics
User Stories
JIRA
Brd
Business Analysis
Documentation
Manual testing
UAT
SDLC
Agile
Scrum
Impact Analysis
Swot Analysis
Product Development
GAP Analysis
Wireframes
Balsamiq
MS Visio
Analysis
MS office suites
Good with cross functional teams
Academic project:
MEDICAL DIAGNOSIS OF HUMAN HEART DISEASE WITH AND WITHOUT USING GRID SEARCHCV (December 2022 to may 2023)
Anticipating cardiovascular illness is viewed as quite possibly of the most difficult undertaking in the clinical field.
It requires a great deal of investment and work to sort out what's causing this, particularly for specialists and other clinical specialists.
In this project, different Machine Learning algorithms like Support Vector Machine(SVM),Linear Regression(LR), Gradient Boosting Classifier(GBC),Random Forest(RF),Decision Tree(DT) and KNN along with the GridSearchCV to predict the cardiovascular disease.
The framework involves a 5-fold cross-validation technique for verification. A comparative study is given for these six methodologies.
The Heart Disease Datasets are utilized to investigate the models' performance.