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12-Week, in-person Bootcamp
Monday - Friday, 9:00am - 5:00pm
2-3 hours of classroom instruction daily
4-6 hours of development and project work
Experience with programming
Experience with stats
25 hours minimum of academic pre-work and variable hours to setup.
Exploratory Data Analysis, Bash, Git & GitHub, Python, pandas, matplotlib, Seaborn
Web scraping via BeautifulSoup and Selenium, regression with statsmodels and scikit-learn, feature selection overfitting and train/test splits, probability theory.
Regularization, hypothesis testing , intro to Bayes Theorem
Classification and regression algorithms (Knn, logistic regression, SVM, decision trees, and random forest), SQL concepts, cloud servers
MLE, GLM, Distributions, Databases ( RESTful APIs, NoSQL databases, MongoDB, pymongo) Natural Language Processing techniques
Various clustering algorithms, including K-means and DBSCAN, dimension reduction techniques (PCA, SVD, LDA, NMF)
Deep Learning via Keras, Recommender Systems
Hadoop, Hive & Spark, Final project initiated
Consider one of our part-time bootcamp prep courses.
Gain the skills you need + apply your tuition paid towards the bootcamp.
Debbie, Chief Data Scientist at Metis, uses her "physics glasses" to solve challenging real-world problems and promote critical thinking.
Deborah Berebichez is a physicist, data scientist and TV host. She has expertise in scientific research and advanced analysis and she has helped automate decision-making and uncover patterns in large amounts of data. Her passion lies in merging critical thinking skills with practical coding skills. She specializes in drawing connections between the approaches used in data science and the challenges organizations face. Deborah has a Ph.D. in physics from Stanford and completed two postdoctoral fellowships at Columbia University's Applied Math and Physics Department and at NYU's Courant Institute for Mathematical Sciences. She is a frequent mentor of young women in STEM. Her work in science education and outreach has been recognized by the Discovery Channel, WSJ, Oprah, Dr. Oz, TED, DLD, WIRED, Ciudad de las Ideas and others.
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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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Jonathan Balaban is a consultant, data scientist, and entrepreneur with ten years of private, public, and philanthropic experience.
As a data scientist, he has worked at McKinsey and Booz Allen Hamilton, and he has taught data science at General Assembly. He has led teams to design bespoke data science solutions that have driven revolutionary changes in client operations. Jonathan - sometimes successfully - leverages data science solutions in his personal life: on friends, racing, and training.
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Talk: Fortune-Telling with Python: Intro to Facebook Prophet (ChiPy Users Group)
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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Sophie loves extracting representations and understanding from data and pushing data science to be a more positive and inclusive discipline.
Sophie Searcy comes to Metis from Elektra, a wearable startup that is replacing haptics with electricity. At Elektra she was cofounder and CTO, designing everything from the electronics to the framework for analyzing data. Before that she worked in the CoDaS lab at Rutgers where she combined cognitive science and theoretical computer science to build models of how people and machines teach and learn. She holds masters degrees in Electrical and Computer Engineering and Psychology. She is passionate about teaching, both in theory and in practice, and about making sure that data science is primarily a tool that is used to improve people's lives.
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Article: A Case for Diversity
Chad enjoys that data science gives practical approximations to the complex uncertainties of reality.
Chad comes to Metis from a diverse technical background. After earning his mathematics PhD from Indiana University, Chad joined Pacific Northwest National Laboratory to work on statistical and computational challenges ranging from homeland security to high-performance computing and machine learning research. Following several of his publications at top-tier ML conferences, he turned to probabilistic programming, then still in its infancy. He has used these systems for consulting projects for industrial clients, and has led development and publication of several new ones along the way. In his spare time, Chad enjoys a wide range of music, and practices martial arts, where he has black belts in several styles.
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Article: Bayesian Optimal Pricing
Robert loves to break deep technical concepts down to be as simple as possible, but no simpler.
Robert has data science experience in companies both large and small. At Intel, he used his knowledge to tackle problems in data center optimization using cluster analysis, enrich market sizing models by implementing sentiment analysis from social media feeds, and improving decision making in one of the top 5 global supply chains. At Tamr, he built models to unify large amounts of messy data across multiple silos for some of the largest corporations in the world. He earned a PhD in Applied Mathematics from Arizona State University where his research spanned image reconstruction, mathematical epidemiology and oncology. Robert is an Adjunct Professor at the Leavey School of Business where he teaches Data Science and Machine Learning. In his spare time, he is a rum judge, avid traveler, and eater of all things coconut.
Content by Robert:
With a background in physics and quantitative finance, Adam has applied data science and machine learning solutions in a wide variety of research settings ranging from recognizing and clustering behavioral patterns to applying natural language processing techniques to help build superior equity portfolios. Adam is passionate about teaching and is excited to share his industry experiences with students.
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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.