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IT professional & Data Engineer with Eli Lilly.Hands-on experience in AWS,Azure Synapse,Python,Pyspark,SQL,IBM DataStage Infosphere, and ETL.
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Eli Lilly and Company
Data Scientist
June 16, 2026 – Present
RedWine_Dataset
December 20, 2020 – December 20, 2020
The case study explores which red wines have properties that make them more alcoholic by implementing linear regression in Python.
View ProjectIntegrating_Apps_casestudy
November 12, 2020 – November 12, 2020
As a data scientist and programmer integrating an app store into a custom operating system you're designing. You can choose to integrate the Apple Store or Google Play, but not both. You want to use the platform that sells the best quality apps. You decide to use the app ratings as a quality metric and gather some data about rating from the two stores. In this case study, you'll analyze whether there is a significant difference between the ratings on these two platforms that would justify choosing one over the other. If there's not, you can always just flip a coin to pick which platform to use at random.
View ProjectFrequentist-Inference-A-and-B
October 17, 2020 – October 17, 2020
In part A, learned to apply the Pythonic implementation of the concepts underlying frequentist inference. In Part B, applied those implementations to a real-world scenario.
View ProjectAPI_PROJECT_QUANDL
September 5, 2020 – September 9, 2020
Qaundl has a large number of data sources, but, unfortunately, most of them require a Premium subscription. Still, there are also a good number of free datasets.
View ProjectSQL_COUNTRYCLUB_CASE
August 20, 2020 – August 20, 2020
A case study, where in used MySQL, PHPMyAdmin, Juptyer Notebook, and SQLite to tackle a series of challenges on a database containing information about a country club
View ProjectCAPSTONE-II-PROJECT-IBM-WATSON-TELECOM-CHURN
July 29, 2020 – September 15, 2020
Context: "Predict behaviour to retain customers. You can analyse all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets] Content: Each row represents a customer, each column contains customer’s attributes described on the column Metadata. The data set includes information about: Customers who left within the last month – the column is called Churn Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information – how long they have been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers – gender, age range, and if they have partners and dependents Inspiration: To explore this type of models and learn more about the subject. Implement different machine learning models to be able to help understand customer features that help in retenti
View ProjectLondonBoroughCaseStudy
June 24, 2020 – June 24, 2020
Here’s the mystery we’re going to solve: which boroughs of London have seen the greatest increase in housing prices, on average, over the last two decades? A borough is just a fancy word for district. You may be familiar with the five boroughs of New York... well, there are 32 boroughs within Greater London. Some of them are more desirable areas to live in, and the data will reflect that with a greater rise in housing prices.
View ProjectCultural Fit Analysis
The candidate's projects are primarily academic or personal case studies, indicating a foundational interest in data science. However, the lack of diverse project types, open-source contributions, or team-based projects makes it difficult to assess cultural fit beyond a basic alignment with data science methodologies. The single listed professional experience is future-dated, providing no current or past professional context.
Soft Skills & Operational Fit
The provided data is insufficient to assess soft skills or operational fit. The candidate's project descriptions indicate an ability to work on self-directed tasks, but there is no information on collaboration, communication, or problem-solving in a team context.