Machine Learning Information Technology

Arif Khan ********@***.***

Tempe, Arizona (***) *14–6589 linkedin.com/in/arif-khan21 https://github.com/arifkhan10 EDUCATION

Arizona State University, Tempe, AZ Dec 2025

Master of Science, Information Technology 4.00 GPA Relevant Coursework: Analyzing Big Data, Database management Systems, Data Visualization, Natural Language Processing Rajiv Gandhi Proudyogiki Vishwavidyalaya, India Aug 2017 – Jun 2021 Bachelor of Technology, Computer Science and Engineering 3.36 GPA TECHNICAL SKILLS

• Languages: Python (NumPy, Pandas, Scikit-learn, Plotly, Matplotlib, Seaborn, SciPy, NLTK), SQL

• Visualization/Big Data Tools: Tableau, Power BI, Alteryx, MS Excel (Analysis ToolPak), Postgres, ETL Pipelines, Kibana, Stat Tools, PyTorch, Scikit-learn, keras

• Cloud Platform and Tools: AWS Cloud Platform, Google Colab, Git, JIRA, Databricks, Postman, Word, Excel, PowerPoint

• Statistical Analysis: Hypothesis testing, Regression analysis, Time series analysis, Machine Learning models, Relational Database Design, Data Preprocessing, Data Interpretation and Forecasting, Data Warehousing, predictive modeling, XGBoost

• Certifications: AWS Cloud Practitioner, AWS Solution Architect, MERN, Alteryx Core Designer, Tableau

• Other: Object-Oriented Design, Data Structures, Algorithms, Operating Systems, Complexity Analysis PROFESSIONAL EXPERIENCE

Data Analyst Dec 2024 - Present

Arizona State University Tempe, USA

• Designed and implemented data preparation workflows in Alteryx, leveraging analytical thinking and problem-solving skills to clean, transform, and prepare datasets for analysis. Exported outputs to formats like Excel, Amazon Redshift, and ODBC, ensuring seamless integration with ETL pipelines and visualization tools

• Performed advanced data cleaning, data processing, and data transformations using Pandas and NumPy in Jupyter Notebook, ensuring datasets met project requirements and delivered actionable insights aligned with organizational goals

• Developed 30+ Tableau and Power BI dashboards using data blending to monitor metrics and trends, automating processes to cut workforce hours by 39%

• Optimized query performance, improving execution speed by 15% for high-priority datasets, which enhanced overall reporting efficiency

Software Engineer Sep 2022 – Dec 2023

Prodapt Solution Pvt Ltd Bangalore, India

• Applied Tableau for advanced data visualization, improving application performance insights, optimizing workflows, and facilitating data-driven decision-making

• Using Python and SQL, I designed and deployed reliable ETL pipelines that automated data extraction, transformation, and loading procedures into Tableau, increasing dashboard performance and efficiency by 50%

• Utilized SQL for data wrangling of large, complex datasets, ensuring 98% data accuracy and consistency. Applied Tableau for optimizing workflows and enhancing application performance insights

• Employed TensorFlow to develop and integrate machine learning components, resulting in a 15% enhancement in predicting user errors

• Conducted comprehensive statistical analyses, utilizing regression and clustering techniques to uncover key user behavior trends findings led to targeted enhancements in application features that increased user engagement metrics by 25% within three months Associate Software Engineer Jul 2021 – Sep 2022

Prodapt Solution Pvt Ltd Bangalore, India

• Deployed Python for data wrangling & statistical analysis, thereby enhancing web app development and user experience

• Enhanced operational efficiency by 17% through optimizing data collection and analytics, streamlining processes for over 120 datasets, and increasing business value by 25%

• Utilized decision trees and causal inference techniques in machine learning to analyze large, complex datasets, achieving a 95% accuracy rate in predictive modeling

PROJECT WORK

Superstore Dashboard (Try it here) Mar 2024 – Apr 2024

• Initiated and crafted an interactive Tableau dashboard, analyzing 9,994 records from the Superstore dataset to visualize sales, profits, and shipping trends, streamlining data analysis and decision-making

• Identified top-performing regions contributing to over 45% of total sales and segments driving 35% of overall profitability

• Provided actionable recommendations that improved regional sales strategies, boosting projected revenue by 15% and optimizing underperforming categories by 10%

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