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Pranshu Kumar Data Analyst

adb3tv@r.postjobfree.com 617-***-****

linkedin.com/in/pranshu-kumar pranshu1921

**, ************ ******, ******, ** 02215

EDUCATION

Northeastern University, Boston, MA Fall 2019 - Current Master of Professional Studies Analytics

Related courses: Probability Theory and Introductory Statistics, Intermediate Analytics University of Petroleum and Energy Studies, India July 2015 - July 2019 Bachelor of Technology Computer Science 2019

Related courses: Artificial Intelligence, Advanced Database Management Systems MACHINE LEARNING: Python, SQL, R, Pandas, Numpy, Scikit-Learn, Seaborn, Matpolotlib, Microsoft Excel DEEP LEARNING: PyTorch, Keras, TensorFlow

SKILLS

PROJECTS

Risk and Returns: The Sharpe Ratio Dec. 2019 - Jan. 2020

- calculated and compare profitability and risk of different investments using Sharpe ratio using Pandas library in Python

- visualized and compared daily stock prices for Amazon and Facebook

- calculated relative performance of stocks and compared with current benchmarks

- concluded for Amazon to have double the higher Sharpe ratio than Facebook

- found Amazon to return double the returns for each unit of risk by investors The Android App Market on Google Play Nov. 2019 - Dec. 2019

- Performed comprehensive analysis of Google Play Store datasets for devising growth and retention strategies using Python

- Visualized data using Matplotlib and found 33 unique categories with ‘Family and Game’ having the highest market prevalence

- Found the average volume of ratings across all app categories as 4.17

- Found the size range of 2MB to 20MB for the majority of top apps under $10

- Performed Sentiment Analysis and plotted sentiment polarity scores of user reviews for paid and free apps Analyzing International Debt Statistics Oct. 2019 - Nov. 2019

- executed SQL queries for problems about international debt using data from The World Bank dataset

- used aggregate functions in SQL to find the global debt of 3079734.49

- grouped the data to find China owns its highest debt

- found the average debt for each country using grouping functions with aggregate functions

- found out the debt indicators in which each country owed its highest debt Generating Keywords for Google Ads Sept. 2019 - Oct. 2019

- Automatically generated keywords for a search engine marketing campaign using Python

- used exact match and phrase match to allow ads to be triggered for Google ads

- explored modified broad match, broad match and negative match types for better visibility and control of ad campaigns

- saved keywords data to CSV file for easy access to import to AdWords editor or BingAds editor CERTIFICATES

IBM Data Science Professional Certificate, Coursera Jan. 2020

- Developed and honed hands-on skills in Data Science and Machine Learning, starting with an orientation of Data Science and its Methodology.

- Became familiar and used a variety of data science tools, learned

- performed Data Visualization and Analysis and created Machine Learning models using Python and SQL

- found the safest Borough in London as part of Capstone Project IBM Scalable Machine Learning on Big Data using Apache Spark, Coursera Jan. 2020

- gained a practical understanding of Apache Spark, applied it to solve machine learning problems

- understood writing parallel code capable of running on thousands of CPUs

- used large scale clusters to apply machine learning algorithms on Petabytes of data using Apache SparkML Pipelines

- tested thousands of different ML models in parallel to find the best performing one

- used Apache SparkSQL and the Apache Spark DataFrame API, ran SQL statements on very large data sets Data Analyst with Python Career Track, Datacamp.com Feb. 2020

- Created visualizations using Matplotlib

- Mastered querying tables, combining tables in MySQL, SQL Server, Postgre SQL

- Learned to import data in Python from Excel, SQL, SAS, various APIs, and web.

- Explored Open Policing Project dataset to analyze the impact of gender on police behavior

- Performed parameter estimation and hypothesis testing for statistical inference

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