77 N Almaden Ave, email@example.com San Jose, CA 95110 Abdullah Nawaz Khan +1 (408)-680-1424 Summary: Detail-oriented Data Analytics graduate student with 2 years of work experience focusing on Business Analysis and the Software Development Life Cycle. Skilled in using Python, PySpark, Apache Beam & SQL for end-to-end software development. Education: MS Data Analytics, San Jose State University, San Jose, CA (GPA: 3.8) May 2022 BS Computer Engineering, Purdue University, West Lafayette, IN May 2017 GitHub: https://github.com/khan85 Key Skills: Python, SQL, Beam, PySpark, Hadoop, Google Cloud Platform. Coursework: Big Data Technology, Database Systems (NoSQL coursework), Computer Architecture, Mathematical Methods for Data Analysis, Discrete Mathematics in Computer Engineering. Technical Projects: Financial Stock Data Pipeline on Google Cloud Platform Oct 2020 – Dec 2020 • Implemented a unified end-to-end data processing pipeline supporting both batch & streaming updates. Focused on technical stock analysis to study past & present price action to create trends and draw analytical insights on future price movements. • Ingested market-wide financial stock data using Cloud Functions (Python), Pub/Sub (streaming) & Cloud Storage (batch). • Processed data using Dataproc (PySpark), Dataflow (Beam) & BigQuery. • Explored stock trends by creating reports using Google Data Studio & BigQuery ML (Time Series, Regression Analysis, etc.). Wine Quality Exploration Analysis Nov 2020 – Dec 2020 • Applied K-Means Clustering algorithm to analyze how wine quality rating clusters based on age. Also, employed TSNE & PCA dimensionality reduction techniques for easy visualization of results (unsupervised machine learning analysis). • For further analysis, utilized the K-Nearest Neighbor algorithm to predict red wine quality achieving 84% accuracy on the data model. Generated a confusion matrix & classification report to summarize results (supervised machine learning analysis). • Utilized Pandas, NumPy, scikit-learn & other python libraries for implementation. Home Value Predictor Oct 2020 – Nov 2020 • Programmed a Home Value Predictor to factor 12 independent variables (including GIS data) for predicting the value of properties in Kings County, Seattle by implementing Multiple Regression Analysis. • Utilized Pandas, NumPy, scikit-learn & other python libraries for implementation. Work Experience: Disney Cruise Line, Business Systems Analyst (Consultant) Jul 2018 – Apr 2020 • Successfully deployed multiple packaged vendor solutions, replacing legacy custom-built systems to significantly improve the guest experience & support a 43% increase in fleet size critical to meeting long-term business targets. • Acted as the primary point-of-contact between the Business, Development team & the QA team to deploy shipboard & shoreside applications. • Created future state architecture diagrams by collaborating with the Solution Engineering Team & worked with project stakeholders to get sign-offs while ensuring solution aligns with business requirements. • Managed scope, project goals, priorities, and timelines using VersionOne. • Established regular cadence of Daily Stand-up, Backlog Refinement, Iteration Planning, Sprint Review & Retrospective while reinforcing best practices and other Agile Scrum processes. • Effectively managed project risks and changes to ensure successful deployments to the production environment. The Walt Disney Company, Business Systems Analyst (Consultant) Dec 2017 – Jun 2018 • Designed and developed the complete data analytics & reporting strategy for 10 business units during the implementation of a packaged Integrated Scheduling & Time system impacting more than 70000 employees. • Engaged ~30 stakeholders to collect requirements and proposed future state reporting architecture incorporating WFC Standard Reports, WF Analytics, and a Workforce Management Data Lake on AWS. • Acted as a liaison between IT and Business Project Team to develop Interface Design Documents and Business Requirements Documents. • Created dashboards and reports meeting on the fly business requirements using Python, SQL, Tableau, SAP Business Objects & Zeppelin Notebook. Image Processing Team, Undergraduate Researcher Jan 2017 – May 2017 • Developed tools to detect poor quality photos & advise users of Poshmark on achieving better quality pictures. • Gained robust experience implementing K-Means Clustering & Gaussian Mixture Model. • Utilized MATLAB & Python for implementation.