Data Analyst Engineer Intern

YADUNANDAN MANDALANENI

Boston, MA 832-***-**** *********************@*****.*** LinkedIn

EDUCATION

UNIVERSITY OF MASSACHUSETTS LOWELL May 2023

Master of Science in Computer Science GPA: 3.50/4.0 Relevant Coursework: Machine learning, Databases, Data Communications, IOT, Software Engineering, Advanced Network Security REVA UNIVERSITY Apr 2020

Bachelor of Engineering in Electronics and Communication Engineering SKILLS

Scripting Languages: Python, SQL, C, C++ Data Bases: MySQL, Postgres Operating Systems: Linux, Windows BI Tools: PowerBI, Tableau Familiar Tools: Github, WireShark, Jira, Jenkins Cloud Platforms: Microsoft Azure PROFESSIONAL EXPIRENCE

Data Analyst, UMASS Lowell Sep 2021 – Mar 2023

As a student data analyst was a part of expert data analysis team at UMASS Lowell, which provided data related solutions to every department in the university

Involved in project planning and was tasked with documenting all the client requirements, finally presented the requirements to the team, which was then used in creating Aims Grid

Performed data analysis with excel and python, created real time dashboards on Tableau and provided it to our clients which enabled them to keep track of their performances

Data Engineer Intern, Canara Bank Jun 2020 – Dec 2020

Developed tailored Azure-based data pipelines for ETL tasks resulting in improved scalability and run-time performance with up to 70% reduction in system resource utilization

Developed effective data models and structures to facilitate data storage, retrieval, and analysis, enabling stakeholders to make data- driven decisions based on accurate and reliable information

Collaborated with cross-functional teams to deliver high-quality solutions that met business requirements

Created and maintained technical Documentation for data solutions, ensuring easy accessibility and understanding for stakeholders PROJECTS

AtliQ Hardware Sales Data Analysis Nov 2022

Tech Stack: Tableau, MySQL Workbench, Excel, Jira

Derived sales insight from AtliQ Hardware ltd.’s steadily declining sales data containing over 150,000 transactions, Enabling sales team to take better future decisions

Involved in project planning process to create an Aims Grid to clearly covey the purpose of the project to the stake holders

Performed data analysis on MySQL workbench, and performed ETL and data cleaning tasks on tableau

Created interactive real time dashboards in tableau for revenue analysis and profit analysis, which provided the sales team with detailed pictorial representation of performance of each product, branch, and teams in the market Formula 1 Data Analysis Using Azure Databricks Jan 2022 Tech Stack: PySpark, SQL, Lakehouse, Dataframe APIs, Databricks, Azure Data Lake Storage, Azure Key Vault

Created Spark workflows with Python & SQL to analyze 5 decades of Formula 1 races data, driving insights with 100K data points

Quantified F1 race data to recognize patterns & trends of dominant drivers & teams across multiple decades

Created complex cloud-based data pipelines using Azure Data Lake Store & Azure Databricks for storage and parallel processing, improving data throughput by 40%

Utilized secure Azure Key Vault services to configure secret keys for Data Lake storage in Databricks and mounted data successfully

Used Dataframe APIs and SQL to read, transform and write data to the data lake and DBFS in delta format

Expedited data extraction & transformation by leveraging DF APIs & SQL, resulting in 5x improved latency for bi-weekly ETL tasks

Utilized PowerBI to build interactive visuals and dashboards for F1 data, overseeing 100+ tracks for better trend predictions House Price Prediction with Machine Learning Jan 2021 Tech Stack: Python, Pandas, Matplotlib, SkLearn, Excel

Developed a house price prediction model for a major metropolitan city in India using Python programming language and ML techniques such as Linear Regression

Trained and tested my model with Bangalore city housing data which contained over 13000 instances

Drew histograms to gather more insights from my data for accurate price prediction

Applied various Industry grade data science and data cleaning techniques to clean my data across various stages and achieved an accuracy of up to 90%

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