I am a creative and talented individual, I have a keen interest in data analytics and visualization. to work with huge amounts of data and be able to find patterns and hidden trends inside it is something I really like, and have envisioned a future in that field, Providing relevant insights so as to make and support better business decisions is something I would like to start my career in. I am of the belief that a picture says a thousand words. I have mastered Tableau and Power BI, as they help communicate the results and findings in a more effective and easier way that is easily understood by people.
Before starting over at NYU I studied at SRM University with a major in Information Technology. There I enhanced my coding abilities and gained a brief understanding of databases and its architecture. I am also proficient in writing code in different languages such as Python, R, C, C++, Java, PHP. SQL. After completing 4 years of coursework and various projects using those languages, I became proficient in writing and debugging codes. I have attached my transcript where you can see all the various coursework and programming languages that I learned and have hands-on experience with.
During my bachelors I did a lot of projects too, I built a mobile app for one of our departmental fests in college using JAVA and SQL which helped with the registration of users and handled their data and let them register for various passes and tickets on the app.
I also interned at Tata Steel for 3 months where I built a database architectural structure for the smooth transitioning of data from the business side to the client side. It reduced the efforts that the customers spent before on finding the right place where they could get their products manufactured. The project led to an increase in sales of Tata Steel by 7% in just 3 months after finishing it.
After my bachelors I came to NYU to continue my studies, I wanted to learn more about implementing the knowledge that I learned previously in the business development side and wanted to be able to relate the ever-changing world of technology and its impact on business. I completed coursework like business analytics, data visualization, supply chain, economics and various other. During my time at NYU I learned how to model databases, data prediction, building models to predict the outcome or result.
Currently I work as a Graduate Employee at NYU Assessment and Institutional Research where I handle day to day data queries from all the departments, I handle all the student and university data and create reports for outside institutions. During my role there I also applied my knowledge of various data collection, data mining, data cleaning, data analytics and data visualization tools and techniques. I started the implementation of tableau and Power Bi to make the reports dynamic and interactive so that students and professors could read and interpret reports easily. Having worked with databases of my whole University I have significant understanding of handling large sets of data and applying queries on them to get results.
I also did a lot of projects over my courses here at NYU, I did a project on Silvercar a car rental company, I analyzed their customer data which consisted of more than a million records. Using R and tableau I narrowed the search for potential customers they should target, thus resulting in an increase in churn rate. Further, I also did a project in business analytics based on credit card fraud detection, where I applied various machine learning algorithms and performed statistical analysis and built fraud detection models using random forest, k-means, and svm models.
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Experience
NYU Assessment and Institutional Research
• Modi�ed and developed the Assessment and Institutional Research
website.
• Extracted, manipulated and performed di�erent queries using SQL on university database to regularly update and modify them.
• Started the implementation of data Visualization tools such as Tableau instead of Excel, to make reports. The results were interactive and dy- namic dashboards that conveyed way more information than before.
• Created actionable insights, leading to better decision making across the University, by using various data mining, data cleaning, data analytics and data visualization tools and techniques.
• Managing projects, identifying and resolving issues cross-functionally between business users and technology.
Tata Steel
• Developed database architectural strategies, and analytical models at the modelling, design and implementation stages to address business or industry requirements.
• Designed database applications, such as interfaces, data transfer mech- anisms, global temporary tables, data partitions, and function-based in- dexes using SQL & C++ to enable e�cient access of the generic database structure and easier table entries, reducing the number of tasks being outsourced. Yielding 20% saving on an average annual expenditure of 170 million.
• Formulated business metrics and development requirements for report- ing purposes.
• Spearheaded the development of the company’s online resource page for their buyers in order to give them more ease of access, subsequently resulting in the increase of sales by 7% over the next 3 months.
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Projects
AMAZON PRODUCT RECOMMENDATION SYSTEM
• We built a recommendation system for Amazon, that gives it users rec- ommendations for products that they might like based on matching user comments. If two users A & B both like product 1 and they leave pos- itive comment on that product, then if person B buys product 2 then person A will get a recommendation for product 2 since they both left a similar comment for product 1. To achieve this we used various machine learning algorithms, �first we extracted and cleaned the data, then we performed an exploratory data analysis following which we performed text mining and sentiment analysis on the user comments and finally we paired them using K-means clustering. So every person will get a recommendation of a product that its nearest neighbor bought.
CREDIT CARD FRAUD DETECTION
• Used various machine learning algorithms such as Random Forest, XG- Boost, and SVM to detect and predict credit card fraud. We used these algorithms and various statistical analysis models. The data set contained more than 50000 transactions and 30 feature. After cleaning the data, we removed the outliers by focusing on features with correlation 0.5 or higher. Finally we used K-fold cross validation to avoid overfitting. In the end we created box plots to look at the visualization of those features. The accuracy of the models were 96%, 97%, & 91% respectively.
SILVERCAR CUSTOMER ANALYSIS
• SilverCar is a car rental company, I performed an exploratory data analysis of its customers using python and SQL. I created a data pipeline for easy transmission of data between platforms, the platform helped in reducing customer churn rate by 10%. Further I developed an application in Python to analyze booking-revenue lift from marketing campaigns targeted at a 5 million customer segment. Insights generated helped increase ROI by 40.
UNIVERSITY EMAIL ENGAGEMENT RATE ANALYSIS
• Analyzed two di�erent data sets of about a million observations and derived business insights using R which helped the client to make the Email Engagement process more �efficient and cost effective. The insights improved the conversion rate of prospective students to applicants from 28% to 37%.
UDGATTI
• Developed a cross platform react-native app. The target users for the app were college students. The main feature was that it allowed anyone in dire need item(Ex: Food, Laptop, etc) to put up a request where he/she will agree to pay an X amount. Then in a radius of 1 mile everyone will get a notification that a person wants to buy this item at the speci�fed rate, any user who has the item then could contact the buyer directly through the app’s chat feature.