Alison Garrett is a data science and machine learning consultant supporting organizations that serve communities. Previously she was a technical leader in the strategy, planning, implementation and operation of complex technology programs and capabilities for public and private organizations. Most recently she served as the Vice-President, Solution Development for Kythera Space Solutions, a start-up developing systems for autonomous, dynamic management of next generation satellites and SATCOM networks. Alison earned a MS and BS in Electrical and Computer Engineering from Virginia Tech.
Garrett, Data Science & Machine Learning
Delivering to Washington DC Food Deserts
Delivering to Washington DC Food Deserts is a business analysis to profitably help eliminate Washington DC Food Deserts. The project leverages multiple Open Data DC datasets with advanced Excel techniques and Tableau visualization.
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.
Atrin holds an MSc in Mechanical Engineering from the University of California, Berkeley. While working at several AI and manufacturing startups, he was inspired by machine learning and data science’s ability to enrich and enhance existing industries as well as to generate completely new, disruptive features altogether. Joining this bootcamp was sparked by this interest, and he is excited to play a role in these modern technological evolutions.
Sarmadi, Data Science & Machine Learning
SF Parking Meter Recommendation System
Big Data/Data Engineering / SQL
Built an end-to-end recommender system for SF parking meters based on user address and time inputs, Geocoding using APIs, stored and processed data using SQL and Python Pandas, created a web application using Streamlit for user interface
Chien Yuan loves to find solutions and make things happen and believes that data science has many potentials to achieve these goals. He decided to come back to data science career after dedicating 8 years to social service and community work ; with a non-profit organization empowering older adults in the community to have a better and healthier life in Seattle. Chien Yuan holds bachelor's degree in Public Health and master's degree in Health Policy and Management from the National Taiwan University. He had worked as a marketing researcher in the pharmaceutical industry about one year before immigrating to the United States. Chien Yuan is currently looking for a team where he can make a positive impact and grow together with his expanding data science skills and tools.
Web app with neural network model deployed on streamlit is designed for YouTube creators to optimize titles, descriptions, tags and thumbnails. Automatic data ingestion and storage were set up on cloud.
With a pivot into full-time data science, Cindy holds an advanced degree in Information Security and Accountancy. Prior to joining Metis, she has years of experience working as a test engineer for telecom system and MES system. She has strong cross-disciplinary collaborative experience in planning and designing technical solutions for business needs. Cindy has advanced proficiency in multiple programming languages and is passionate about data exploring, analytics, and problem-solving.
Su, Data Science & Machine Learning
Identify Bankruptcy risk Through Classification
Big Data/Data Engineering / Regression / Classification
Built a classification model through feature engineering and model tuning to predict company bankruptcy risk or financial distress for bank creditors or investors.
Over the past three years, Clayton has co-authored several academic manuscripts and managed a neurology research lab investigating decision-making and neuroethics. Here, he began analyzing and visualizing clinical data in R, which spurred him to strengthen his technical skills and explore novel ways to apply data science to solve practical problems. The projects completed through Metis reflected his interests–ranging from cryptocurrency and soccer to public health and human cognition. Clayton holds a Bachelor of Science from the University of California, Davis, where he majored in Biopsychology and gained research experience in two cognitive neuroscience labs.
Young, Data Science
An ear to r/CryptoCurrency
Natural Language Processing
Using PRAW, a Reddit API wrapper, collected data from r/CryptoCurrency. Performed topic modeling and sentiment analysis to investigate the topics associated with each crypto and how Redditors felt about them.
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.
Emily earned a Bachelors of Science in Accounting and Finance, with a minor in Biology, from the University of Nebraska, Lincoln. Prior to joining Metis, she was a District Manager for Scooters Coffee in Lincoln, Nebraska. Overseeing six store locations. During her time there she found a love for making user-friendly tools for her team to better understand sales trends from a large data dashboard. Her passion for data, math, and problem solving led her to pursue a full-time career in Data Science.
Ubbelohde, Data Science & Machine Learning
Avocado Classification and Price Prediction
Regression / Classification
Using data gathered from Hass Avocado Board to first classify avocados as conventional or organic. Then utilizing the predictions from the classification model to build and tune a regression model for price prediction.
