Executed complex SQL queries, conducted extensive data gathering and integration for large-scale analytics projects, handled large volumes of data from multiple sources, resulting in a 20% increase in data accessibility and quality.
Designed automated dashboards and reports on customer acquisition and revenue generated working cross-functionally, using Power BI increasing efficiency and saving about 20 business hours of report creation.
Developed MS Excel VBA Macros to automate fetching of new data, data aggregation and generating reports and dashboards from relational database - Amazon Redshift that reduced manual labor by 10 hours a week.
Utilized Python libraries (NumPy, Pandas, scikit-learn, SciPy) and employed advanced statistical methods to process and analyze US Census data 8M population records.
Performed data cleaning, data manipulation and data validation using advanced SQL queries, improved the data accuracy and reduced query run-time by more than 20%.
Developed customized dashboards using Power BI and Excel (PivotTables, VLookups) to illustrate real-time analytics, resulting in a 20% improvement in data-driven decision-making.
Architect and developed scalable ETL/ELT data pipelines for client services with complex data model transformations to address analytics needs using tools like SSIS, Apache Airflow, AWS Glue, and Google Cloud Dataflow from wide variety of data sources.
Leveraged Python, Conducted extensive data cleaning by utilizing data manipulation to remove inconsistencies and transform unstructured data, processed over 5 million rows of data.
Applied advanced statistical techniques such as regression analysis, A/B Testing and cluster analysis to identify key patterns and trends in client data, leading to a 30% improvement in data visualization and a 20% increase in analytics accuracy.
Extracted, interpreted, and studied transaction level data using tools like Power BI, Excel, SQL and Python from large set of data to identify key metrics and transform raw data into meaningful, actionable insights.
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In this project unsupervised and supervised learning, models were used to analyze the demographics of the general population in Germany against demographics data of customers of a German mail-order company
Stack Overflow released the results of their 2019 survey of more than 90,000 developers. So from about 90000 respondents on StackOverflow, there are about 2.5% Data Scientists or Machine learning specialists.
I will try to explain How coronavirus works in our body (just a high-level overview), What is the need of this classifier in today’s world, and at last, we will cover Some deep learning concept and code to build this classifier