Avinash Gangarde

I'm Avinash Gangarde, a seasoned Data Analyst with a passion for extracting actionable insights from complex datasets. Proficient in SQL, Python, Tableau, Power BI, Advanced MS Excel, and machine learning algorithms, I excel in using data-driven strategies to solve intricate business challenges. My enthusiasm for exploring new datasets, building predictive models, and employing evaluation metrics has driven informed decision-making across a variety of projects.

Wynisco | Data Analyst       Jan 2023 – Present

• Created clear and insightful visualizations using tools such as Tableau, Power BI, or matplotlib/seaborn in Python.
• Collaborated with software developers, data analysts, and other stakeholders to understand data requirements and provide database solutions.
• Collaborated with data engineers, business analysts, and other team members to ensure the seamless flow of data and information across the organization.
• Generated comprehensive reports using Tableau, presenting analytical findings to both technical and non-technical stakeholders.
• Created SQL views to simplify complex queries, facilitating streamlined data analysis and reporting for business stakeholders using SQL.
• Developed and maintained data models in Power BI, ensuring efficient relationships, hierarchies, and utilizing DAX (Data Analysis Expressions) to create calculated columns and measures for accurate data representation.
• Enhanced data retrieval using VLOOKUP and HLOOKUP functions, ensuring accuracy in reconciliation and consolidation tasks.
• Created Pivot Tables in Excel for summarizing and analyzing datasets, facilitating efficient data aggregation, filtering, and dynamic reporting.
• Created insightful visualizations using Power BI and Tableau to communicate data-driven insights effectively to both students and management.

Almabetter | Data Analyst Intern  Aug 2022 – Jan 2023

• Utilized SQL, including JOINs, window functions, and subqueries, to proficiently manage and manipulate databases, executing data extraction, transformation, and loading (ETL) tasks.
• Utilized Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn to apply machine learning algorithms, enhancing the development of predictive models across diverse projects. Conducted descriptive and statistical analysis, supported by SciPy, to ensure robust model performance and data-driven decision-making.
• Created dynamic visualizations utilizing Tableau, Power BI, and Python libraries, incorporating calculated fields, custom parameters, and functions to create engaging charts. Leveraged DAX expressions to formulate intricate calculations and employed filters and slicers for enhanced interactivity, ensuring effective communication of insights derived from intricate datasets.

BE-Engineering 2019 (Pune University) 
CGPA : 9