Evelyn is a recent graduate of the University of Connecticut, where she earned an Honors Bachelor of Science in Allied Health Science. There, she wrote a thesis using data science to create a food environment index through a deep learning-based image recognition model, where she became fascinated by data. This combined with her role as lead coordinator for a UConn survey research project, Evelyn sought to expand her analytical skills and deepen her understanding of machine learning models. Evelyn's hunger for learning and her passion for optimization will propel her drive within the field of data science.
Johnson ,Data Science & Machine Learning
Home: Helping or Hurting the Environment? Classifying Energy Star Certification in NYC Apartment Buildings
House Hunting for an Environmentalist: Classifying Energy Star Certification in NYC Apartment Buildings
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.
Jared is a data scientist and business analyst. Previously, he served as an Associate Manager at Accenture, where he managed junior resources on SaaS product implementations while coordinating with intra-project teams on large-scale digital transformation projects. Outside of work he enjoys sports and spending time with his golden-doodle, Churro. Jared earned a B.A. in Mathematical Economic Analysis from Rice University.
Williams, Data Science & Machine Learning
Airline Passenger Satisfaction Analysis
Regression / Classification
The goal of this project was to create an interpretable model that would provide insight into which airline amenities were most likely to influence whether a customer had a satisfactory experience with the airline.
When the Dot-com bubble burst, John returned his laptop and put JAVA / Sybase books in storage and heading off to pursue an MBA in Finance from Indiana University. As a VP of Finance at Citi, he came to realize he was less interested in the numbers being reported than the systems and analytics behind the numbers. This led to more IT related positions and ultimately the decision to 'retool' with Metis. He is excited to apply his consulting background to new problems and work with the latest technologies.
Michitsch, Data Science & Machine Learning
Stock Trend App
Cloud Computing (AWS/Google Cloud) / Tableau/Dashboards / Big Data/Data Engineering
Viewed stock price trends via a Streamlit hosted app using API calls to an Oracle Cloud Autonomous Database with time series forecasts obtained via Facebook Prophet module
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.
Data Scientist with STEM background, proficient in Python, SQL, and Java. Experience using regression, classification, natural language processing, and neural network models to solve business problems. Accepted to Carnegie-Mellon University but deferred enrollment until 2023. Interested in pursuing a full time career in Data Science.
Kwee, Data Science & Machine Learning
Classifying ASL Alphabet Hand Signs
Classifying ASL Alphabet Hand Signs / Classification / Neural Networks
Classified ASL hand signs using a Multilayer Perceptron neural network and Google's MediaPipe hand pose detection module.
Michael Kamel is a Data Science enthusiast with 2 years of experience as a Business Development Analyst in the Aviation and Hospitality Industry. During this time, he learned how to use data analytics to enhance the B2B sales process.
Prior to this, Michael graduated from Georgetown University with a double major in Finance and Management and a minor in Entrepreneurship. His passion during school was innovation and problem solving, and his goal to start his own company down the road.
Michael hopes to continue to bolster his data science skills and pair them with his previous sales experience. His current career goals can be described by this quote from Naval Ravikant: “Learn to sell, learn to build, if you can do both, you will be unstoppable.”
Kamel , Data Science & Machine Learning
Creating Human-NFT Hybrids Using Neural Style Transfer Models
The goal of this project was to utilize a Neural Style Transfer Model to fuse a person's face with the art NFT of their choosing.
Nick is a problem-solver who enjoys utilizing data to inform critical decision-making by answering complex questions. Prior to joining Metis, Nick worked as a researcher at an investment research firm, servicing clients at top hedge funds, private equities, and credit firms, and assisting them in understanding the intricacies of industries and companies they invested in. At Metis, Nick worked to develop his data analytics and machine learning competencies. He applied these techniques to real-world topics like understanding lumber price trends, predicting employee attrition, and analyzing sentiments from music reviews. He continues to seek projects that present fascinating challenges with the potential for impactful and transformative results for both individuals and organizations.
Bronson, Data Science & Machine Learning
Real-Time Data Pipeline & Dashboard for US Earthquake Analytics
SQL / Cloud Computing (AWS/Google Cloud / Big Data/Data Engineering
A data pipeline and a Streamlit app for tracking and analyzing earthquake trends between 2010 and the present. Analysis of the December 2021 earthquake surge off the coast of Oregon using this app.
