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.
Alex is a recent graduate of the Metis Data Analytics Bootcamp. Prior to Metis, he worked in finance roles including wealth management, real estate consulting, and commercial banking. His desire to help businesses improve their performance by deriving insight from data led him to pursue a career in data analytics. He graduated from Colorado College in 2018 with a bachelor’s degree in Economics & Business.
Pope, Data Analytics
Improving Offensive Play Calling in the NFL
Big Data/Data Engineering / Tableau/Dashboards / Time Series/Forecasting
Identified inefficient decision making in NFL play-by-play data to advocate for the development of an offensive play calling model. Created Tableau dashboard for NFL coaches to help identify flaws in their play-calling decisions.
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.
As a former Process Automation Engineer at a major oil and gas company, Braden regularly leveraged data to drive decisions and to answer tough problems faced in the industry. He was also exposed to the analytical mindset during his time at the University of Texas at Austin, where he earned his degree in Chemical Engineering. It was these experiences that led Braden to discover a passion for data analytics. He is looking forward to applying his data science toolkit on challenging projects and by continuing to learn with his team.
Taack, Data Science & Engineering
What's In the Air? - An Air Quality Dashboard
Tableau/Dashboards / Big Data/Data Engineering
Air Quality can be of concern in many areas of the world. The goal of this project is to create an efficient data pipeline and leverage satellite-based air quality data into a Streamlit web application.
Prior to living in Colorado, Chris was born and raised in Queens, New York. With a background in retail management and sales, Chris eventually moved to the e-commerce industry and discovered a passion for Data Analytics. Joining Metis to continue growing his skillset, Chris hopes to have an impact on an organization whose main priority is to better the lives of others, and have a positive impact on the environment.
Guerrero, Data Analytics
Ecuador's Education System - An Analysis of Primary and Secondary Schools
Regression / SQL / Dashboards
An analysis of the relationship between areas with higher poverty rates, and number of primary and secondary schools in Ecuador.
Connie graduated from Stony Brook University with a B.S in Respiratory Care Therapy specialized in working with pediatrics and neonates. During her time at NYU Langone Health she often found herself working with data and was intrigued when she was analyzing patient data. While learning programming in her spare time, she realized how passionate and enthusiastic she became when applying data analytics to unravel patterns. Connie then made the leap of faith to transition from patient care to data science. She wants to use the power of data science and pursue a career in field for the greater good.
Xiao, Data Science & Machine Learning
Who Let The Dogs Out? - Dog Breed Image Classifier
With hundreds of dog breeds, it is often difficult to distinguish what kind of breed they are. By using convolutional neural networks and transfer learning it can accurately distinguish what breed they are based on a given image.
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.
Derek is a recent college graduate (Quantitative Economics) that really wanted to gain more hands-on experience with programming and machine learning concepts. Derek has 7 years of experience from his previous role as an IT Support Technician that helped him continually hone his communication skills in stressful environments. As Derek transitions to Data Science/Analytics he is excited to use skills learned from Metis, as well as his undergraduate degree, to help companies use data to make a difference in this world. Learning why and how things work is a lifelong passion of Derek's. When not working Derek wants to be in the mountains either camping or backpacking with his wife and 4 kids.
Call, Data Science & Engineering
Predicting Board Game Complexity
Regresson / Natural Language Processing / Classification
Used linear regression to determine which board game details best predict the complexity of a game. All data was obtained by web scraping the website boardgamegeek.com.
Detail-oriented Data Scientist with academic research experience in modeling, analyzing, and interpreting data with the purpose of driving business insights. Gulay has a strong theoretical background in operations research, applied probability, and finance - a BSc in Statistics and a PhD in Production and Operations Management. She joined METIS to take her programming and machine learning skills to a larger stage and has gained extensive on-hands experience through the projects. She is excited to use her new and existing skills and knowledge in a new Data Science position.
Identifying critical features affecting the occupancy level of a listing. The results can help the host optimize their listings to increase occupancy. As a result of higher occupancy, hosts will observe growth in their revenues.
