tulasi

Involved in all the phases of project lifecycle starting with gathering data requirements to post implementation.
Involved in planning sessions and collecting data sources, organizing them and interpreting statistical information.
Identified solutions to strategic business problems high-level modeling, statistical analysis techniques.
Utilized Spark SQL to perform advanced-level data extraction, data transformation, data management tasks providing on the go responses to some management questions by performing complex joins, queries.
Responsible for fully documenting, managing library of source code, algorithms for future use.
Developed, tested hypotheses (t-test, F-test) using R to support research, product offerings and communicate findings to data reports/ visualization in a clear, precise, actionable manner.
Responded to operational data requests, create ad-hoc queries to support research projects.
Extensive working experience with Python 3. x including Pandas, NumPy, Matplotlib, and Scikit-learn.
Worked closely with data management, data integration teams to identify, understand, resolve data issues to improve the efficiency, productivity, scalability of data, production of data processes.
Used SMOTE to treat highly imbalanced data before prediction to improve model accuracy when symptom prediction.
Implement NLP methods using Python NLTK and SpaCy to process client data like prescriptive data, customer comments data to improve customer satisfaction.
Extensive experience in data cleaning, web scraping, fetching live streaming data, data loading & data parsing using a wide variety of Python packages like beautiful soup
Worked on data cleaning to ensure data quality, consistency, and integrity using Pandas and NumPy.
Used Power Query to connect the external data, and then shape the data. Used for data manipulation and for changing the data type, or merge tables, in ways of our own business requirements.
Led the initiative to establish end-to-end data lineage within complex enterprise systems, ensuring transparency and traceability of Data across diverse platforms and processes, contributing to enhanced data governance and compliance.
Identified and resolved issues within workflows, maintaining data integrity and minimizing disruptions.
Designed scalable workflows to accommodate data asset growth, ensuring long-term data management efficiency.
Developed standards for documenting metrics and KPI definitions while enhancing metrics/KPI descriptions.
Managed onboarding to Google Data Platform data quality tool (Dataplex) and translated business data quality rules to technical specifications for assessment.
Used data analysis packages (e1071, caTools, scikit-learn) in programming languages (R, Python) as well as use of big data tools (Spark SQL) in query, extraction, manipulation of the data to validate data quality and data preparation.
Built many machine learning models such as Random Forests, Logistic Regression, Naive Bayes, RNN to predict the billing amount for each client.
Enhanced and tuned already existing statistical models’ accuracy (linear models) for predicting the best prices for commercialization by applying an ensemble model with Linear Regression, Logistic Regression, Random Forest, XGBoosting, Feed forward Neural Network.
Integrated Automation scripts in WebDriver using continuous integration tools Jenkins for batch run of the Script.
Collaborated with other data analysts, key stakeholders to identify underlying trends, both internally, externally, impacting current, future enrolment and financial considerations by incorporating the resulting trends into forecast models to make improved predictions.
Worked independently to develop models that address specific business problems related to enrollment management, retention, marketing, class scheduling.
Used Power BI to transform the data. Create data models. Visualize data. Share assets as per business requirements.
Environment: Tableau, Python (Scikit-Learn/SciPy/NumPy/Pandas/NLTK/ SpaCy), Power BI, Statistics, Machine Learning (Random Forests, Logistic Regression, Naive Bayes, RNN), Hadoop, Hive, Pig, No SQL, PL/SQL, Excel, AWS RedShift, EC2, EMR, Hadoop Framework, S3, R, Spark (PySpark, MLlib, Spark SQL), Azure, Jenkins, Mavens.

