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Click here or scroll down to respond to this candidateCandidate's Name
Data Scientist, Data AnalystSummaryExperienced 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. ExperienceData AnalystIpsos, Chicago, IL Jul 2022- presentEmployer: 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 AnalystSunflower 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.EducationBachelor Of Computer EngineeringGujarat Technological University, India - 2019 7.12/10 CGPA Master Of Data Science:DePaul University, Chicago,IL- 2023 3.30 GPATechnical SkillsProjectsTitle: Twitter Data ClassificationResponsibilities: 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 Preprocessingo 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, SQLiteData 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 |
