Free Online Corporate Training Series: Intro to Python Register Now
12-Week, in-person Bootcamp
New Live Online Format Also Available!
Monday - Friday, 9:00am - 5:00pm
2-3 hours of classroom instruction daily
4-6 hours of development and project work
400 total clock hours
Experience with programming and stats
30 hours minimum of academic pre-work and variable hours to setup.
Python, Bash Shell, Git & Github workflow, data wrangling and EDA (Exploratory Data Analysis) with Python, pandas, and Matplotlib
Linear regression theory/application, web scraping via BeautifulSoup and Selenium, machine learning concepts (overfitting and train/test splits)
Intro to time series modeling, statistics review, intro to Bayes Theorem, linear regression regularization, hypothesis testing
Classification and regression algorithms (Knn, logistic regression, SVM, Naive Bayes), SQL concepts, cloud servers
More algorithms (Classification and regression trees, Random Forest), interactive visualization with business intelligence tools, web dev essentials, Flask
NLP (textblob, NLTK, chunking, stemming, POS tagging, tf-idf) data & databases (RESTful APIs, NoSQL databases, MongoDB, pymongo), k-means
More clustering algorithms (DBSCAN), machine learning topics (curse of dimensionality, dimension reduction, PCA, SVD, LSI), intro to deep learning & neural networks
A/B testing experiment design and interpretation, distributed databases (Dask, Hadoop, HiveQL)
PySpark, deep learning (CNNs, RNNs) data ethics, MapReduce algorithm, and final project initiated
Consider one of our part-time bootcamp prep courses.
Gain the skills you need + apply your tuition paid towards the bootcamp.
Vinny, a Metis data science instructor, views data as a facet of perception.
Vinny comes to Metis after leading a team of Data Scientists at High 5 Games. Prior to that he taught Machine Learning at General Assembly and built tools for Animators and Effects Artists at Blue Sky Studios (the company that made Ice Age, Rio and The Peanuts Movie). He has a Masters in Computational Engineering and another in Creative Writing. He enjoys the nexus of mathematics, computer programming, human perception and arts. Over the past three years, Vinny has been knee-deep in large distributed data -- aggregating, building recommendation systems, measuring popularity and predicting Lifetime Value. In that time, he also finished a first draft of his novel. An avid programmer, Vinny has won various programming contests including the Regional ACM Collegiate Programming Contest. He has taught at the University of Miami and has given talks and presentations at various colleges and conferences.
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Alice, a Metis data science instructor, enjoys making complex things easy to understand.
Alice comes to Metis from Cars.com, where she started as the company's first data scientist, supporting multiple functions from Marketing to Technology. During that time, she also co-founded a data science education startup, Best Fit Analytics Workshop, teaching weekend courses to professionals at 1871 in Chicago. Prior to becoming a data scientist, she worked at Redfin as an analyst and at Accenture as a consultant. She has her M.S. in Analytics and B.S. in Electrical Engineering, both from Northwestern University. She blogs about analytics and pop culture on A Dash of Data. Her blog post, "How Text Messages Change From Dating to Marriage" made it onto the front page of Reddit, gaining over half a million views in the first week. She is passionate about teaching and mentoring, and loves using data to tell fun and compelling stories.
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Article: Geek of the Week (GeekWire)
Joe loves tackling unusual problems with the tools of data science and has a passion for effectively communicating quantitative ideas.
As both a math enthusiast and a former competitive debater, Joe was drawn to data science by its place at the intersection of statistics, computing, and communication. He has worked on projects ranging from quantifying and comparing story plots to building a Bayesian model of human rights abuse rates that accounts for informational bias. Before transitioning into data science, he worked in various data analytics roles, most recently as an Equity Research associate on Credit Suisse's portfolio strategy team. He holds a B.A. in Mathematics from Columbia University, and is also a Metis alumnus. He currently coaches NYU's parliamentary debate team, and in his free time he enjoys playing piano, reading, and playing board games.
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Kimberly uses her background in applied math to discover data's what, why, and how.
Kimberly joins Metis from MRM//McCann, a leading digital advertising agency, where she focused on helping clients understand their customers by leveraging unstructured data with modern NLP techniques. She is passionate about data storytelling and the power of compelling data visualizations to challenge pre-conceived assumptions. Kimberly's enthusiasm for teaching comes from her days as an academic. She holds a Ph.D. in applied mathematics from Rensselaer Polytechnic Institute and completed a postdoctoral fellowship in math biology at the Ohio State University. In her spare time, Kimberly likes to stay active and particularly enjoys swing dancing, rollerblading, and jogging with her dog.
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John is a data scientist with experience in machine learning, cloud technologies, and business intelligence.
John joins Metis from WithumSmith+Brown, where he was a manager in their data and analytics practice. At Withum, John led engagements developing end to end solutions for clients with applications ranging from data management and cloud infrastructure to predictive analytics and business intelligence. He has taught data science for General Assembly as well as Microsoft training workshops and professional education courses for financial professionals.
Trevor is a life-long teacher who stresses the importance of model interpretability as it pertains to business practices.
Trevor has taught the Metis Bootcamp with our Corporate Training team at the AI Institute of Advanced Studies in Riyadh, KSA. He has a BA in Mathematics from the Courant Institute of Mathematics at New York University. Before Metis, Trevor was at IAA Capital Management, where he incrementally introduced Machine Learning techniques to the analysis of financial markets. At every step of implementation, he built a proof-of-concept to demonstrate the added value. The biggest takeaway from his experience would be the ability to account for unknown-unknowns; an invaluable but often unaccounted for skill that prepares predictive models for what we haven’t before experienced. In his spare time, he is an avid reader and strong believer in the (added) effects of physical exercise and mindfulness on “life performance”.
As a former teacher assistant for several courses at Columbia, Richard is excited to share his knowledge of data science, academic and industrial, with aspiring data scientists of all backgrounds.
Richard Chiou holds a BA in Computer Science and Economics-Mathematics from Columbia University and a Master of Engineering degree in Data Science and Systems from UC Berkeley. He joins Metis from Signifyd, where he worked on machine learning models for online e-commerce fraud prevention. In his spare time, he is a board game enthusiast, a frequent escape room artist, and an intrepid traveler who seeks to uncover the mysteries and pleasant surprises hidden in life.
Omar is a data scientist and neuroscientist with a passion for understanding complex data and how it relates to the real world.
Omar completed his MS in Health Disparities in Neuroscience-related disorders at Wake Forest University, where he researched cognitive functioning, brain connectivity, and brain structure in psychiatric and neurological conditions. Previously, he researched meditation and neuroplasticity at the Martinos Center for Biomedical Imaging at the Massachusetts General Hospital. In his spare time, he enjoys hiking, cooking, and spending time with friends.
Chris Bruehl, a Data Science Instructor, enjoys empowering others to turn data and algorithms into clear business results.
Chris has always had a passion for business and mathematics, which led him to his undergraduate degree in Economics at Cal Poly. After working in international supply chain management for several years, he realized the best part of his day was sitting down with a cup of coffee and crunching data, so he returned to school to get his Masters Degree in Analytics at North Carolina State University. He brings 4 years of data science experience in the insurance industry, working on a wide array of projects from customer conversion modeling to fraud detection. In his spare time, he is a dog dad, gamer, and vinyl record hunter.