PARTH PATEL

Data Scientist, Data Analyst

Summary

Experienced Data Analyst with proficiency in SQL, Python, and Excel, delivering over a year of impactful data processing. Expert in creating compelling reports and dashboards using Tableau and Power BI, with a proven ability to translate complex insights into strategic recommendations for data-driven decision-making. Committed to contributing valuable information to drive business growth. Experience

Data Analyst

Ipsos, Chicago, IL Jul 2022- present

Employer: CodersData LLC, Cheyenne, WY

● Utilized Python and R to perform exploratory data analysis, data cleaning, and data preprocessing tasks on diverse datasets.

● Developed interactive dashboards and reports in Power BI to visualize key business metrics and track performance against targets.

● Implemented predictive models using machine learning techniques, such as regression, classification, and clustering.

● Presented findings and recommendations to senior management through clear and concise reports and presentations.

Data Analyst

Sunflower Lab, Gujarat, India Jul 2019 – Aug 2021

● Analyzed large datasets using Python and R to identify trends, patterns, and correlations, resulting in improved decision-making processes.

● Developed advanced SQL queries to extract and manipulate data from relational databases, ensuring data integrity and accuracy.

● Designed and implemented data visualization dashboards in Power BI and Tableau, providing stakeholders with intuitive insights into key performance metrics.

● Applied machine learning algorithms to predict customer behavior, optimize marketing campaigns, and increase ROI by 20%.

● Collaborated with cross-functional teams to define project requirements, establish KPIs, and drive data-driven initiatives across the organization.

● Conducted ad-hoc analyses to support business operations, such as market segmentation, customer churn analysis, and sales forecasting.

Education

Bachelor Of Computer Engineering

Gujarat Technological University, India - 2019 7.12/10 CGPA Master Of Data Science:

DePaul University, Chicago,IL- 2023 3.30 GPA

Technical Skills

Projects

Title: Twitter Data Classification

Responsibilities:

● Utilized Pig, Hadoop, and Spark for processing large amounts of data.

● Implemented various algorithms such as decision tree, random forest, and support vector machines for data classification.

● Conducted feature selection and engineering techniques to improve the accuracy of the models.

● Analyzed and visualized the results using tools such as Tableau and Matplotlib.

● Collaborated with cross-functional teams to deliver insights and recommendations.

● Technologies: Pig, Hadoop, Spark, Decision Tree, Random Forest, Support Vector Machines, Tableau, Matplotlib.

Outcome:

● Successfully classified the Twitter data set with an accuracy rate of over 95%.

● Improved the speed and efficiency of data processing by utilizing Spark.

● Provided actionable insights and recommendations to stakeholders. Title: Advertisement Click Classification

• Objectives:

To predict the intended ad based on user information using machine learning methods Analyze the prediction model's performance and compare it with different classification techniques

• Analysis Approach:

o Data Cleaning and Preprocessing

o Exploratory Data Analysis (EDA)

• Model Development:

o Using various machine learning algorithms such as Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Naive-Bayes, KNN, Support Vector Machine, etc. o Train the model on a subset of the data and test on a separate dataset to evaluate its performance.

Title: FIFA Player Wage Prediction using Supervised ML Model

● Conducted deep regression analysis on FIFA data sets to predict player wages and salaries

● Implemented feature selection using Lasso and Ridge regression techniques

● Achieved a 67% prediction accuracy using Root Mean Squared Error (RMSE)

● Improved model performance to 90% explained variation using Stochastic Gradient Descent with a 0.90 R-squared value.

Languages: Python, SQL, R, NoSQL, SQLite

Data Analytics and visualization Tools: Tableau, Power BI, IBM SPSS Cloud and Big Data Technologies: Microsoft Azure, AWS, S3, Hadoop, PIG, Spark,Hive Libraries and Framework : NumPy, Pandas, matplotlib, Scikit-learn, Seaborn, TensorFlow, Pytorch Machine Learning and AI Techniques: SVM, Logistic Regression,Naive Bayes, Decision Tree,Random Forest, KNN, K-mean, Linear regression, XGboost,TF DIF

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