Data Science

After nearly 20 years of building engaging consumer relationships, it is clear that implicit data analytics and machine learning are the path forward in modern marketing. That truth is in the numbers and those numbers are in patterns unseeable by humans alone. I have chosen to start a journey to master the tools of data science. I hope to bring creativity and passion for building brands to communicate insightful predictions and an understanding of our world

I have been fortunate to be able to work in analysis and discovery in social media marketing. A great new frontier to create brand awareness and credibility. I have built projects to assist Fohr, an influencer marketing platform. These projects forecast consumer reach on Instagram and to recommend, with machine learning, spokespersons for your brand based on tone and style.

Flatiron School

January - May 2019

A competitive immersive Bootcamp style course. Completed this intensive 15-week in-person program on the fundamentals of Data Science. The course required collaborative project work for 4 distinct modules of learning and a 5th capstone project completed individually.

• Data Exploration and Analysis

  • Data gathering and cleaning, analysis using probability and summary statistics, and data visualization. Fundamental concepts in programming using Python and SQL, along with functions and object orientation, writing,  scraping, and regular expressions.

• Probability and Statistics for Data Science

  • Bayesian and Frequentist statistics, regression analysis, linear and logistic regression. Building regression models — including linear regression with gradient descent from scratch.

• Machine Learning and Big Data

  • Supervised learning, non-parametric algorithms like k-nearest-neighbors and support vector machines.  Introduction to threading and multiprocessing to work with big data — Apache Spark and Apache Spark on AWS.

    Decision tree learning and how it is applied to classification and regression tree analysis, and time series analysis using Pandas.

• Machine Learning and Deep Learning

  • Unsupervised learning. Building recommender algorithms using collaborative filtering, matrix decomposition, clustering, and deep learning approaches

UDACITY NanoDegree - Data Engineering

May 2020

Completed 3 months of study on Database Architecture, Data Pipelines, AWS and Apache Spark.

• Data Modeling with Postgres and Apache Cassandra

• Cloud Data Warehouse with S3 and Redshift

• Data Lakes with Apache Spark

• Data Pipelines with Apache Airflow

• Capstone Project with machine learning data augmentation in Apache Spark

  • Social Media Agency Project

UDACITY NanoDegree - Data Science

July 2021

Completed 3 months of learning to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud.

• Data Science CRISP-DM Process

  • The data science process, including how to build effective data visualizations, and how to communicate with various stakeholders

• Software Engineering for Data Scientists

  • Develop software engineering skills for data scientists, such as creating unit tests and building classes.

  • Linear Algebra library package for PyPi

• Data Engineering for Data Scientists

  • The entire data science process, from running pipelines, transforming data, building models, and deploying solutions to the cloud.

  • Disaster Relief Pipeline Project

• Experimental Design and Recommendations

  • Design experiments and analyze A/B test results.

  • Explore approaches for building recommendation systems

  • Build recommendations for IBM site with FunkSVD

• Capstone Project in Customer Segmentation and Supervised Learning

  • Arvato Financial Project

Google Cloud Certified Professional Data Engineer

June 2021

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

The Professional Data Engineer exam assesses the ability to:

• Design data processing systems

• Build and operationalize data processing systems

• Operationalize machine learning models

• Ensure solution quality

UDACITY NanoDegree - Programing for Data Science

July - September 2018

Completed 3 months of study on Python, SQL and Version Control for data science applications.

• SQL language fundamentals such as building basic queries and advanced functions like Window Functions, Subqueries and Common Table Expressions

• Command line essentials with the bash shell

• Python programming fundamentals such as data types and structures, variables, loops, and functions

• Version control with Git

• Visualization techniques and fundamentals with Tableau

Neo4j GraphAcademy: Certificate of Completion

Completed certificate courses on Neo4j’s Graph database

• Introduction to Neo4j and Cypher query language

• Data Science with Neo4j

• Applied Graph Algorithms