DATA SCIENCE & ENGINEERING BOOTCAMP

Jumpstart Your Tech Career

Become a competitive job applicant in the data engineering field with the Metis Data Science and Engineering Bootcamp. This program consists of expert-led classes and five projects, plus Career Support.

Acquire In-Demand Tech Skills and Transform Your Career

Our bootcamp will teach you how to use and leverage essential data engineering skills—but we don’t stop there. Our career advisors will support you during and after the bootcamp to help in your job goals.

Expert Data
Science Instructors

Metis teachers aren’t just data scientists, they’re also experts at teaching and engaging students in an online classroom.

A Professional
Portfolio

Use the projects you complete during bootcamp to build a knock-out portfolio, and wow your interviewers.

Career Support During
and After Bootcamp

Our career support is multifaceted. You’ll work with a dedicated advisor and get connected with our world-class hiring partners.

A Lifelong
Community

The Metis alumni community is something you want to be a part of. Connect, network, and make lifelong friends.

Data Science & Engineering

MODULE 1 - 

Exploratory Data Analysis

MODULE 2 - 

Linear Regression & Web Scraping

MODULE 3 - 

Introduction to Data Engineering

MODULE 4 - 

Machine Learning Classification

MODULE 5 - 

NLP & Unsupervised Learning

UNIT 1: Exploratory Data Analysis Basics

Get a grasp on the basics of exploratory data analysis and how to use tools such as SQL and Python libraries.

UNIT 2: Exploratory Data Analysis Advanced

Understand advanced SQL and Python methods used in Exploratory Data Analysis.

Project Due:

You’ll extract insights from a messy dataset. You will also use Jupyter notebooks to write code, the pandas Python package to perform exploratory data analysis, and packages like Matplotlib to visualize results. Finally, you’ll work with an SQL-based relational database to obtain, clean, and maintain data.

UNIT 1: Linear Regression Basics and Web Scraping

Learn the basics of linear regressions, feature engineering and cross validation, and how to webscrape.

UNIT 2: Linear Regression Advanced

Learn about advanced methods in linear regression, including regularization and stochastic gradient descent. Plus, we’ll introduce you to time series regression methods.

Project Due:

You’ll solve a linear regression problem. You will gather data using web scraping tools and go in-depth into regression theory, practicing using python modules such as scikit-learn. Finally, you’ll apply foundational machine learning techniques such as validation and feature engineering.

UNIT 1: Advanced Coding and Cloud Computing

Learn advanced programming techniques, advanced database tools, cloud computing, and web application deployment.

UNIT 2: Big Data

Learn the techniques and application of big data handling tools.

Project Due:

You’ll develop a modularized data processing pipeline, incorporating tools such as cloud computing, relational and non-relational databases, web application deployment, and big data handling tools (Hadoop or Spark).

UNIT 1: Classification Basics

Learn basic classification models, classification metrics, and feature engineering for classification problems.

UNIT 2: Classification Advanced

Understand advanced classification models and how to work with imbalanced datasets.

Project Due:

You’ll solve a classification problem using algorithms such as KNN, logistic regression, Naive Bayes, decision trees, random forests, and gradient boosting. You’ll further explore the foundational concepts and techniques in supervised machine learning, determine the proper metrics to use for your modeling problem, and address potential challenges involving class imbalance.

UNIT 1: Natural Language Processing and Unsupervised Learning Basics

Learn the basics of natural language processing, recommendation systems, and dimensionality reduction. In addition, you’ll discover some basic clustering techniques.

UNIT 2: Natural Language Processing and Unsupervised Learning Advanced

Learn advanced clustering algorithms and natural language processing techniques.

Project Due:

You’ll analyze text data using NLP algorithms. You’ll then utilize different techniques for dimensionality reduction such as Principal Component Analysis, apply clustering algorithms such as K-means, and learn topic models such as Latent Dirichlet Allocation.

Program Benefits

Our project-oriented, career-driven approach will take you to places you never thought you could go.

ONLINE FLEX FORMAT

Part-Time

Study on your own time with pre-recorded lectures. Structured modules, dedicated 1:1 instructor support, and assignment deadlines keep you on track.

Length

Weekly Time Commitment

Schedule

Lectures

Instructors

Office Hours

Instructor 1:1s

Projects

Project Presentations

Career Support

Career Support the Whole Way Through

Work with career advisors with unparalleled industry experience throughout the bootcamp and after graduation.

ONE-ON-ONE GUIDANCE

A dedicated advisor will help you map out your desired job location, industry, salary, and company.

SPEAKER SERIES

Join our speaker events where industry experts will share their experiences and give you advice.

JOB SEARCH TUTORIALS

On-demand videos will teach you to build your online presence, craft a resume, and network effectively.

WORKSHOPS

Join our expert-led workshops and learn how to network, write a great resume, and practice for job interviews.

MOCK INTERVIEWS

Mastering the interview process takes practice. Work with our experts to nail your technical and non-technical skills.

GRADUATE DIRECTORY

You’ll be added to our graduate directory automatically. It’s a place for hiring managers to view your profile, read your resume, and review your portfolio.

CONTINUED SUPPORT

We’ll support you until you get a job. Period. Plus, you’ll get access to our Alumni Portal where opportunities are posted regularly.