Involved in all the phases of project lifecycle starting with gathering data requirements to post implementation.
•
Involved in planning sessions and collecting data sources, organizing them and interpreting statistical information.
•
Identified solutions to strategic business problems high-level modeling, statistical analysis techniques.
•
Utilized Spark SQL to perform advanced-level data extraction, data transformation, data management tasks providing on the go responses to some management questions by performing complex joins, queries.
•
Responsible for fully documenting, managing library of source code, algorithms for future use.
•
Developed, tested hypotheses (t-test, F-test) using R to support research, product offerings and communicate findings to data reports/ visualization in a clear, precise, actionable manner.
•
Responded to operational data requests, create ad-hoc queries to support research projects.
•
Extensive working experience with Python 3. x including Pandas, NumPy, Matplotlib, and Scikit-learn.
•
Worked closely with data management, data integration teams to identify, understand, resolve data issues to improve the efficiency, productivity, scalability of data, production of data processes.
•
Used SMOTE to treat highly imbalanced data before prediction to improve model accuracy when symptom prediction.
•
Implement NLP methods using Python NLTK and SpaCy to process client data like prescriptive data, customer comments data to improve customer satisfaction.
•
Extensive experience in data cleaning, web scraping, fetching live streaming data, data loading & data parsing using a wide variety of Python packages like beautiful soup
•
Worked on data cleaning to ensure data quality, consistency, and integrity using Pandas and NumPy.
•
Used Power Query to connect the external data, and then shape the data. Used for data manipulation and for changing the data type, or merge tables, in ways of our own business requirements.

Created and analysed business requirements to compose functional and implementable technical data solutions.
Identified integration impact, data flows and data stewardship.
Created new data constraints and or leveraged existing constraints for reuse.
Created data dictionary, Data mapping for ETL and application support, DFD, ERD, mapping documents, metadata, DDL and DML as required.
Anticipated JAD sessions as primary modeler in expanding existing DB and developing new ones.
Evaluated and enhanced current data models to reflect business requirements.
Generated, wrote and run SQL script to implement the DB changes including table update, addition or update of indexes, creation of views and store procedures.
Consolidated and updated various data models through reverse and forward engineering.
Compared different DB environments and determined, resolved and documented discrepancies.
Analyzed DB discrepancies and synchronized the Staging, Development, UAT and Production DB environments with data models.
Worked on data cleaning to ensure data quality, consistency, and integrity using Pandas and NumPy.
Improved model performance by using random forest and gradient boosting for feature selection.
Wrote Spark SQL queries for data analysis to meet business requirements.
Created multiple custom SQL queries to prepare the right data sets for Tableau dashboards. Queries involved retrieving data from multiple tables using various join conditions that enabled the utilization of efficiently optimized data extracts for Tableau workbooks
Reviewed and revised data models for soundness of data structures and adherence to client standards.
Restructured Logical and physical data models to respond to changing business needs and to assured data integrity using PowerDesigner.
Created naming convention files and co-coordinated with DBAs to apply the data model changes.

Communicated effectively in both a verbal and written manner to client team.
Completed documentation on all assigned systems and databases, including business rules, and processes.
Created Test data and Test Cases documentation for regression to validate performance.
Conducted one-to-one sessions with business users to gather data for Data Warehouse requirements.
Part of the team analyzing database requirements in detail with the project stakeholders through Joint Requirements Development (JRD) sessions.
Developed an Object modeling in UML for Conceptual Data Model using Enterprise Architect.
Developed logical and Physical data models using Erwin to design OLTP systems for different applications.
Worked with DBA group to create Best-Fit Physical Data Model with DDL from the Logical Data Model using Forward engineering.
Created entity process association matrices using Zachman Framework, functional decomposition diagrams and data flow diagrams from business requirements documents.
Involved in detail designing of data marts by using Star Schema incorporating shared dimensions.
Worked with the ETL team to document the transformation rules for data migration from OLTP to the Warehouse environment for reporting purposes.
Developed data mapping documents between Legacy, Production, and User Interface Systems.
Generated comprehensive analytical reports by running SQL queries against current databases to conduct data analysis.
Maintained the data integrity during extraction, manipulation, processing, analysis, and storage.