Danielle Ronkos is an experienced problem-solver who has been wrangling language data since 2013. She received her PhD in Linguistics from The Graduate Center, CUNY, where her research focused on phonetics and phonology and took her on field trips to Nepal to collect speech data. Since graduating in 2020, she has worked on two language-focused products -- first as a curriculum developer at the National Foreign Language Center, and later as a linguist at an HR start-up developing a text editor for job descriptions. It was in this second position that she realized she was fascinated by the work the data science team was doing, and set about getting the technical skills she would need to become a data scientist in her own right. She's excited to combine these technical skills with her years of experience as a researcher and communicator to find new data-driven solutions in her next position.
Ronkos, Data Science
The PhD Candidate: Using NLP and Topic Modeling to Look at PhD Hiring Trends
Clustering / Natural Language Processing / Classification
This project combines techniques in NLP, clustering, and classification to analyze a corpus of job description documents scraped from Tapwage.com and identify the top skills employers are looking for when hiring candidates with PhDs.
David (Dayv) Doberne grew up a baseball fan in the San Francisco Bay Area, and experienced the Moneyball A's revolution firsthand during his formative years. This deeply ingrained in him an appreciation for looking past conventional wisdom and toward solutions rooted in statistics and observable trends. Dayv graduated with degrees in Biology and Music Performance from the Oberlin College & Conservatory, and is now equipped with tools sharpened by the Data Science & Machine Learning program at Metis to sculpt solutions out of problems big and small. Outside of his professional life, Dayv makes a point to watch Moneyball once a year, and also enjoys rock climbing and strategy games.
Doberne, Data Science & Machine Learning
The Filthiest (Baseball Pitching)
Big Data/Data Engineering / Classification / Cloud Computing (AWS/Google Cloud)
The Filthiest uses a Random Forest Classifier model to sift through web scraped data from MLB pitches and return the best highlights via a Streamlit app.
Erica Stevenson is an accomplished scientist who is fascinated by the complex data of human behavior and the natural world. She discovered her curiosity for data science while working with mass spectrometry data in R, which prompted her to pursue further studies with the project-based bootcamp, Metis. Erica has Masters degrees in Organic Chemistry and Chemical Engineering, as well as 10 years of experience working in scientific fields. Outside of work, she is a violinist, dancer, and consciousness researcher. She looks forward to continued learning and new challenges as she pivots her career.
Stevenson, Data Science
Music Information Retrieval - Genre Classification
Big Data/Data Engineering / Regression / Classification
This project used the Free Music Archive (FMA) dataset, Music Information Retrieval (MIR), and classification models to predict the genre of a song based on its high dimensional spectral features.
Marjan Rezvani graduated from the City University of New York in 2021 with a Master's Degree in Data Science and Engineering. She has experience in analyzing data, developing Machine Learning, and Statistical models to solve complex problems. She has a passion for using her skills to leverage data to aid companies with their business strategies, and explore exciting new fields developing from within data science, but on the side, she also enjoys panting. To get in touch with her, email her on [email protected]
Rezvani, Data Analytics
Predicting Housing Prices
Anomaly Detection / Big Data/Data Engineering / Regression
Collected information on properties from Century21 pertaining to several states of the America by leveraging BeautifulSoup to collect and parse data based on specific search queries. Analyzed 12 unique property features for 1800 properties.
Matt grew up in the sunny suburbs of Denver, Colorado, and feels incredibly fortunate to have had so much access the outdoors in his formative years. The natural world is an enormous piece of his identity: he likes to play and rest in it, and strives to protect it through his professional efforts. Matt combined his academic background in ecology and sustainability with his natural communication and storytelling talents to excel as a professional naturalist and biodiversity steward before enrolling with Metis. Newly empowered with a broad array of data science skills and tools, he hopes to leverage AI to work towards a zero-carbon, biocentric world. In his free time, he loves to tinker, craft, climb rocks, travel, read, and spend time in nature.
Edrich, Data Science & Machine Learning
Neural Networks / Classification / Natural Language Processing
Deep learning tools and NLP techniques to automatically caption images.
