Data Science Machine Learning

PARAG GAWANDE

Master’s in Data Science

Email ID : ******@*****.*** LinkedIn :www.linkedin.com/in/parag-gawande Phone : 734-***-**** PROFESSIONALSKILLPROFILE

● Python ● SAP ● Ms Office

● Alteryx ● R ● GA4

● PySpark ● PowerBI ● SQL

EDUCATION

University Of Michigan Dearborn,US

Master’s in Computer Science (Specialization in Data Science) 2025 Grade - 3.85

CERTIFICATE

● Databases and SQL for Data Science with Python

● Programming for Everybody (Getting Started with Python)

● Google Analytics Certification (GA4)

● Alteryx Foundation Micro-Credential

PROJECTEXPERIENCE

Case study of Global Bike using SAP HANA Cloud Analytics Projects SAP HANA Cloud Analytics, R

● AnalyzetheSalesdatafrom2007-2019ofanindependentBikecompanywithdistributionintheUS andGermany.Objectivetoincreasesalesofeachstoreindifferentlocationsusingdifferentmeasures and dimensions like sales, quantity, price, discount etc.

● Data Visualization in Word cloud using R programming. Predictive Analysis of CO2 Emissions by Automobiles using Regression Models Python

● Analyzed CO emissions trends over a 7-year period using Canadian Government vehicle data

● Using machine learning models such as Linear,RandomForest,andXGBoostregressiontomeasure various factors like MSE,Redual,MAE to evaluate factors affecting most emissions. Effective Road Segmentation by analyzing accidents due to autonomous vehicles Python

● Analyzed datasets like Tesla Deaths,KITTI-road segmentation,and CamVid to improve image processing.

● Utilized Python libraries such as TensorFlow,Matplotlib,CV2,Numpy,and Pandas,to train and compare different U-Net models (e.g.,VGG,Residual,Attention,Dense,MultiRes)for better road segmentation, aiming for higher accuracy and real-time performance. Enhanced Intrusion Detection using Data Analysis PySpark

● Apache Spark utilized the LUFlow Network dataset which contains simulated intrusions andnormal activities.

● Evaluate various machine learning algorithms like RandomForest,LogisticRegression,and SVM on various factors like precision, recall, F1 score, and accuracy

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