Data Analyst Sql Server

Navyasri Duddela

Data Analyst

Email: *****@*********.*** Mobile: 732-***-**** Location: MO, 63103 LinkedIn SUMMARY

Experienced Data Analyst with a strong background in Python, R, SQL Server, and AWS. Skilled in optimizing ETL processes, conducting statistical analysis on large datasets, and implementing machine learning models for predictive analysis. Proficient in data visualization tools like Tableau, Power BI, and Excel, with a focus on improving data accessibility and driving strategic decision-making. Expertise in ML/AI models, data cleansing, and transformation, with a solid understanding of Agile and Waterfall methodologies for precise project management. Experienced in managing MySQL, MS SQL Server, and NoSQL databases, enhancing operational efficiency and decision-making through comprehensive analysis and reporting. SKILLS

Methodologies: SDLC, Agile, Waterfall

Languages: Python, R, SQL, SAS

Cloud Technologies: AWS, Azure, GCP, DataBricks

Databases: MS SQL, Hive, SQL Server, Oracle, Redshift, Snowflake, AWS S3 AI/ML Algorithm: Linear Regression, Logistic Regression, Decision Trees, Supervised Learning, Unsupervised Learning, SVM, Random Forests, Naive Bayes

Packages: NumPy, Pandas, Keras, Matplotlib, SciPy, Seaborn, TensorFlow, Plotly, SkLearn, ggplot2 Tools: Tableau, QlikView, Power BI, DAX, MS Excel

Platform/IDE: Jupyter, PyCharm, Visual Studio Code, Hadoop, R-Studio Version Control Tools: GIT, GitHub, JIRA

ETL Tools: SSIS, Informatica, Apache Spark, Oracle Analysis Techniques: Data Visualization, Data Mining, Data Warehousing, Data Cleaning, Predictive Analysis, Statistical Modelling, Data Wrangling

Operating System: Windows, Linux, Mac

EXPERIENCE

Data Analyst, Berkshire Hathaway USA

Jan 2024 – Present

Managed software development projects using SDLC methodologies, including Agile and Waterfall, achieving 95% on- time delivery and 98% alignment with business objectives.

Implemented DAX functions for extracting and transforming datasets, enhancing actionable insights from diverse sources.

Developed JavaScript applications for data analysis, achieving a 25% reduction in data processing time.

Optimized ETL processes using SQL and Apache Spark, resulting in a 30% improvement in data processing efficiency and integrity.

Integrated data from 10+ sources into Power BI, improving data-driven decision-making processes by 30%.

Applied Python (Pandas, NumPy, Matplotlib, Seaborn, Plotly) to analyze datasets, supporting strategic initiatives with a 20% increase in efficiency.

Utilized TensorFlow and Keras for machine learning models, improving predictive accuracy by 20%.

Managed AWS services (S3, EC2, Redshift) for scalable data storage and processing, reducing infrastructure costs by 25%.

Creating adhoc EXCEL based tools for reporting, position reconciliation and proactively monitoring the potential errors.

Utilized advanced Excel functions (e.g., VLOOKUP, INDEX-MATCH, SUMIFS) to manipulate and analyze large datasets, streamlining data processing and improving efficiency.

Designed impactful PowerPoint presentations with integrated Excel charts and graphs, effectively communicating data insights to stakeholders and executive teams.

Prepared comprehensive data visualizations and summaries in PowerPoint for client presentations and internal meetings, enhancing the clarity and impact of business proposals.

Produced detailed Tableau reports, enhancing data accessibility by 30% and supporting a 25% improvement in strategic planning and execution.

Conducted comprehensive data visualization using Tableau and Power BI, transforming complex data into clear visual insights for executive presentations.

Applied data mining techniques to uncover patterns and trends within customer behavior data, leading to targeted marketing strategies that increased ROI by 15%.

Implemented data warehousing strategies using AWS Redshift, ensuring efficient data storage and retrieval for analytical purposes.

Utilized advanced statistical modelling techniques, including regression and time series analysis, to forecast sales trends with 95% accuracy.

Led data cleaning initiatives, utilizing Python and SQL scripts to maintain data integrity and accuracy, reducing error rates by 20%.

Data Analyst, Bytes Soft Solution India

Jun 2019 – Jul 2022

Integrated Azure/AWS services for workflow automation, enhancing overall processing efficiency by 30%.

Managed structured and unstructured data using Data Lake Storage and GCP Cloud Storage, enabling analysis of datasets over 50 terabytes.

Implemented data cleaning, validation, and wrangling techniques, ensuring data accuracy and reliability.

Implemented AI/ML algorithms, including Linear Regression, SVM, and Random Forests, achieving 85% accuracy improvement in predictive models.

Designed impactful visualizations using Tableau, Power BI, and Excel, improving data accessibility by 40%.

Developed interactive data visualizations with R Shiny, facilitating data-driven decision-making processes.

Managed MySQL and SQL Server databases for efficient storage and retrieval of datasets exceeding 10 million records.

Utilized advanced Excel functions (VLOOKUP, INDEX-MATCH, SUMIFS, pivot tables, data validation, conditional formatting) for data manipulation and automation, reducing processing time by 20%.

Conducted data wrangling using Python and Pandas, preprocessing raw data to prepare it for analysis, improving data quality and usability by 25%.

Implemented predictive analysis models using machine learning algorithms to forecast customer churn rates, achieving a prediction accuracy of 85%.

Applied statistical modelling techniques, including hypothesis testing and ANOVA, to analyze marketing campaign performance and optimize strategies for a 20% increase in conversion rates. EDUCATION

MS in Information System, Saint Louis University USA GPA: 3.91/4.00

Aug 2022 – May 2024

Bachelors in Information System, JNTUH College of Engineering India Aug 2016 – Jun 2020

PROJECTS

Applied Analytics

Utilized advanced statistical analysis techniques including hypothesis testing and regression analysis to uncover trends and patterns in customer data, leading to actionable insights that improved customer retention strategies by 15%. Profanity Detection in Social Media

Led a machine learning project using Python to develop a classification model that accurately detects profanity in social media posts with a precision of 90%, enhancing content moderation processes. CERTIFICATES

AWS Certified Cloud Practitioner

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