Hannah is a goal-oriented researcher and detailed machine learning engineer who holds a PhD in Astronomy from the University of Virginia. As an astronomer and data science consultant at the University of Virginia Library, she gained extensive experience in the processing, classification, and segmentation of images from both telescopes and Earth-observation satellites. Hannah is passionate about employing deep learning methods and collaborating to solve challenging, data-driven problems. She hopes to apply her data science and machine learning skills to pursue a career that will allow her to impact the world around her.
Lewis, Data Science & Machine Learning
Extracting Road Maps from Satellite Images for Disaster Relief Networks
Classification / Neural Networks
A deep convolutional neural net for the segmentation of remotely-sensed satellite images. The CNN extracts road maps from images captured both before and after the impact of natural disasters to identify significantly impacted areas.
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.
Louisa is a chemist turned data scientist. She earned her Bachelor of Science in Biochemistry from Gonzaga University and her Master of Science in Chemistry from the University of Washington. Throughout her education, she participated in a multitude of research projects ranging from organic to computational chemistry. As a graduate student, she joined a material science and engineering lab studying the synthesis of semiconducting polymers for organic electronics. Due to her experience in computational chemistry, she joined an NLP project where she web-scraped chemistry textbooks and built a word2vec model for later use in named entity recognition of chemicals. This piqued her interest in data science and was the reason she joined Metis. She relishes the fast-paced environment, learning new topics, and applying her knowledge to solve business problems.
Reilly, Data Science & Machine Learning
Classy DnD: Detecting Multiclass Characters
Tableau/Dashboards / Classification
A character’s class in Dungeons and Dragons is akin to a job. Sometimes characters have more than one class. Using character sheet data, can a classification model predict whether a character is multiclassing?
Manveer holds a B.S. in mechanical engineering with experience in composite materials research and aerospace forging quality. In his teens, he became enamored with computer technology. He built home computers and was lucky enough to develop a foundation in object-oriented programming through a tech program in high school. More than a decade into his technical career, Manveer realized that the aspect of work that he enjoyed the most was obtaining insights from data. Data science was a natural way to combine his passions for data analysis and computer technology. This led him to enroll in the Metis Data Science & Engineering bootcamp to kickstart a new career path.
Sadhal, Data Science & Engineering
Reddit WallStreetBets Topic Modeling
Natural Language Processing / Tableau/Dashboards / SQL
A deep dive into the discussions on the WallStreetBets subreddit over time using natural language processing and unsupervised learning models.
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.
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.
Varsha graduated from Vanderbilt University with a degree in Neuroscience. Prior to joining Metis, her research experience working in an Autism lab and her formal coursework in Statistics and Computer Science helped her discover a passion for using technical skills and tools to find insights that drive impact. As a creative person and fan of the arts, Varsha enjoys data visualization and storytelling. She is excited to leverage her data science toolkit to tackle challenging problems, work on a team, and continue to learn.
Garla, Data Science
Pricing Airbnb Stays in NYC for Hosts
Big Data/Data Engineering / Regression / Anomaly Detection
Built a regression model to predict the price of Airbnb listings in New York City based on features of the property and online listing to determine a reasonable price for host’s profitability and customer affordability.
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.
For the past eleven years, Michael has been working in the service industry. Holding a variety of positions from server to manager, working in hospitality instilled a strong sense of customer service, attention to detail, and communication. Deciding he wanted a greater challenge, Michael began teaching himself various programming languages, and fell in love with data science. Manipulating data and finding actionable insights was challenging, but rewarding. After learning a whole new set of skills with Metis, Michael is excited to turn this new passion into a career.
Harnett, Data Science & Machine Learning
I'll Have What She's Having - A Cocktail Classifying Application
An interactive application that classifies a user-submitted cocktail photo. Backed by a convolutional neural network built using Google Colab, the application returns the highest probable match, along with the cocktail's ingredients and recipe.
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.
Sheralee Lovejoy earned her B.S. in Nutritional Science from Texas A&M University in 2016, who then went on to experience life unconventionally. It was through these hurdles and unexpected opportunities that she discovered her interest for data analytics. This began when she was put in charge of keeping track of a new product line. She used Excel analyze and identify shopping trends. With theses findings, she was able to tailor her surveys in order to collect and deliver better feedback to the corporate offices. Since her new discovery, she has taken on a fellowship with a venture capital firm to do market research. In addition, she has completed the data analytics curricula at Metis to acquire skills in data exploration, visualization, web scraping, regression modeling, and business analysis.