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
Nina finds joy in combining her liberal arts background, project management experience, and data science skills to approach complex problems and communicate technical solutions. In her role as a Project Manager at a coding bootcamp, she overhauled the school's national expansion strategy to be data-driven and repeatable, launching two campuses in new cities. The analyses she completed as a Business Analyst for the company's CEO directly informed executive decision-making and future strategy. Nina has studied data science after-hours since 2019 and decided to attend Metis to begin applying her technical skills to a new career tackling social and climate justice-related challenges.
Sweeney, Data Science & Machine Learning
Accelerating Our EV Future: Increasing Electric Vehicle Adoption through Machine Learning
Identified relationships between car features and positive consumer reviews scraped from Kelly Blue Book to guide the design decisions of future electric vehicle manufacturers and increase EV adoption.
Paul began his career in commercial real estate as an investment analyst and project manager, however, his analytical mind and desire to always understand “why” uncovered his love for data. To accelerate his career transition into data science, he recently completed Metis’s full-time data science bootcamp. Paul is fascinated with data’s universal relevance across business and enjoys engineering impactful solutions for complex problems.
Troy, Data Science & Machine Learning
Ski Jacket Reviews: NLP Analysis
Natural Language Processing
Scraped customer ski jacket reviews from Evo.com. Applied topic modeling, part of speech tagging and sentiment analysis techniques to identify customer jacket design preferences.
Prior to Metis, Prathap worked as an actuary in the life insurance industry. He pursued this role due to his passion for digging into numbers and interpreting them to tell a story. After eight years in this function and passing all actuarial exams required for full designation, Prathap wanted to expand his analytical skills beyond actuarial and financial modeling. This led him to pursue a pivot to data science via Metis and he looks forward to what the future holds in the data science realm.
Rajaraman, Data Science
Predicting Your Next Favorite Movie using NLP
Recommender Systems / Natural Language Processing
Built a recommendation system based on the summaries of thousands of films to predict what movies a user may like based on their previously liked films.
Praveena is a Senior IT Analyst with Masters In Computer Science and Bachelors in Mathematics. Given her background in Math and CS, she discovered a passion for using data analytics and machine learning while working with reports and dashboards in her current job which were used extensively to draw business insights. This led her to join Metis to do a Data Science and Analytics bootcamp. After learning about the wide range of applications of Data Science, she is excited to use her problem-solving skills using data in a new data science role.
Suresh, Data Science
Predicting Credit Card Fraud
Designed a model using supervised learning and classification algorithms to predict whether or not a transaction is fraudulent and thereby improve the efficacy of fraudulent transaction alerts for people and help businesses reduce their fraud loss and increase their revenue.
Roberto is detailed oriented, hard-working, and always willing to learn. He is an entrepreneur that built his own company in the tourism industry in his home country, Venezuela, where he developed and managed the entire sales funnel, and strengthened his client facing skills. After moving to Brooklyn in 2019, he started to develop a broader understanding of the challenges that societies across all levels of development are facing, and the relevance of data in the present and future, that led him to join Metis, where he built the foundations of his data science and machine learning skills. He earned his BE in Telecommunications Engineering from the University of Carabobo. Some of his personal interests include: fitness, meditation, concerts, and the nourishing of social relations with friends and family.
Linares, Data Science & Machine Learning
Topic Modeling of Venezuela's News Articles.
Natural Language Processing
Topic Modeling over 5 year period (1999- Present) to identify key factors that led to crisis in Venzuela.
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.
Stephen achieved top honors obtaining his MSc in Structural Engineering and a professional engineering registration (P.E.) because of his aptitude for mathematics and understanding of complex systems. Always fascinated with advanced computer programming and problem solving, he discovered a passion for Data Science though his desire to speed up his engineering workflow through computer automation. Building upon his innate talent and emerging interest, he spent the last year studying computer science, probability and statistics, with a focus on linear algebra and machine learning techniques.
Blount , Data Science & Machine Learning
Classification / Big Data/Data Engineering / Neural Networks
Built a real-time eye state image classification system using a convolutional neural network (CNN) and computer vision. OpenCV was used to detect the eyes of a person from a video and was sent to the CNN for classification.
Zeynep has 10+ years of experience in cultural product & management and has been contracted by museums & cities to work as a producer & curator creating cultural exhibitions and experiences internationally. She has also built and managed the branding for 2 arts advisory companies. With a background in product management and branding, she attended the Metis Data Science & Machine Learning program in order to expand her knowledge of how data can shape new products. She holds a Studio Art & Mathematics major from Dartmouth College and an MA from Bard College in Curatorial Studies.
