Paramdeep Singh is seeking a computation intensive data analysis role leveraging his experience in information technology and academic training in quantitative research methods. He has worked as a research assistant at Indiana University conducting data analysis, visualization, and supervising undergraduate assistants. His professional experience also includes work as a programmer at Infosys developing applications using Java and as a graduate teaching assistant delivering statistics courses. He has strong skills in statistical analysis using Stata and R, machine learning algorithms in Python, and software development in Java. His graduate projects include econometrics modeling, data mining with random forests, an economic impact analysis, and a spatial analysis of food deserts using ArcGIS.
Experienced Analyst with a demonstrated history of working with huge amounts of data. Skilled in R and python, SQL, Tableau, Microsoft Office, Leadership, Project Management. Strong research professional with a Masters in Statistics with specialization in Data Science from California State University - East Bay. Currently working part time as Data Analyst in the Office of Sustainability, Cal State East Bay.
Experienced Analyst with a demonstrated history of working with huge amounts of data. Skilled in R and python, SQL, Tableau, Microsoft Office, Leadership, Project Management. Strong research professional with a Masters in Statistics with specialization in Data Science from California State University - East Bay. Currently working part time as Data Analyst in the Office of Sustainability, Cal State East Bay.
Seeking a challenging position to utilize my quantitative and data interpretation skills complementing with my knowledge of Technology and Management to excel in areas of Analytics and Digital Marketing; which will nurture and bring forth the best I can offer to the organization, self & society
Seeking a challenging position to utilize my quantitative and data interpretation skills complementing with my knowledge of Technology and Management to excel in areas of Analytics and Digital Marketing; which will nurture and bring forth the best I can offer to the organization, self & society
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Paramdeep Singh
Bloomington, IN - (812) 349-8940 - Parasing@umail.iu.edu
Dedicated and motivated graduate student seeking a computation intensive data analysis role to
leverage my professional experience in information technology and academic learning of quantitative
research/analysis methodologies.
PROFESSIONAL EXPERIENCE
Research Assistant Bloomington, IN
Indiana University School of Public & Environmental Affairs Current Job
Automating data collection, processing and structuring procedures and contribute in
designing experimental studies.
Conducting exploratory data analysis for coming up with hypothesis and building models
using statistical and econometric methodologies in Stata;
Making Visualizations for reports and to address non-technical audience;
Supervising and coordinating with undergraduate research assistants.
Graduate Teaching Assistant Bloomington, IN
Indiana University School of Public & Environmental Affairs Aug 2016-May 2017
Delivered a range of teaching and assessment activities including conducting a lab
directed towards the delivery of two statistics courses at graduate level;
Participated in the evaluation process and provide effective timely and appropriate
feedback to students to support their learning;
Supervised practical work advising on skills methods and techniques to assist the
transfer of knowledge.
Research Assistant Bloomington, IN
Indiana University School of Public & Environmental Affairs May 2016-August 2016
Evaluated Crash Reports available via resources like NMVCCS, CDS, Indiana Transport
Department for recommending better on-vehicle road safety equipment and
development of Road Departure Warning system;
Textual and Graphical data mining from XML, HTML and PDF to collect data about road
edges and road departures using Python and its packages;
Analyzed road coordinates to design a generic methodology for calculating the curvature
of roads around US to come up with distribution of same using SAS;
Developed a GUI interface which facilitated the leveraging of human intelligence for
analysis of crash cases and storing the information in a database.
Programmer Bangalore, India
Infosys Ltd June 2011-May 2015
Implemented intricate requirements from various users, created optimum design
solutions and developed/maintained user interface of the web application;
Programmed applications using Core Java, JavaScript, jQuery, PL-SQL;
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Performed optimization reviews for better performance in high data volume
environments;
Project Management and training colleagues to support and maintain the application.
CRITICAL SKILLS
Data modeling, multivariate statistical analysis and data management using Stata/R
.Linear and Non-Linear Regression Models, Experimental and Quasi-Experimental
methods (Propensity Score Matching, Regression Discontinuity, Fixed Effects, Instrument
Variables), Simulation Methods, ARIMA and ARIMAX models.
Creating and using SAS MACROS. Writing and interpreting SAS SQL code.
Designing data-mining and machine-learning algorithms like Decision trees, Random
Forests, K-Means Clustering, Nearest Neighbors and its application on data using Python
Knowledge of vector based GIS techniques using ArcGIS.
Software development and database management using Core Java, JavaScript, jQuery, PL-
SQL with planning, designing, implementation, testing and maintaining applications.
EDUCATION
Indiana University, School of Public & Environmental Affairs Bloomington, IN
Master of Public Affairs, Policy Analysis Expected Graduation: July 2017
Indiana University, Informatics and Computing Bloomington, IN
Graduate Certificate in Data Science Aug 2016- May 2017
Punjabi University Patiala, India
Bachelor of Technology September 2006-May 2010
KEY GRAD PROJECT WORK AND PUBLICATIONS
Econometrics: Lead a team of 4, to build an Econometrics model testing Gauss Markov Linear
Assumptions to analyze the factors decoupling a country's GDP from carbon emissions.
Machine Learning/Data Mining: Implemented different algorithms like K-Means and Random
Forests using Python, on a school-based survey dataset from The National Longitudinal Study
of Adolescent Health, to study the factors that may influence adolescents’ health and risk
behaviors in United States.
Economic Analysis of Indiana Geological Survey (IGS): Survey designing using Qualtrics and
Economic Impact analysis of the services provided by IGS using quantitative methods like
Input –output model.(Took an IRB certification for this project).
Cost Benefit Analysis: Performed cost benefit analysis of one of India’s flagship government
ventures – AADHAR (meaning support), which primarily aimed at uniquely identifying Indian
residents and improved execution of government’s social benefit schemes by linking them to
the unique IDs issued under this scheme.
ArcGIS: Conductedspatial analysis to examine the potential effects ofgrocerystore proximity,
transportation access, and unemployment rates to determine whether or not food deserts
exist in the suburban area surrounding downtown Boston, Massachusetts.