Analytical and detail-driven developer with a strong focus on business intelligence, data visualization, and cross-platform dashboard solutions. Proficient in building intuitive, performance-optimized dashboards using
Python frameworks such as PyQt5, Plotly Dashand host VIA Streamlit, Render, Netlify.
Skilled in developing smart applications for sectors including automotive, education, finance, induatrial Experienced with data engineering for cloud-hosted platforms like Snowflake and AWS
Gained working knowledge around core Azure Data Concepts including IaaS, PaaS, SaaS. Deploying cloud data solutions such as Azure Cosmos DB, Azure SQL Server and various other Relational, Non Relational and Graph Databases(Gremlin) Gained knowledge around Document Databases (JSON). Touched upon topics including Azure Synapse Analytics, Azure Data Bricks, Data Factory and Azure Data Lake.
Completed a Sequence of 8 Courses consisting of Stages of Data Analysis, Data Sourcing, Data Cleansing, Data Transformation, Data Analysis and Finally Visualization Differentiated between a capstone, Case Study, and a Portfolio Identified the key features and attributes of a completed case study Applied the practices and procedures associated with the data analysis process to a given set of data Discussed the use of case studies/portfolios when communicating with recruiters and potential employers
Describe the major methods of customer data collection used by companies and understand how this data can inform business decisions Describe the main tools used to predict customer behavior and identify the appropriate uses for each tool Communicate key ideas about customer analytics and how the field informs business decisions Communicate the history of customer analytics and latest best practices at top firms
Demonstrated my ability to think strategically, analyze the competitive environment, and recommend firm positioning and value creation. Through this course developed at the Darden School of Business at the University of Virginia, I did explore the underlying theory and frameworks that provide the foundations of a successful business strategy and provide the tools needed to understand that strategy such as SWOT, Competitor, Environmental, Five Forces, and Capabilities Analyses and Strategy Maps
In this course I was introduced to the main tools and ideas in the data scientist's toolbox. The course give me an overview of the data, questions, and tools that data analysts and data scientists work with. There were two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second was a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
Completed Snow Flake Data Warehousing Workshop and gained hands on skills for creation of Databases, Creation of Tables and Schemas, Loading data into Tables, API connections to AWS Buckets, RBAC involved in Snow Flake, SnowSql Queries for Data Manipulation & Data Definition
I gained an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. I was guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. I learnt how one can perform predictive modeling and leverage graph analytics to model problems. I was able to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets
This course provided a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics included (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks) (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning) (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI) The course also drew from numerous case studies and applications, so I learnt how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
I have worked in data analytics projects which involved data extraction, cleansing, wrangling, analysis and visualization
A Quarterly Analysis carried out for McKinsey on their employee healthcare data. I did it during my tenure with IBM Watson Health. This is a very exhaustive and thourough analysis which yeilds various financial insights such as YoY%, QoQ%, MoM%, Forecast, KPI .,
This analysis provides a very granular level of insights of employee claims data carried out by extracting data from Cognos repository and applying various Cognos and Spreadsheet (Excel) functions to derive results and curate a detailed presentation as the final deliverable
It was a document managemet project based on Veeva platform carried out during my tenure with AstraZeneca Here I carried out Supplier data extraction, cleanse, and Analysis by applying various code book Filters in order to derive the final deliverable which is a Excel Flatfile
Supplier Data was extracted from the SAP Data redundancy was eliminated and clustered using OpenRefine Then, Data Code Book was used for applying filters and curating the information needed
Cyclistic, a bike-share company in Chicago. For maximizing the number of annual memberships, the team wants to understand how casual riders and annual members use Cyclistic bikes Differ. This was carried out using public data set and results yeilded help make strategic business decisions using Quarterly generated data files
The four data files in CSV format were loaded into "R" IDE (R Studio) and then binded into a single Data Frame Data Manipulation was carried out to eleminate any differences in data parameters A Summary of the analysis was created and finally Visualization of the Findings was created To answer the question “In what ways do members and casual riders use Divvy bikes differently?”
Health Company data also which is at any given point of time, has around 4000 employees. However, every year, around 15% of its employees leave the company and need to be replaced with the talent pool available in the job market.
the management wants to understand what factors they should focus on, in order to curb attrition and how the employement numbers will impact them
The analysis was carried out by loading the Excel Flat file into Tableau and creating joins of different tables The Dashboard consists of four charts each gauging the employee retention factors consolidated togeather
Amazon customer reviews sample data was collected and cleansed Post which it was uploaded onto Google Colab and sentiment of each review was classified by the Model powered by LLM Pipeline () for sentiment analysis .
the reviews were classified as Positive or Negative based upon the sentiment by leveraging the llms trained on massive data
CHAT GPT for code generation and Google Colab for running the Model by loading the CSV flat file