Oz, Data Science & Machine Learning
Analyze & Play: LostPoets on OpenSea
Neural Networks / Big Data/Data Engineering
This project uses engineering and data tools to analyze the financial data on the NFT collectible LostPoets as listed on OpenSea while also creating derivative images using a GAN model. Both are deployed on Streamlit.
With a love for problem-solving and a BA in archaeology, it was the examination of data and the creation of narratives around the insights from the data that drew Alex to data science. His technical experience comes from formal collegiate training and self-instruction. He is known for providing solutions to a variety of technical issues of moderate scope and complexity.
Stake, Data Science
Predicting League of Legends Wins
Regression / Classification
Predicting winners in League of Legends within the first 15 minutes using a logistic regression model and identifying the features with the highest increase in log odds.
Andrew is a graduate of NYU’s Stern School of Business with a B.S. in Finance and Management. Andrew began his career working in investment management, analyzing the financial health of high-yield companies. This role developed a passion for digging into numbers and interpreting them. After four years in the industry, Andrew wanted to expand his analytical skills beyond financial modeling. With interests ranging from food to blockchain, sports to tech, he realized that he was constantly looking at and using statistics and data in his spare time - leading to his pursuit of data science starting at Metis. Andrew possesses a hunger for knowledge and a love of problem-solving that he is excited to complement with a new data science skillset.
Seo, Data Science
Pitching Wins Championships
SQL / Regression
Identified the most important factors contributing to a pitcher’s success (with success being measured as maximizing ERA+) using linear regression models.
Fascinated by how data science and machine learning automate the process of data analysis, and make people's lives more convenient and provide data-driven decisions in real time without human intervention, Andy started his journey to learn data science. Prior to joining Metis, Andy served as a pharmacist, working with doctors and patients to ensure both parties receive the most value out of the prescribed medications. Andy earned his PharmD from the University of Colorado, Denver, and a B.A. in Molecular and Cell Biology from the University of California, Berkeley. Outside of work, he loves hiking, snowboarding, and spending time with his wife and two lovely daughters.
Wang, Data Science & Machine Learning
What's Your Car Worth?
SQL / Regression
Built a web application to accurately predict used car sale prices.
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.
A former risk manager and market-maker on the CBOE and CME who fulfilled his lifelong dream of opening a successful Italian restaurant. However, with his passion still for data, analytics and problem solving, Data Science was the perfect field to combine his past experiences with computer technology. To bridge his background in statistical Sociology, the markets, and entrepreneurship he attended Metis to sharpen the technical skills needed to quantify and programmatically express ideas. Within the field of data science, his main interests are data sourcing and cleaning, algorithmic modeling, and providing real-world impact through experimentation, analysis, and implementation.
Palumbo, Data Science & Engineering
Stock Predictions: As a Classification Problem
SQL / Big Data/Data Engineering / Classification
Developed a classification pipeline that uses historical stock data, a suite of custom functions, and machine learning to calculate the probability that a stock will hit a predetermined target price on a monthly basis.
John is a highly curious scientist who loves to solve new and challenging problems. While getting a bachelor’s and then a master’s degree in physics, he came to realize that the skills he loved to use and grow the most were those pertaining to analyzing and modeling data. This led John to pursue a career in Data Science. To prepare for this career switch, he dove deep into literature on machine learning theory and best practices, studied for and passed the TensorFlow developer certification, and completed an intensive 10 week data science and engineering bootcamp.
Lassetter, Data Science & Engineering
Solar Power Production Predictor for the Consumer
Big Data / Data Engineering / SQL
The solar power production predictor is a web app that provides users with an estimate of how much money they can save on electricity by installing solar panels where they live.
While earning his BA in Economics, Joey discovered his love for data analytics and the power of simple, clear data visualization. Both fields heavily rely on an interdisciplinary approach and an analytical mindset. Prior to joining Metis, Joey was in a sales role which taught him the importance and power of understanding his customers. His passion for storytelling, and aptitude for problem solving drove him to pursue a career in data analytics.
Brazzale, Data Analytics
What Makes a Hit?
SQL / Big Data/Data Engineering / Regression
Built a linear regression model to predict which inputs (genre, budget, release date) have the greatest impact on domestic revenue.