Melanie is a fast-learning data scientist with a strong economics and math background who is passionate about finding data-driven solutions to policy failures. She currently collaborates with economists at the Cornell Dyson School on regression analysis in applied economics. Previously, she worked on Capitol Hill as a Public Policy Data Analyst for the Joint Economic Committee Democratic Staff where she wrote a report urging Congressional investment in child care, which increased legislative support for the struggling sector, as well as a policy brief analyzing disparate internet access across the U.S. While she loved this work, she wanted to make a larger impact with her technical skills, which led her to Metis. As a former college athlete, she loves watching and playing basketball, hiking, and reading outside of work.
Ackerman, Data Science & Machine Learning
Grey's Anatomy Episode Recommender
Recommender Systems / Natural Language Processing / Tableau/Dashboards
This project uses web scraping, NLP, and topic modeling to produce an episode recommender app based on topic for Grey's Anatomy fans who want to rewatch episodes but don't know where to start.
Nate DiRenzo is an experienced professional with a demonstrated track record of success in customer-facing roles. During his career, he has managed hundreds of customer relationships, and gained in-depth experience with numerous cloud-based software, infrastructure, and platform services. In his roles thus far, Nate has striven to gain technical skills that he could one day put toward a new career trajectory in Data Science and Machine Learning. To culminate that process, Nate recently completed Metis' Data Science and Machine Learning bootcamp. Now, his aim is to secure a role where he can make use of his prior experience, work as part of a great team, and continue to learn and grow in his new career.
DiRenzo, Data Science & Machine Learning
DDoS Mitigation Solution
Classification / Big Data/Data Engineering / Anomaly Detection
Mitigating DDoS attacks and ensuring service availability using Classification models.
Pramila Chaudhary is a graduate with a Masters degree in Computer Science from Nova Southeastern University and Bachelors in Electronics and Communication from NIT Puducherry India, and has a great interest in new technologies. During her master's courses in "data mining" and "data visualization" she worked on large datasets and developed interest in pursuing the data science field and joined Metis Data Science program in order to expand her knowledge on how data can shape new products. She has worked on different projects and she enjoys cleaning data, data modeling, and providing great data science solutions through the results.
Chaudhary, Data Science
Classifying Poisonous Mushrooms: Pharmaceutical Company
Regression / Classification
Built a model using supervised learning and classification algorithms to determine whether a mushroom is poisonous or not for a pharmaceutical company and trained the model based on a dataset of 61,000 different mushroom physical characteristics and thereby improve the recall and f-beta by 24%.
Ryan is a former professor with a PhD in math and computer science. He transitioned to data science because he enjoys the challenge of solving real problems with data driven solutions. He has been coding in Python for over 10 years, and has particular interest in natural language processing, machine learning, and deep learning. Outside of work, Ryan enjoys board games, baking bread, and used book shopping.
Solava, Data Science & Machine Learning
Can Someone Find Me a Nice Latte?
Natural Language Processing / Big Data/Data Engineering / Recommender Systems
This project leverages natural language processing tools to create recommendations to coffee shops. An interactive web application was built in Streamlit, which implements the recommendation system. Coffee shop reviews were processed with tokenization, lemming, and CountVectorizer. Topic modeling with several dimensionality reduction algorithms (LDA, LSA, and NMF) were compared. Cosine similarity of topic vectors generate the recommendations.
Saramoira Shields, Data Science & Machine Learning
Saramoira is an exceptional analyst and communicator, capable of identifying and relaying key relationships in massive datasets. Most recently, she worked as a Robotics Engineer at an early stage startup. Prior to that, she was the Assistant Director of NASA’s New York Space Grant Consortium, where she managed a multimillion dollar STEM engagement and research program with 25 member institutions. Saramoira holds a bachelor’s degree in mathematics from Cornell University, where she studied topology, differential geometry, and manifold theory. Her strong mathematical background and extensive experience in science communication, government relations, and technical project management led her to pursue a career in data science. She is particularly passionate about leveraging big data to address social problems, such as issues related to equality, democratizing access to scientific data, and public engagement/education.