Lovejoy, Data Analytics
Predicting Opening Gross
This project developed a regression model to predict the opening gross income of a film, in order to help producers determine the success of their film, calculate a reasonable budget, and improve negotiation deals.
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.
After graduating from Harvard with a degree in Computer Science and a minor in English, Andrew Zhou set his sights on using his quantitative know-how and humanistic worldview to better the world in his own way. He discovered that his particular passion lays in data science, a field uniting considerable mathematical depth with an astounding capacity for fostering communication and understanding. After completing a data deep dive at Metis, he’s excited to bring his skills to bear on whatever problem comes next.
My Honor!: Modeling Avatar: The Last Airbender Fanfiction with Natural Language Processing
Neural Networks / Recommender Systems / Computer Software and Security
Using natural language processing, Andrew constructed topic and language models for Avatar: The Last Airbender fanfiction. He developed an application that leverages these models to recommend, generate, and present visual analytics for fanfiction.
Duncan holds a BSC in economics with a mathematical emphasis from the University of Wisconsin. Prior to joining Metis he was an equity trader for a proprietary trading firm in New York and Chicago. He has built his technical background through a combination of self-instruction and formal coursework. His interest in solving problems using an analytical approach and a drive to always be learning led him to pursue Data Science full-time.
Sheepshead Card Hand Evaluator
Classification / Regression / Tableau/Dashboards
Developed a live game assistant app that uses image processing techniques to identify held and played cards. Additionally, it uses machine learning to classify the quality of the player's cards, trained on a custom built database of hands.
Christine Lloyd is a data scientist and educator. While getting her PhD in microbiology, she wrestled with one of the problems facing most organizations today: does it really help to have all this data if you don’t have a streamlined and reproducible way to figure out what the data means? After several years teaching college, she realized that data science was an opportunity to continue wrestling with data problems, communicate with diverse stakeholders, and be a force for good in whatever organization she works with.
Classification / Clustering / Neural Networks
Recommends sweater patterns on Ravelry that are most similar to the user-input image.
Laura is a Ph.D. student in Evaluation, Measurement, and Research (EMR) at Western Michigan University. Her data analysis journey started with her first job in a marketing research firm and continued during grad school. With her work as an Engineering & Quality Data Analyst at a manufacturing company, Laura is excited to take her skills and domain knowledge and apply it to a new position in data science.
Paintings, Movies, and Emotions in the Context of Computer Vision
Applied Convolutional Neural Network, transfer learning with Resenet-101, and data augmentation to classify painting images by the emotion evoked in the observer.
Currently located in Berkeley, Ryan holds a Bachelor's degree in Mechatronic Engineering and has primarily held positions at companies developing robotics and automation engineering products. His passion is for discovering how new technologies can be applied to existing problems in order to find simple and elegant solutions. His positive outlook and ability to convey complex principles in a digestible manner make him a joy to work with!
Using Reinforcement Learning to Train Autonomous Vehicles
Anomaly Detection / Big Data/Data Engineering / Neural Networks
Used Deep Reinforcement Learning to train and model autonomous vehicles interacting with human drivers in a simulated urban environment.
An inquisitive and explorative person by nature, Allen is interested in making solutions that improve people's lives. Experienced in biomedical research, social outreach, and startups/small businesses, he aims to make a lasting mark by helping others. A love for complex multidisciplinary problems and a wide ranging knowledge on various subjects, allows Allen to adapt easily to any situation.
Cross Media Recommendations
Big Data / Natural Language Processing / Recommender Systems
A recommender that can suggest books similar to a movie or tv show and vice versa, to expand the user's palette and prevent oversaturation of a specific medium
Edith is a creative thinker with a passion for learning new and diverse skills and information. A Bay Area native, she graduated from the University of New Mexico in Spring 2019 with a B.S. in Applied Mathematics. Edith has a wide variety of interests, ranging from astrophysics and chemistry (how the world works) to evolutionary anthropology and psychology (how people work). She is an adaptive problem solver with dogged persistence in the face of challenges, and thrives in the analytic, logic based environment of data science. In her free time, Edith enjoys creating beautiful things, such as embroidery, painting, weaving and crochet, and caring for her many houseplants.