As a former New York City math teacher, Max Harrington constantly leveraged the connection between data and student outcomes. It was this relationship that led him to pursue data science full time. A recent graduate of Metis, Max has applied his mathematical background to address regression, classification, and natural language processing problems. Previous to teaching, Max worked in the outdoor education industry, leading corporate and school groups on wilderness expeditions. Max earned his M. Ed. in Math Education from St. John’s University and B.A. in English from Boston University. Outside of work, he loves exploring NYC through bike lanes and restaurants.
Harrington, Data Science
Learn More, Spend Less: A Regression Analysis of US College Tuition Prices
Using web scraped data for four year colleges across the US, this project used a regression model and feature engineering to accurately predict the tuition price for a university relative to other institutions.
Maya is a physician turned data scientist with a clinical background in Women's Health, Obstetrics & Gynecology. She holds a B.S in Biochemistry with Highest Honors from UCLA and a M.D. from UC San Diego School of Medicine. Prior to coming to Metis, she worked for an e-learning company, where she wrote medical content and managed other medical writers. Her love of problem-solving, statistical thinking, and coding led her to pursue a career in data science.
Remington, Data Science & Machine Learning
Turning down the jab: factors associated with low COVID vaccination rates in the US
Used linear regression to determine the political and socioeconomic factors that best explain low COVID-19 vaccination rates at the US county level. The primary dataset was obtained by web scraping the CDC website. Regression coefficients were used to identify the most highly correlated features.
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.
Thrilled by the elegance and inherent beauty of some of the Machine Learning algorithms, Milad joined Metis in order to expand and polish his knowledge of data science. He received his PhD in the field of Neuroscience from Rutgers University which helped him develop critical thinking and a strong problem solving skills. He seeks to combine these abilities with his data science skill set, to tackle and solve real-world problems.
Afrasiabi, Data Science
Identifying the Flake!
Classification / Big Data/Data Engineering / Regression
Built a classification model to identify hotel bookings with the highest chance of being cancelled.
Mishka is a self-driven, positive, and curious Data Scientist with strong problem solving skills. She graduated from UC Santa Barbara with a BS in Financial Mathematics and Statistics and a minor in French. She then spent several years working in logistics and demand planning in the food and beverage industry. Because of her constant curiosity to learn and passion for analytics, Mishka decided to join Metis. She enjoys data cleaning, data modeling and providing easily interpretable and actionable results to other departments in a company. When Mishka is not learning new Machine Learning algorithms, she enjoys spending time outdoors and being with friends and family.
Asli, Data Science
Minimize Loan Default Through Classification
Regression / Classification / Tableau/Dashboards
Designed a classification model through feature engineering and tuning to help banks determine whether a loan application should be approved or denied. This can aid banks in efficiently categorizing loan applicants to maximize profits.
Mitch graduated from Indiana University with a degree in Mathematics. His first role out of college was at a fin-tech company where he worked in customer service before getting promoted to the operations department. This was a really great opportunity as he was able to cross collaborate with a lot of different departments and learn about different positions in a tech company. Mitch found himself taking more analytic projects and worked closely with the BI team where he began to hone his data storytelling skills. It was this experience that propelled him to enroll in a data science bootcamp and refocus his career.
Seiter, Data Science
Credit Card Fraud Detection
Regression / Anomaly Detection / Classification
Using supervised machine learning and classification algorithms such as random forest, KNN etc. I built a model that detects fraudulent credit card while minimizing false positives.
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.
Zhi has a strong knowledge in computer science fundamentals after graduating from University at Buffalo with bachelors and masters in computer science. His strong programming and problem solving skills highlight his talents in the technology industry. Zhi also develop sophistication with the experience as a retail seller, teacher assistant, and other service related experience. He has an optimistic mindset, takes feedback well, and is always willing to learn from others. His inquisitive mind makes him a strong data scientist by asking meaningful questions when solving problems. Coming from a computer science background, his goal is to bring his prior skills to the data scientist world and keep exploring, solving, and telling more stories with others.
Huang, Data Science & Engineering
Music Genres Classification
Classification / Regression
Deployed various classification models after pre-processing music sounds of 10 different genres into 35 different features in order to test the best classification model and help applications with classifying.
