MSBA Term Project: Built fraud detection models for bank credit card transactional data using R and further analyze
the accuracy of each model to predict credit card fraud in the transactions. Worked on classification models using
4 different algorithms (Decision Tree, boosting decision tree, Navies Bayes and Logistic Regression) to find out
the model that can provide highest precision and can detect maximum number of fraud customer's applications.
Human Resource Information Systems (HRIS): Performed descriptive analysis, factor analysis and regression
analysis on employee survey data provided by a health care provider. This helped with understanding the relation
and causation of HR practices on employee performance and empowerment.
Data Analytics: Application of key concepts and methodologies of statistics. Analyzing and solving data analytics
models using statistical methods. Application of data analytics tools and models to business decision making.
Tools: Statistical packages in Python.
Data Mining: Understanding of concepts data preparation and mining process. Developed and implemented data
mining models based on Association rules, Classification and clustering algorithms. Plotted visualizations for
decision making. Tools: R, R-Studio and other R packages. WEKA.
Project 1 : Association mining: Market basket analysis using association mining rules on grocery data provide by a Grocery
store to analyze the transactional database and identify interesting patterns from the database.
Project 2 : Cluster Analysis : Performed cluster Analysis on
McDonald's breakfast menu data set that provides a nutrition information to understand the least number of items
to order which meet daily nutritional requirements.
Shiny App development - Built Apps, HeatMap Agnes as clustering algorithm and Heatmap hclust as clustering
algorithm. User can change color of high heat as well as low heat clusters. User can also change the angle of the
title as per the requirement.
Big Data: Knowledge of technical components of big data technologies such as distributed computing,
applications and technologies for effective data processing and analysis. Understanding of data manipulation,
storage and analysis of big data in a project setting. Tools: Hadoop, Amazon MapReduce. Working knowledge
of AWS. Apache Hadoop, Pig, Hive, Amazon EMR
SAS Programming: Importing and manipulating statistical data in SAS, formulating solutions to problems,
producing relevant computer output using SAS, and interpreting the results appropriately, understanding of the
terminology of SAS programming. Tools: SAS 9.4, SAS University Edition.
Prescriptive analytics, decision-making using problem solving methods like optimization, queuing models and
simulations. Tools: Excel: Solver graphics, pivot tables, V-lookups.
Applied Project Management - Development of project definition, time and resource scheduling, budgeting, risk
management, and performance measurement to manage projects.
Term Project: Optimization of Backup Data
Recovery Process for a large SaaS provider, helping IT to implement more robust and comprehensive data backup
plan in the event of data loss. Worked in a team of four members to articulate project goals, scope, quality control,
risk and cost management. Communicated and gathered requirements from key stakeholders, prepared project
charter, detailed work breakdown structures (WBS) and firmly established shared accountability for achieving
project milestones (MS project 2010, MS Visio, MS Outlook, MS Office).
Marketing Analysis through combining conceptual marketing knowledge and analytical tools to solve marketing
problems. Analyzation and interpretation of large datasets and survey using mathematical models and software.
Tools: Ms. Excel.
Enterprise Management - Generated material requirement planning (MRP), advance supply chain planning
(ASCP) with resource constraint & master demand schedule (MDS) reports using Oracle E-Business Suite R12.
Created the roughcut capacity plan (RCCP) and the capacity requirements plan (CRP) reports based on the given
MRP and MDS data. Developed comparative production plan and aggregate planning reports for a chip-
manufacturing firm based on forecasted demand data (Excel). Tools: Excel for Data manipulation, macros, and
database management. Aggregate Planning Report on Forecasted Demand Data
Working knowledge of RDBMS. Database concept, database models and database management systems (SQL
query, Oracle SQL*Plus, MS Access).
Data warehouse concepts, design and management. Experience in building dimensional models and modelling
process. Implemented several dimensional models and OLAP cubes. Developed visualizations of multi-
dimensional data. Tools: Microsoft SSDT (SQL server data tool), SQL Server Management Studio and Visual
studio.
