in-person instruction + ongoing career coaching + job placement support
Bootcamp: September 2 - November 21
Application deadline August 11
Use real data from sports, business, government, or from where ever else you can borrow. Build a soup-to-nuts Passion Project to share with potential employers.
Leave fully qualified for an entry-level data scientist job. Placement Programs are available to all graduates.
Irmak is a partner and data scientist at Datascope, where he solves business problems with data by designing analyses and interfaces.
Irmak has helped companies across industries solve problems with data, from small companies to members of the Fortune 50. He also conducted and published academic research on a wide range of topics, such as web browsing habits of people from different demographics, school choices of high schoolers, global airline networks, endangered species conservation, natural language topic models, and optimizing DNA for protein production among others. Technically, Irmak is a Material Scientist (BS and MS, Sabanci University, Turkey), Chemical and Biological Engineer (MS and PhD, Northwestern University) and an Art Historian (Minor, Sabanci University). Practically, he believes in merging knowledge from different disciplines to ask and answer the right questions. When he is not striving for this, he believes in movies, bourbon, and Elliott Smith.
Laurie, a data scientist at Datascope, loves going after tough answers to great questions.
At Datascope, Laurie builds analytical tools for a wide range of clients to help them use data to solve their business problems. Prior to her switch to data science, she studied social neuroscience at the University of Chicago, building machine learning models of imaging data to explain how the brains of incarcerated psychopaths perceive and process emotions. Her least favorite part of data science is pruning the scope of a project down to a manageable size. Actually, that is her least favorite part of most things. Laurie is a passionate teacher, and she is very excited to return to the front of the classroom for the Data Science bootcamp.
Alex, a recent developer bootcamp graduate, loves to create compelling offline experiences that help people connect.
Hailing from the San Francisco Bay Area, Alex is a cultural technologist, an individual that uses technology to shape and inform culture. After graduating from UCLA as a theater major, he worked with a handful of technology start-ups in both technical and operational capacities. He believes access to education and technology cultivates healthier societies. At Metis, he hopes to connect people to technology that will help them improve their lives, and the lives of those in their community.
This bootcamp runs in-person for 12 weeks, Monday through Friday. It is preceded by online pre-work focused on command line, Python, and installing various packages.
Applicants must have some previous experience programming (writing code) and studying or using statistics.
Upon graduating, you will be comfortable designing, implementing, and communicating the results of a data science project, including knowing the fundamentals of data visualization and having introductory exposure to modern big data tools and architecture such as the Hadoop stack. You should feel confident pursuing a job as an entry-level data scientist or data analyst. Read our syllabus
We offer a . We also provide a $2,000 refund when you accept a position through our placement program.
If you are a woman, part of a underrepresented minority group*, or a veteran or member of the U.S. military, you will be automatically eligible to receive a $2,000 scholarship toward your Metis tuition.
Why? The story is a familiar and unfortunate one:
Women make up less than one-third of all employees in the tech sector. Tech companies employ an average of 12.33 percent female engineers. Women contribute to just 1.2 percent of open source software.1
Only four percent of people in software development, application and systems jobs are African-American and five percent are Hispanic or Latino.2
Women of color represent less than three percent of the people in technology fields.3
As a country, we need to reverse these trends and create more avenues for talented individuals from underrepresented demographic groups and communities to help drive our future economic growth. This scholarship is a step toward supporting a more diverse workforce. Scholarship funds are applied to Metis tuition only and are not transferable.
Scholarship eligibility is subject to validation. Metis has sole discretion in the award of the Scholarship and the right to revoke the Scholarship offer for prospective applicants at any time.
* Underrepresented minority groups include African Americans, Mexican-Americans, Native Americans (American Indians, Alaska Natives, and Native Hawaiians), Hispanic and Latino Americans, Pacific Islanders, and mainland Puerto Ricans.
The bootcamp experience is intense, but we aim to maximize learning while preventing burn-out. Metis believes that a student’s brain is like a muscle, and to grow without injury the brain must take time to recover. Therefore, we operate five days a week from 9-6. Each Monday - Thursday consists of three hours of group classroom instruction and five hours of practical skill development and project work. Fridays are “personal investment days” for catch-up, independent work, special guest speakers, career-related work, and/or fun.
We’ll provide a Command Line Crash Course, tutorials to become familiar with Python, and a number of package installation tutorials (i.e., numpy, scipy, pandas, scikit.learn), as well as some preliminary statistics work. Test-out/check-out modules will let students know when they are “prepared enough” for class.
After the at-home pre-work phase, we will convene in class and spend our first 9 weeks together doing iterative, project-centered skill-acquisition. Over the course of four data science projects we’ll "train up" different key aspects of data science, and results from each project will be added to the students' portfolios. In the last three weeks, students build out and complete their individual Passion Projects, culminating in a Career Day reveal of their work to representatives from our Metis Hiring Partners.
Students will complete an entire bite-sized data science project from start to finish. We’ll start using the IPython environment and Git for version control, use the pandas package to perform exploratory statistical analyses, and publish the results using the matplotlib package.
For Project 2, students start to learn one of the most important tools a data scientist uses: the iterative design process. We’ll learn tools for web scraping and start fitting simple models to data. Also, we introduce cloud computing: students will work on remote servers.
We’ll go in-depth on regression using modules from scikit.learn and matplotlib. Choosing among the analysis methods and approaches to reporting their results, students will finish the second project and present their findings.
We cover relational databases such as SQL and more ways of obtaining, cleaning and maintaining data. We overview the concepts of machine learning and introduce classification and supervised learning with a few examples such as logistic regression and KNN. We will also discuss different types of feasibility related to data science questions and projects.
More detail and more algorithms for supervised learning including SVM, decision trees and random forests; techniques for feature selection and feature extraction; concepts and applications for deep learning. Students will choose to apply one or more of these algorithms as part of this unit’s project.
The project for the fourth unit will involve text data. We’ll round out data acquisition methods with APIs and online database servers. The students will also learn about NoSQL databases and start using MongoDB.
We’ll analyze the text data collected in the previous week and learn about naive Bayes and NLP algorithms. We’ll learn about how large amounts of data are handled, discussing parallel computing and Hadoop MapReduce.
Greater depth on unsupervised learning and more algorithms, covering K-means, hierarchical clustering, mixture models and topic models. Project 4 presentations will be presented as lightning talks.
Students switch gears and work full time on their Passion Projects (which they've been designing in bits and pieces through the first 9 weeks). They will also learn more on cloud computing, system architectures and feasibility evaluations.
Upon graduating from the Data Science bootcamp, a student will be prepared to take an entry-level position on a Data Science team as a data scientist or data analyst. This means a student will:
Bootcamp: September 2 - November 21
Application August 11