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Leon Adams

Leon Adams

Certified

Data Scientist

The George Washington University

Technologies

My Portfolio Highlights

My New Track

Data Scientist

My New Course

Introduction to Python

Insights architect, constructing bridges between data and actionable strategies.

My Work

Take a look at my latest work.

course

Introduction to R

course

Introduction to Python

course

Intermediate Python

My Certifications

These are the industry credentials that I’ve earned.

Deloitte Experienced Professionals (XP) Program Capstone

Deloitte Experienced Professionals (XP) Program Capstone

Other Certificates

Udemy Databricks Fundamentals & Apache Spark Core

CITI Program Data or specimens only

FourthBrain Machine Learning Engineer Certificate

Coursera Executive Data Science

Coursera TensorFlow in Practice Specialization

DataRobot DataRobot Essentials

Coursera Google Cloud Platform Big Data and Machine Learning Fundamentals

Coursera Mathematics for Machine Learning Specialization

DataCamp Data Scientist with R Track

Coursera Machine Learning Specialization

IBM Big Data Foundations

Coursera Hadoop Platform and Application Framework

Coursera Machine Learning: Clustering & Retrieval

Coursera Machine Learning: Classification

Coursera Machine Learning: Regression

Coursera Machine Learning Foundations: A Case Study Approach

lagunita.stanford.edu Statistical Learning

DataCamp Course Completion

Take a look at all the courses I’ve completed on DataCamp.

My Work Experience

Where I've interned and worked during my career.

Deloitte | Sep 2021 - Present

Lead Data Scientist

N/A

Redjack | Mar 2019 - Sep 2021

Data Scientist

● Created distributable Python package for the purpose of generating threat intelligence data feeds. ● Leveraged Cluster analysis to facilitate the completion of exploratory analysis looking for structure in unlabeled threat intelligence data.
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Deloitte Consulting | Nov 2017 - Mar 2019

Senior Consultant

● Used a variety of Gaussian and Gaussian Mixture models to address business needs of a large federal client. Delivered solutions to difficult problems of imputation, anomaly detection and estimation. ● Worked extensively with Postgres and SQL programming to produce complex permanent tables and stored procedures in support of developing new models of identity theft. ● Contributed to the development of an internal course on optimization focused on a variety of potential client business cases and delivered via Juypter Notebooks. ● Contributed to the development of project workflow management, leveraging GitLab for project documentation, code repository, and version control.

The George Washington University | Aug 2015 - Nov 2017

Professorial lecturer

● Contributed to improved curriculum development through collaboration and discussion with lead professor at GWU school of engineering and applied science. ● Provided lectures on multivariate data analytical methods including: regression, principal components for dimension reduction, and analysis of variance. ● Deployed Jupyter Notebook web application to aid the communication of abstract probability theory whilst teaching data analysis course.

Personal | Jan 2015 - Present

Statistical Machine Learning

● Latent Dirichlet allocation (LDA) for document retrieval task. ● Approximate nearest neighbor using KD-trees and locality sensitive hashing (LSH). ● Probabilistic mixture models for document clustering. ● Linear regression models for predictive analytics and feature selection. ● Nearest neighbor and kernel regression for predictive and classification tasks. ● K-means for document clustering tasks. ● Logistic regression for classification tasks. ● Decision trees for multi-class classification tasks. ● Use of ensemble classifiers for boosting model performance. ● Validation set and cross validation for parameter tuning. ● Bias-variance trade off using L1 and L2 penalties. ● Gradient and Coordinate descent for parameter estimation. ● Stochastic gradient descent for batch and online parameter estimation.

Clearview Consulting | Feb 2013 - May 2015

Consultant

● Met with client’s senior leadership during project initiation to scope project requirements and establish business goals and objectives. ● Conducted interviews with staff, in conjunction with, document analysis to better understand existing data collecting infrastructure. ● Consolidated disjoint data sets from csv text files, excel files and mySQL databases by importing and merging using pandas. ● Used pandas and Matplotlib to conduct data quality assessment and exploratory data analysis in aid of assessing and communicating deficiencies in client’s data collection procedures. ● Utilized regression and classification techniques to provide insight into a variety of supervised learning business use cases; including logistic regression for sentiment analysis and; prediction of default rates using gradient boosted decision trees. ● Performed statistical analysis of data using Python programming, developed reports using Tableau, and utilized Python to build analytic tools for quantitative modeling. ● Leveraged python beautifulsoup4 module to collect and organize structured and unstructured web data used in developing predictive models. ● Used locality sensitive hashing for unsupervised approximate nearest neighbor document retrieval. ● Explored high dimensional text datasets using a variety of machine learning classification techniques, including k-means and diagonal gaussian mixture models. ● Leveraged topic modeling for the calculation of mixed membership in document corpora. Used latent dirichlet allocation (LDA) to learn and identify topics present within corpus.

My Education

Take a look at my formal education

Doctor of Science, Engineering ManagementThe George Washington University | 2012
Master's degree, Mechanical EngineeringUniversity of Maryland College Park | 2005
Bachelor's degree, Mechanical EngineeringSan Jose State University | 2000

About Me

Leon Adams

Leon's multidisciplinary engineering and analytics experience helps him deliver innovative solutions to complex institutional and business opportunities. Through the use of applied statistical data analysis, machine learning and mathematical modeling

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