Aiman graduated with a B.S in Biology from Macaulay Honors College at CUNY. Prior to Metis, she worked as a researcher in a neuroscience lab, analyzing the results of multiple rehabilitative therapies on skilled motor performance in mice with bilateral and unilateral spinal cord injuries. With an initial interest in pursuing a career as a physician, many of Aiman’s prior roles were intimately involved in recommending workflow optimizations at hospitals, effectively tackling the problem of long patient wait times and overworked doctors at once. It was here that Aiman discovered a passion for using data analytics and machine learning in order to drive meaningful change in real world situations.
Chughtai, Data Science
Need Car Insurance? - Using Classification Algorithms to Predict Cross-Buying
Classification / Regression
In order to predict whether a health insurance policy-owner would be interested in cross-buying car insurance from their insurance company, multiple classification algorithms, including Sklearn’s Logistic Regression, Decision Tree, Random Forest, XGBoost models were leveraged.
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.
Crystal holds a BSc in Exercise Science from Rutgers University and is a registered sonographer in four specialties. With 3 years of working experience in the medical field as a sonographer, Crystal found her way to pursue data science full-time through the application of deep learning in medical images. She loves learning new skills and seeks to uncover hidden meanings and patterns. More recently, she sees the value of data and is keen to utilize it to capture elusive insights about the world.
An interactive web app for face mask detection leveraging pre-trained Haar cascade classifier with openCV and custom-trained mask detector with convolutional neural network using cloud computing on Google Colab.
Diego earned a Bachelor in Business Administration from Universidad Católica del Táchira (UCAT) and a Master in Finance from Instituto de Estudios Superiores de Administración (IESA). Prior to the Metis bootcamp, he served as an account executive at Yelp using SalesForce tools and data to target small businesses and provide different solutions to boost their sales. With an interest in statistics and analysis, some of Diego's prior roles were always focused on doing financial analysis and business development, using data to provide solutions based on models, and scientific evidence.
Dong transitioned from marketing to data science to work closer with data. He's curious about what insights data could reveal and stringent on the ethical use of data to ensure that a deployed model is adding value to the world. At Metis, he identified opportunities in a government agency and media, food, airline, and sports companies and presented scalable data solutions.
Zhen, Data Science
A Topic Model's Beginner Guide to Fishing in New York
Natural Language Processing
Answer commonly asked fishing questions with local insights found from topic models on posts from New York's two largest fishing forums.
Hernan is a Business Administrator with extensive experience in Market Research. Charged with building industry trends and analytic reports for past clients, Hernan's interest was sparked and his desire to work with more advanced data analytic tools and machine learning served as the catalyst for him to pursue a full-time career in data science. His versatile background provides him with a familiarity of analyzing massive amounts of data while holding a keen business perspective.
Trujillo, Data Science
Bank Marketing Prediction
Tableau/Dashboards / Big Data/Data Engineering / Classification
Based on a Bank marketing phone calls campaign, built a classification model to predict which clients will subscribe a term deposit investments account
Aaron is a Data Engineer with a lifelong passion for gaming and data. He holds a Bachelor's in Game Design and has worked as a Product Manager for the last 6 years. During his role, he interacted closely with developers, marketers, and clients in the Health & Wellness industry. Having to frequently use SQL in his role as a PM and formerly as a Data Specialist he decided to pursue data engineering. During his program, he discovered two new passions in the form of automation and the creation of data pipelines.
Barela, Data Science & Engineering
Product Drops Tracker
SQL / Big Data/Data Engineering / Cloud Computing (AWS/Google Cloud)
Scraping multiple sites to generate push notifications via (Discord) webhooks, for products which are in demand and/or sold out nearly instantly.
Lin Mei holds a MS in Electrical Engineering from Polytechnic University, Brooklyn NY. Prior to studying Machine Learning techniques at Metis, he spent two decades developing and trouble shooting software for telecom systems. He enjoys detective type work to gain better understanding of the inner working of many different things. The many powerful new tools provided by ML leads him to Data Science as a way to resolve important real life problems.
Mei, Data Science & Engineering
Review Filtering Using NLP
Clustering / Natural Language Processing / Big Data/Data Engineering
Using Nature Language Processing techniques, filter product reviews to a much smaller set of distinct reviews, to remove reviews too similar to each other.