Shields, Data Science & Machine Learning
Hurricane Satellite Image Browser
Cloud Computing (AWS/Google Cloud) / Big Data/Data Engineering / Tableau/Dashboards
The Hurricane Satellite Imagery Browser is a cloud-based app that allows users to view, process, and download hurricane data and imagery from the GOES-R geostationary weather satellites.
Amir graduated from UCLA with a BS in Engineering and obtained an MBA from Mahan Business School. Having an eye for technology and professional experience by administering two startups, he decided to accelerate his career in Data Science and Analytics by obtaining a certification from Metis.
Amir enjoys spending time collaborating with others. He is also passionate about cooking, fitness, outdoor activities, and environmental sustainability.
Khoeilar, Data Science & Machine Learning
Classification of "Star Clients" for GetMoney.com
Regression / Natural Language Processing / Classification
Recognizing profit making clients for eCommerce businesses is a valuable advantage. By implementing classification models for the data provided by GetMoney.com, "Star Clients" features were identified. The results will aid the marketing team to allocate the correct marketing funds, and optimizing their search for the right clients.
Before completing Metis, Edward was a medical technician at BioReference Laboratory in the immunology department. Graduated from New Jersey Institute of Technology with a bachelor’s degree in General Studies. His passion for data and the stories it can tell is the reason why he wanted to get into data analytics. With this goal in mind, he decided to attend Metis intensive bootcamp.
Kerr, Data Analytics
Heart Disease Predictor
Medical cost is expensive for both patients and hospitals, if a predictive model was built and presented to patients before it gets to the point that they have to be admitted, not only will it save the hospital expenses but also most importantly the patient. Used Tableau to create visualizations that will convince hospitals that building a predictive model is worth the investment.
Ignasi Sols Balcells, Data Science & Machine Learning
Ignasi holds a Ph.D. in Cognitive Neuroscience, an M.Sc. in Neuroscience, and a B.Sc. in Biochemistry and has been coding for eight years, tackling interesting questions about human memory. Curiosity is his main driver and enjoys problem-solving, finding better and faster solutions, and making useful visualizations. Before joining Metis, Ignasi was a postdoc at NYU and Columbia University. Ignasi's broad background, bridging across different fields, has enriched him with diverse perspectives and strengths.
Sols Balcells, Data Science & Machine Learning
Predicting Hospital Readmissions
Developed a machine learning model that predicts which diabetic patients will be readmitted to the hospital within 30 days of discharge. This could be used to improve patient outcomes and hospital finances.
Matt Ryan is an aspiring data scientist with an undergraduate background in mathematics and physics. After working in business analytics for an agricultural engineering and manufacturing company for 3 years, Matt left to pursue data science training and completed the Metis Data Science bootcamp in December 2021. In his spare time, Matt likes to road cycle around his scenic hometown of Walla Walla, play guitar, and spend time with his cat, Storme.
Ryan, Data Science & Machine Learning
Natural Language Processing and The Dune Series
Clustering / SQL / Natural Language Processing
Using natural language processing techniques, I attempted to identify and model thematic exploration across installments in the science-fiction book franchise, Dune.
Nick holds a BSc in Finance and International Business from New York University and an MSc in Data Mining and Predictive Analytics from St. John's University. Prior to joining Metis, he was a corporate bond originator at UniCredit Bank AG in New York, helping American corporations understand the market dynamics and raise funding in Euro and US Dollar. His interest in weaving comprehensible narrative and client advice from financial data and fascination of machine learning's analytical abilities led him to pursue a full-time career in data science.
Kim , Data Science & Machine Learning
Catching bad actors on Ethereum blockchain
Classification / Regression
Detecting fraud perpetrators on blockchain by applying classification techniques on Ethereum addresses and the summaries of their activity, in an effort to support everyday users managing their counterpart's risk better
Sam is a geologist turn data scientist who recently graduated with a Masters in Geology from The University of Texas at Austin. While pursuing his degree, he completed several field seasons in Egypt, Russia, and Morocco and wrote two papers in pursuit of his research. While he enjoyed the research process–particularly visualizing all of the data he collected–he wants to transition into a field that is capable of having more tangible impacts on society. To that end, with so much data out there on any subject you can imagine, Sam views data science as an incredible tool to address a variety of pressing issues facing the world. As they say at UT, “What starts here changes the world”–Hook ‘em!
