Specifications : Data Analyst, Business Analyst
Skills
Python, Django Rest Framework, Database Management System, Data Research, SQL, Pandas, Matplotlib, Seaborn, Power BI, Exploratory Data Analysis, Machine Learning, Data Visualization, Linear Algebra, Data Modelling, Excel, R Programming, Inferential Statistics, Stochastic Process,, Problem Solving, Critical Thinking, Writing, Leadership, Teamwork, Collaboration, Management,Languages
Hindi, English,Hobbies
Reading, Writing, Playing Video Games, Travelling, Volunteering
Summary
Seeking opportunities to gain hands-on experience in the industry, I am driven by a strong passion for analytics. My mathematical background equips me to think critically and find solutions skillfully. As I progress through my data science master's program, I am exploring vital subjects such as machine learning, big data analysis, data extraction, and advanced statistical approaches. Additionally, I am actively engaging in collaborative projects, internships, and scholarly research to apply my knowledge in real-world settings and stay updated on cutting-edge methodologies. My goal is to have a meaningful impact on data science initiatives and teams by combining academic knowledge with practical proficiency.
Education History
Internships
During my internship as a Data Scientist at Evoastra Ventures Private Limited, I worked on a variety of data-driven projects, where I focused on leveraging data to provide actionable insights and support business decisions. My role involved data collection and preprocessing, where I utilized web scraping techniques to gather accurate and clean datasets, handling tasks such as data normalization and transformation. I conducted exploratory data analysis (EDA) to uncover patterns and trends, and created visualizations using tools like Matplotlib, Seaborn, and Tableau to effectively communicate these findings to stakeholders. Additionally, I developed and implemented machine learning models to solve specific business problems, involving tasks like feature selection, model training, and evaluation. I optimized model performance through hyperparameter tuning and evaluated models using metrics such as accuracy, precision, recall, and F1-score. Throughout the internship, I collaborated with cross-functional teams to understand project requirements and deliver data-driven solutions, preparing reports and presentations to communicate findings to both technical and non-technical stakeholders. This experience provided me with a comprehensive understanding of the end-to-end data science process and honed my skills in data analysis, machine learning, and effective communication.
- Learned and implemented web scraping techniques to collect relevant data from multiple sources.
- Developed and optimized ETL (Extract, Transform, Load) pipelines for efficient data handling.
- Uploaded and managed large datasets in the company’s database, ensuring data integrity and accuracy.
- Collaborated with the team to automate data extraction processes, contributing to improved workflow efficiency.
- Gained hands-on experience with database management systems, data transformation, and data cleaning processes.
Projects
Designed and implemented a robust Hospital Management Website using Django for backend development, complemented by HTML, CSS, and JavaScript for a responsive and dynamic user interface. The website is equipped with several critical features:
- Role-Based Access Control: Different dashboards for administrators, doctors, nurses, and patients, ensuring secure and personalized access to relevant features and data.
- Patient Records Management: Centralized system for storing and managing patient information, including medical history, treatment plans, and prescriptions, with secure access and privacy controls.
- Appointment Scheduling System: Simplified process for booking, managing, and tracking appointments, with automated notifications and reminders to both patients and healthcare providers.
- Reporting and Analytics: Real-time dashboards and detailed reports for monitoring hospital performance, patient statistics, resource utilization, and other key metrics.
In this project, I conducted an in-depth Exploratory Data Analysis (EDA) using Python, leveraging powerful libraries such as Pandas, Matplotlib, and Seaborn. The project involved several key stages:
- Data Collection: Gathered data from various sources, ensuring its relevance and completeness for the analysis.
- Data Cleaning and Preprocessing: Used Pandas to clean and preprocess the data, handling missing values, outliers, and data normalization to prepare it for analysis.
- Data Visualization: Employed Matplotlib and Seaborn to create a wide range of visualizations, including histograms, scatter plots, box plots, and heatmaps, to uncover patterns, trends, and correlations within the data.
- Statistical Analysis: Performed descriptive statistics to summarize the central tendency, dispersion, and shape of the data’s distribution, providing insights into the underlying structure of the dataset.
- Feature Analysis: Analyzed the relationships between different variables to identify key features that significantly impact the target variable, using visualizations and statistical methods.
- Insights and Reporting: Summarized the findings from the analysis, providing actionable insights and recommendations based on the data, supported by visual evidence.
This project highlights my ability to handle and analyze complex datasets, using advanced Python libraries and data visualization techniques to draw meaningful conclusions and insights.