Darshana Mathur

 Data Analyst, PwC, (March 2022 - Present)

• Employed Python (pandas and NumPy) to perform meticulous data cleaning, enhancing accuracy by 30% handling missing values, outliers, and data inconsistencies. 

• Conducted detailed Exploratory Data Analysis (EDA) using Python on diverse datasets, uncovering critical trends and patterns that led to a 20% increase in understanding of customer behaviors and preferences 

• Developed interactive dashboards in Power BI, effectively presenting complex segmentation data and insights in a user-friendly manner. 

• Provided actionable, data-driven marketing strategies, resulting in a 25% increase in marketing ROI and a 15% rise in customer engagement by tailoring approaches to each customer segment. Offered recommendations on optimizing marketing resources by targeting high-value customer segments. 

• Presented findings and strategic recommendations to key stakeholders, including the marketing and sales teams, enhancing inter-departmental collaboration and decision-making.  

 Data Analyst, Unique Traders, (Janurary 2020 - July 2021)

• Led the development and implementation of advanced financial tracking and operational metrics systems using SQL and Python, enhancing forecasting accuracy by 10% and ensuring timely reporting. 

• Designed and executed robust data quality checks with Excel and SQL, reducing data errors by 20% and increasing data reliability. 

• Utilized Power BI and Python for in-depth cost analysis and financial feasibility studies, aiding in investment decisions that yielded a 10% return on investment. 

• Collaborated with cross-functional teams, using SQL and Python to identify key performance indicators and financial metrics, driving data-driven decision-making across the organization. 

• Presented analytical findings and recommendations to senior management through clear, concise reports and dashboards, influencing strategic planning and operational improvements. 

 Graduate Research Assistant, UTA Data Research Services , (January 2023 - July 2024)    

• Designed ETL procedures for the improvement of the distribution of 600+ university student records, leading to passing rate of 97%.

• Worked in a 4-member team to analyze data using SQL queries for data processing and Power BI dashboard for presenting solutions to upper management, resulting in 15% increased finance for asset installations and procurement.

• Provided analytical support to doctorate candidates for Python and SQL, developing solutions to resolve issues for 80+ students. 

• Developing Visualizations, dashboards and workshops with the marketing team increasing the rate of arrival of students by 30%. 

 Graduate Student Associate, UTA Admissions and Enrollment Management , (November 2021 - December 2022)

• Led a team of 11 members under Director of Graduate Students for training the chatbot, thereby increasing its efficiency by 65%.

• Developed automation methods for data manipulation, to analyze large data sets using SQL queries thereby answering student questions with excellent written and verbal communication skills, reducing the mailbox size by 300%.

• Led the conflict management team, to resolve issues in decision making process, reducing number of pending issues by 60%.

• Developed an Ensemble Method Predictive model with the use cases from previous records to generate operating procedures and protocol of decision making for higher authorities, thereby reducing processing time by 70% and decreasing cost by 15%.

• Collaborated with the marketing team to receive access to previous year databases to perform data manipulation and analysis, thereby evaluating financial performance. 

• Developed Employee Assessment methods using Excel focusing on Integrity, and Innovation to generate more efficient results. 

Master's of Science, Data Science, University of Texas at Arlington

Bachelor's of Technology, Electrical Engineering, Pandit Deendayal Petroleum University 

Starbucks Dashboard - Analyzing Customer and Offer Behavior

• This Project includes and overview of the Customer and their transaction data for Starbucks from year 2013 to 2017 to understand various demographics and research areas of improvement. 

Airline Review EDA and Sentiment Prediction

• This Project includes extensive feature engineering, data cleaning, manipulation, and wrangling for an airline review dataset to prepare a sentiment prediction model with 98% accuracy. 

Restaurant Recommendation System

• This Project provides an overview of recommendation systems, their importance in today's data-driven world, and how they work, including an example of using cosine similarity to find related restaurants based on user input. 

Shark Tank EDA

• This project focuses on the TV show Shark Tank, utilizing SQL and Python to perform an exploratory data analysis on a collected dataset, and answering key questions using statistical tests and visualizations. 

Drowsiness Detection using CNN and Haar Models

• Developed a system to capture the eyes from the camera and scoring their open or shut status, using CNN and Haar Models, thereby detecting drowsiness.  

Student Database Management System

• Developed an interactive Student Database Management system for the handling the enrollment of students and performing complex operations on SQL using Python, for better handling of records thereby reducing error by over 40%. 

1. AWS | Build a Text Model Classifier with Amazon Glue and SageMaker 

2. AWS | Data Analytics Fundamentals

3. AWS | Introduction to  Amazon Athena

4. AWS | Introduction to Amazon Quicksight

5. AWS | Financial Services Learning Plan Artificial Intelligence and Machine Learning

6. AWS | Introduction to Amazon Transcribe

7. AWS | Introduction to Amazon Rekognition

8. LinkedIn Learning | Data Engineering with AWS

9. LinkedIn Learning | Machine Learning with Python

10. LinkedIn Learning | Advanced SQL for Data Scientists

11. LinkedIn Learning | SQL for Data Analysis

12. LinkedIn Learning | Python for Data Analysis

13. LinkedIn Learning | Statistics for Data Analysis

Comparative Analysis of MPPT Techniques, NPSC 2020, Indian Institute of Technology Gandhinagar

The isolated locations across the globe, where the grid is not accessible, the renewable energy based standalone systems can play a significant role as a power generating system. Due to the prolific solar insolation available as a resource, solar PV plays a crucial role in the renewable energy sector. Generally, Solar Photovoltaic Energy Systems are incorporated with maximum power point tracking methods, to enhance the effectiveness of power transformed from generation to the load. This paper comprises of comparative analysis of four MPPT methods, and their comparative analysis. The MPPT methods are Perturb and Observe (P&O), Incremental conductance (INC), Fractional Open Circuit Voltage (FOCV) and Fractional Short Circuit Current (FSCC) taken for the consideration. To regulate the DC voltage, these methods are incorporated with DC-DC converter. The performance of these MPPT methods are checked with comparative analysis for various permutations and combinations for climatic conditions such as insolation and temperature. The algorithms are also compared with sudden and gradual change in climatic conditions.