Born and raised in the New Jersey/ New York City area, I have always been passionate about learning new things and solving big problems. I have a B.S. in Computer Science, a minor in Mathematics, and three years of experience teaching the two subjects at the high school level. Currently, I am excited about exploring the world of Data Science and progressing in my career by continuing to infuse my passions for Math and Computer Science. For fun, I love to write and record music, spend time playing through my board game collection, and find new ways to maintain my athleticism and good health.
Natural Language Processing / Neural Networks / Recommender Systems
This project offers guidance for people new to podcast listening. The Flask app stores user likes and uses a Word Embedding Neural Network to generate search results and recommendations from "This American Life."
A former History teacher, Esteban holds a B.A. in History from The City College of New York with several courses in economics and jazz studies. Prior to joining Metis he was a law student, but it was his fascination with business efficiency and problem solving that led him to pursue a more technically focused, full-time career in data science. Outside of data science Esteban enjoys playing tennis and has been a guitar tutor for 20 years.
Formerly an Agricultural Engineer, Faustina’s passion for data was sparked by her experience in soil analysis and statistics. Realizing that data-informed decision-making could, for example, increase crop yield in her native Argentina, she transitioned to full-time work in data science. Faustina works side-by-side with individuals and organizations to solve problems using Machine Learning solutions.
Controlled Chaos: A tool for Taming Digital Clutter
Clustering / Natural Language Processing / SQL
A web application running locally and automatically that organizes text files based on its content. Controlled Chaos maps and classifies files, builds file clusters, and indexes them for easy application-based access.
Frederick Lam has a B.A. from Roosevelt University, majoring in Actuarial Science and a minor in Finance. During his studies, he found interest in the data manipulation and analysis side of Actuarial Science and it inspired him to pursue more data-focused roles after graduation, where he found out about the Data Science field. After searching for a year, he was pointed to the Metis Data Science bootcamp by a fellow Data Scientist. As a student at Metis, he took the opportunity to combine his passions, such as anime and video games, with complex machine learning algorithms and modeling. As a result, he created an anime recommender, based on the collaborative filtering technique, as his final project at Metis. His motivation for this recommender was not only to present the skills he'd learned at Metis but also to showcase how data science can be applicable to a general level of interaction and understanding.
Satisfying Your Anime Needs
This project is a take on Collaborative Filtering Recommender systems and how it works on a simple user-based level. This project also focuses on taking data from a reputable source, MyAnimeList, and creating an interactive recommender app using it.
Gio DeLisa has a BA in Economics with a minor in web development from New York University. Prior to Metis he helped to build and grow a small New York based apparel brand with a focus on local manufacturing. Always interested in learning, Gio is looking forward to being part of the data science community and its continual developments.
Forecasting for Fantasy Birding
Big Data / Neural Networks / Time Series/Forecasting
Developed convolutional sequence-to-sequence neural network to forecast expected amout of recorded bird ovservations in over 4,000 east coast locations. Used dask to process 100M+ bird obsrevations. Used the H3 library for geospacial indexing.
Recent Data Scientist bootcamp graduate with design background in architecture; fascinated by the possibilities of machine learning and big data. Prior to the Data Science Bootcamp, John was the Technical Director and a Project Manager at a creative/design oriented architecture + real-estate development practice.
Architectural Form Generation using LiDAR Surveys of Architectural Interiors
With over a decade of experience in the management consulting industry, Jonathan primarily focused on Business Intelligence and Visualization and is looking to leverage those skills with full data science tool kit. Jonathan is an avid traveler of the world, a car enthusiast, a husband and a father of 3.
Justin is a Bay Area native with a B.S in Accounting from Santa Clara University. His previous role was in Non-Profit Finance at the Kenneth Rainin Foundation. During that time, he was tasked to create various dashboards that were used for investment decisions. Most importantly, he scrutinized and ensured the accuracy of any report or dashboard so that the investment team can be 100% confident in their decision making. He's a highly focused individual, making sure there's no stones left unturned on whatever task he seeks out to finish. On his free time he enjoys golfing!
