Python Data

Mayurkumar Amrutiya

MS in Data Analytics, UCF * 772-***-**** * ****************@*****.***

PROFILE

Data Scientist - detail-oriented, quick learner and team player with emphasis on accuracy, precision and efficiency. Firm understanding of data mining, machine learning and quantitative analysis. 2+ years experienced professional in data modeling, ad hoc SQL, data wrangling and reporting. Subject area knowledge and experience in finance and construction. CORE COMPETENCIES

Supervised & Unsupervised Learning

Network Science

Machine Learning

Python

Statistical Analysis

SQL

Data Wrangling (Python, VBA)

Reporting (Financial)

R

PROFESSIONAL EXPERIENCE

STRATICON CONSTRUCTION, Stuart, FL, USA

Software Consultant – Analytics and Reporting Aug 2018 – Present Noteworthy achievements:

- Played leading role in company's all activity regarding Reporting, Analytics and BI.

- Saved money (60k $) and time (80%) by bringing in house custom rad hoc reporting ability instead of going through third party.

- Saved time of accounting manager by creating VBA template to automate process.

• Created JasperReports using advance SQL and IReport studio for various corporate departments including Account Payable, Accounts Receivable, Auditing (bank inspections), Project Management and Budgeting.

• Created Excel sheet that helped the sales department to distribute revenue budget normally over period of project for their sales revenue projection for the year.

• Created VBA templates to automate standard data processing tasks for account receivable and account payable.

• Helped management track project earned value with plot, projections of cost and revenue based on construction schedule vs actual billed revenue.

• Created dashboards with key analytics to assist department managers in managing their respective departments across numerous projects.

SOUTHERN STATES TOYOTALIFT, Orlando, FL, USA

Consulting Analyst – Capstone project Jan 2020 – Present (expected April 2020) Noteworthy achievements: achieved 91% accuracy for model which is 11% increased from previous model.

• With the goal of creating better preventive and remedial maintenance processes, used supervised natural language processing (NLP) on work order descriptions to categorize and classify the root cause of forklift faults.

• Helped management to identify the types of work orders that generate maximum revenue for given customer.

• Created a labeled word database using the Word2Vec algorithm in order to transform words into n-dimension vectors to identify semantically similar groups of words.

• Assigned weights to group of words using TF-IDF and implemented SVM and Naive Bayes algorithms for multi- class classification of work order description.

KHODIYAR AUTO, Surat, GJ, India

Data Wrangler Jun 2017 - Dec 2017

Noteworthy Achievements: Improved productivity of mangers by providing structured dataset and charts

• Transformed raw data into structured data using Python (Pandas, NumPy) and Excel (Lookup functions, pivot tables, and VBA).

• Created presentation charts using Python (Matplotlib and Seaborn). EDUCATION

UNIVERSITY OF CENTERAL FLORIDA, Orlando, FL Aug 2018 – Present (April, 2020) Master’s in Data Analytics (GPA 3.72)

GOVERNMENT ENGINEERING COLLEGE, INDIA

Bachelor of Engineering in Electronics and Communication (CGPA 8.79/10) Jun 2013 - Jun 2017

• Assisted a PhD student in the publication of a research paper and presented it at a national conference

(Emerging Research Trends in Engineering-2016)

DUKE UNIVERSITY (COURSERA) Mar 2018 - May 2018

Statistics with R (Probability, Inferential Statistics, Linear Regression and Modeling, Bayesian Statistics) through Duke University on Coursera.

CLASS PROJECTS

CNN implementation on MNIST dataset using Python (Keras) Worked on the implementation and fine tuning of Convolution Neural Network (CNN) in Python using Keras to implement the CNN on MNIST dataset for image reorganization. Unsupervised learning of a Portuguese banking institution’s marketing campaigns Implemented an unsupervised learning algorithm (K-means, hierarchical clustering) employing Python. Used Sci-Kit learn, Pandas, Numpy and matplotlib library.

Used elbow method to select number of clusters for hierarchical clustering. Supervised learning on Rain in Australia data

Applied and compared supervised learning algorithms (logistic regression, KNN, SVM) for classification and comparison of results using Python. Used Sci-Kit learn, Pandas, Numpy and Matplotlib libraries. Time series data preparation

Wrote a technical report on data preparation of time series data.

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