Robbins, Data Science & Machine Learning
Crafting a Winning Message
Natural Language Processing / Regression
Used Unsupervised NLP techniques to analyze the tweets of gubernatorial candidates and built a supervised regression model to predict tweet engagement. Proposed a new workflow for message refinement applicable to campaigns and businesses.
Cordelia’s love of problem solving and data mining lead her from the world of the social sciences to data science. Prior to joining Metis, she was a teaching professor at Rutgers (NJ) University where she both taught scientific psychology to undergraduate students, and managed graduate students teaching psychology lab classes. Cordelia has a BA in Mathematics from Williams College, and an MS and PhD in psychology from Rutgers University.
Aitkin, Data Science
Marketing to Banking Customers - Classification
Clustering / Classification / Regression
Which bank customers are most likely to buy a new product, based on results from a previous campaign? A Random Forest Classifier allowed us to determine which customers might be included in the new campaign.
After taking a hiatus from engineering to raise a family, Melissa’s data science path builds upon her hardware engineering background and incorporates machine learning through an intensive and immersive data science bootcamp. She is a curious, knowledge seeking machine learning engineer, dedicated to understanding data and applying flexible approaches to modeling and algorithms that empower solutions. Melissa earned her MS in Electrical and Computer Engineering from Carnegie Mellon University.
Cooper, Data Science & Machine Learning
Eco-Acoustic Monitoring of Endangered Species
Classification / Big Data/Data Engineering / Neural Networks
Rare species detection in dense ecosystems is central to climate change and conservation monitoring. CNNs enable real-time processing to predict bird and frog species by converting the audio to Mel spectrogram images within a deep learning pipeline.
Prior to Metis, Nick worked as a Team Lead in the Operations department for a FinTech start-up that focused on providing retirement plans to small to medium sized businesses. In this role Nick was able to work closely with software engineers and data analysts to help problem solve issues with the product. Through this collaboration Nick developed an interest in data and decided to take on the challenge of switching career paths. With a knack for data storytelling and a passion for helping others, Nick hopes to have a meaningful impact in his next role.
Pondok, Data Science & Engineering
Natural Language Processing with Movie Reviews
Natural Language Processing
Generated a topic model surrounding audience reviews for Marvel's movie Shang-Chi in order to see which topics were being discussed for both positive and negative reviews.
Born and raised in the San Francisco Bay Area, Andrew graduated from Santa Clara University with a BS in Psychology in 2016. Andrew has over 5 years of experience working in tech, most recently working in the FinTech industry. During his time at Metis, Andrew has developed a new interest in Data Science, Analytics, and programming and is looking forward to applying his skills into the tech industry.
Wong, Data Science & Engineering
Natural Language Processing for Amazon Video Game Reviews
Big Data/Data Engineering / Natural Language Processing / SQL
Built interactive web app for Amazon Video Game Reviews integrating Natural Language Processing and Topic Modeling. The app visualizes the positive/negative review topics to provide insight to game developers.
Bernard started his career in Philadelphia, PA where he obtained his Bachelors degree in Computer and Information Science. While in college, he participated in various summer research ranging from computational Biology to pure machine learning. After graduation, Bernard worked in sales and hospitality; wanting to return to his roots and pursue a more technical role, Bernard thought back to his research experience in undergrad where he worked on a massive data set using machine learning. Reinspired, Bernard enrolled in Metis' Data Science & Engineering track where he enjoyed using data, computing and mathematics to positively impact everyday lives of people. In his free time, Bernard enjoys playing table tennis and soccer.
Opoku, Data Science & Engineering
Auto FAQ Answering Machine
Recommender Systems / Big Data/Data Engineering / Natural Language Processing
Have you scrolled through a company's site to find an answer to an FAQ? Well not anymore with HDFC Bank. In this project, I used natural language Processing techniques to answer customer queries based on our FAQ database which contains the sites Frequently asked Questions and answer pairs.