Classification / Neural Networks
Using neural networks for handgun detection within a video.
Kelsey graduated from the University of California, Davis in 2017. While pursuing his degree in linguistics, he developed an initial interest in data science through coursework in computational linguistics and NLP. After gaining experience in political research, he spent the past two years teaching high school speech and debate in the San Francisco Bay Area. But, despite a passion for helping his students find success, a growing desire to further his journey into the world of data and A.I. drove him to enroll at Metis. By combining a strong foundation in machine learning with his background in education and qualitative research, he seeks to bring data-driven perspectives to big-picture strategic thinking.
AniMaker: AI-Generated Story Concepts from Anime Plot Synopsis
Natural Language Processing / Neural Networks / Regression
Generation and curation of unique story concepts through training GPT-2 and regression models on Anime series plot synopsis and community ratings.
Louis has never had a problem asking questions- his curiosity seemingly knows no bounds- and data science allows him to put that curiosity into practice. From coming up with questions to finding insights in the results, there is no shortage of interesting discoveries to be made along the way. In a field that is continuously expanding, Louis finds himself well-prepared and excited to get started.
Visualizing Policing in Chicago
Classification / Tableau/Dashboards
Can we predict if a reported crime in Chicago will result in an arrest based on surrounding area of the crime?
Michael graduated from the University of Maryland, College Park with a degree in Economics. After graduating, he worked for a public health organization for five years where his most recent role was a production coordinator. In his role, he got exposed to learning and using data visualization with Tableau. By learning about data visualization, it peaked his interest in data science where he wanted to learn more about analyzing data and machine learning. For his final project, he used deep learning to see if he could detect anger in text.
Angry Text Detector
Classification / Natural Language Processing / Neural Networks
Michael set out to analyze the sentiment of text. He wanted to predict whether a text was angry or not. He did topic modeling, created a recurrent neural network model, and created a Streamlit app.
Nick is motivated, competent, and hard working and holds a bachelors in Anthropology from UCSB. Nick believes that his background as a technical writer, where he consulted with engineers and a high school tutor, where he was recognized for reaching difficult students provides him with a rich experience where he can contribute meaningfully to a team.
Image Creation with Generative Adversarial Networks
3 web apps for manipulating images in different ways - style transfer, face swap, and latent representation blending.
Raymond graduated with a BA in Computer Science from UC Berkeley. Prior to Metis, he was a quantitative researcher at Old Mission Capital where he led the research efforts on their options market making desk and managed risk across two low-latency strategies. At Berkeley, he has done research under several professors in computational game theory and latency arbitrage. He co-founded Fluint while in school, an angel-backed startup providing an online marketplace for peer-to-peer foreign currency exchange. Raymond enjoys taking on challenges in the areas of quantitative finance, behavioral sciences, computer vision, and game theory. Outside of work hours, he’s an avid poker player and travel photographer. Ask him for international travel and food recommendations or tips on collecting credit card bonuses.
AttackGAN: Adversarial Attacks Using GANs
Classification / Neural Networks
Researched new methods to generate adversarial examples using generative adversarial nets to fool deep image classifiers.
Sam began his career as a mechanical/controls/robotics engineer in the aerospace manufacturing industry. He encountered data analysis and statistics in the context of measurement, inspection, alignment, compensation, and qualification of parts and machinery and developed an interest in building further machine learning and programming skills at Metis to take part in the ongoing new industrial revolution. His areas of interest include manufacturing, industry, mechanical design, robotics, signal processing, sustainability, and environmental policy.
Sean graduated from University at Buffalo with a Bachelor's in English Literature. His data science skill set was developed at Metis where he defined, executed and presented five full scale projects based on regression, classification, and unsupervised learning techniques. Prior to Metis, Sean worked in the hospitality industry and wrote creatively. He looks forward to leveraging his passion for storytelling in a data driven role.
Tremendous Patterns in Speech: An analysis of the Trump Administration
Natural Language Processing / Tableau/Dashboards / Time Series/Forecasting
An analysis of the President and his press secretaries using transcripts from the White House Press Briefing and Press Releases.