Sabrina Yang is a recent graduate of Metis’ Data Science Bootcamp. Originally from Taiwan, she started off her career working as a sales data analyst at Procter and Gamble and later as an account executive at Ogilvy and Mathers. Prior to leaving for New York, she also had the opportunity to work as an FX trader at a bank, gaining insight into the financial services industry. In New York, she completed a Masters in Data Science and Machine Learning and found herself working in multiple data analyst roles at retail and real estate firms in the city. Through these experiences, she recognized the importance of data science in all industries and hopes to align her personal passion for data science with a career in the field.
Yang, Data Science & Machine Learning
Bag Hunter - Luxury Bags Brands Image Classification App
Bag Hunter is an image classification app that is designed to identify luxury bag brands using only their images. This app will help retailers identify luxury brands quickly without needing human verification.
Yordanos has an MBA from Lincoln University, California. She believes working with data is a very powerful way of identifying and coming up with solutions for real world problems. Joining Metis was her important steps to develop the necessary skills as a data scientist and she will continue to learn more and more about the field each day.
Woldebirhan, Data Science
Depression Drug Reviews with Topic Modeling
Tableau/Dashboards / Natural Language Processing
This project analyzes the issues that concern patients on depression medications using the reviews they provide about the drugs they are taking.
Born, raised, and based in the San Francisco Bay Area, Gabriel Vieira Equitz earned a BSc in Computer Science from San Francisco State University in 2019. Afterwards, Gabe chose to follow his ardor in machine learning and joined Metis, desiring the up-to-date instruction and tools they offer. During this time, he learned technical and communication skills critical for data scientists. Gabriel appreciates the empirical insights data science brings to the world and wants to make a positive difference with his skills. In his personal life, Gabriel likes studying history is an animal lover.
Sovereign Risk Model
Classification / Regression / Time Series/Forecasting
This model calculates the probability of future sovereign default for more than 200 countries using macroeconomic data. Performance is evaluated using different machine learning techniques. Web app functionality is built for the model.
Data scientist with advanced degree in Mathematics and Statistics, and strong experience in analyzing and interpreting data for driving business solutions. Advanced proficiency in multiple programming languages and deep understanding of applied analytic.
AirBnb Recommendation System
Natural Language Processing / Recommender Systems / SQL
Deployed a machine learning algorithm to create a recommendation system on airbnb listings based on user's previous airbnb.
Satenik holds bachelor’s and master’s degrees in Business Management. She has more than six years of experience working in operations management, data analytics, finance and marketing. Prior to the bootcamp in her most recent role as an Operations Manager she led a team of up to 30 people and was tasked with conducting analytical experiments to help solve various business problems. As part of her transition into data science Satenik completed multiple online courses on Statistics, Data Analytics and Machine Learning, and enrolled into the Metis. She thrives in fast-paced environments, loves working with people and is most passionate about creating business value using data.
Self-Supervised Voice Emotion Recognition using Transfer Learning
Classification / Natural Language Processing / Neural Networks
Built a self-supervised voice emotion classifier using transfer learning. The model classifies audio clips of human voice into positive or negative emotion classes.
Shannon received her undergraduate degree from the University of Michigan in Ann Arbor, and has since gathered over 6 years experience in the startup industry across various departments. She is Co-Founder and COO of a ‘500 Startups’ SaaS company which sparked her interest in big data and data science. She’s an accomplished leader and self-driven individual with an entrepreneurial mindset and strong business acumen. Her passion for data science is fueled by her love of strategic problem solving and growing small businesses through the power of machine learning technologies. She is especially interested in natural language processing and machine learning architectures, and is excited to continue learning and take these new skills and apply them to the data scientist field––eager to help shape the future.
Scotch Whisky Recommender
Natural Language Processing / Neural Networks / Recommender Systems
Created a Scotch Whisky recommendation system and interactive web app that uses NLP and neural networks from online review sources to recommend and predict similar Scotch preferences based on user input.
Young Suh graduated from UCSD with B.S in Cognitive science with specialization in machine learning and neural computation and a minor in mathematics. Prior to Metis, he worked at a bio-tech startup as a computer engineer, analyzing genotypes and phenotypes of DNA sequences, along with developing covid-19 detection kit. Young is interested in applying different types of machine learning and deep learning models to solve real life problems and exploring. In his free time, he enjoys dancing, brewing coffee and experimenting using raspberry pi.
Reci-py Recommender: Minimizing Food Waste
Recommender Systems / Natural Language Processing
Recommending recipes to users using ingredients provided along with user preferences to minimize food waste.