Metis Bootcamp Programs

Flexible. Project oriented. Expert led. Our online bootcamp curriculum offerings are designed to give professionals the skills and knowledge they need to succeed in high-demand fields, including data science and analytics.

Build Skills. Make Connections. Get Hired.

Acquiring essential skills is just the start. Our project-oriented, career-driven approach will take you to places you didn’t think you could go.

Learn From Elite
Instructors

Our industry-seasoned practitioners must meet a high bar of instructional excellence.

Graduate with
Professional Projects

Put together an impressive portfolio of projects you can bring to interviews.

Build Career Skills

Get career support made for you. Plus, connect with our hiring partners.

Become Part of a
Lifelong Community

Join an active alumni community of data professionals you’ll keep up with for life.

Career Support:

Metis does not offer placement services or guarantee employment or career advancement.

Sample Bootcamps

The fundamentals of data analytics—from implementing regression models to applying exploratory analysis—using real-world data sets

The application of data science theory, skills, and tools, including extracting insights and building models to solve business programs

Core data science skill sets, advanced coding, cloud computing, and big data handling tools

A combination of topics from our other curricula offerings—from analytics and machine learning to engineering—plus a deep dive into neural networks and deep learning

Our Bootcamp Format

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

ONLINE FLEX

Part-Time
  • On-demand lectures let you work on your own schedule, with dedicated, live 1:1 instructor support and office hours
  • Clear deadlines for assignments and organized modules provide structure and accountability
  • Perfect for those who are employed or want more flexibility

Length

Weekly Time Commitment

Schedule

Lectures

Instructors

Office Hours

Instructor 1:1s

Projects

Project Presentations

Career Support

Career support

Our career advisors offer unwavering support and personal guidance.

WORKSHOPS

ONE-ON-ONE

SPEAKER SERIES

MOCK INTERVIEWS

POST-GRAD
SUPPORT

GRADUATE
DIRECTORY

Data Analytics Bootcamp Curriculum at a Glance

MODULE 1 - 

Exploratory Data Analysis

MODULE 2 - 

Linear Regression and Web Scraping

MODULE 3 - 

Business Fundamentals for Data Practitioners

UNIT 1: Exploratory Data Analysis

Learn how to use tools like Python and SQL, and build fundamental knowledge of exploratory data analysis.

UNIT 2: Exploratory Data Analysis Advanced

Explore the advanced methods used for Python and SQL when performing data analysis.

Project Due:

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

UNIT 1: Linear Regression Basics and Web Scraping

You’ll learn linear regressions fundamentals, how to web scrape, and the basics of feature engineering and cross validation.

UNIT 2: Linear Regression Advanced

You’ll continue mastering linear regressions with advanced methods like regularization and stochastic gradient descent. We’ll also introduce you to methods for time series regression.

Project Due:

Solve a linear regression problem by gathering data with web scraping tools, and go in-depth into regression theory. You’ll also practice using python modules such as scikit-learn. Finally, you’ll apply the foundational machine learning techniques you’ve learned from validation to feature engineering.

UNIT 1: Business Analysis

We’ll teach you how to use spreadsheet tools to analyze data, as well as the best practices for data visualization.

UNIT 2: Presentations, Project Management and Ethics

You’ll learn ethical implications and the best practices of working with data. Plus, you’ll acquire basic project management skills.

Project Due:

Apply your knowledge to solve a business problem using data in an ethical, practical way. This involves using project management practices and iterative design techniques. You’ll then describe and extract insights from tabular data sets while building interactive data dashboards.

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.

Data Science & Machine Learning

MODULE 1 - 

Exploratory Data Analysis

MODULE 2 - 

Linear Regression & Web Scraping

MODULE 3 - 

Business Fundamentals for Data Practitioners

MODULE 4 - 

Machine Learning Classification

MODULE 5 - 

NLP & Unsupervised Learning

MODULE 6 - 

Deep Learning Fundamentals

MODULE 7 - 

Introduction to Data Engineering

UNIT 1: Exploratory Data Analysis Basics

Grow your knowledge of fundamental exploratory data analysis. This includes using programming languages like Python and SQL.

UNIT 2: Exploratory Data Analysis Advanced

We’ll expand on unit one, diving deeper into Take your EDA learnings a step further by learning advanced methods for SQL and Python.

Project Due:

Your project will involve extracting insights from a messy dataset, writing code in Jupyter notebooks, performing exploratory data analysis using Python, and visualizing results with Matplotlib packages. You’ll also work with a relational database to pull and clean data.

UNIT 1: Linear Regression Basics and Web Scraping

We’ll teach you how to web scrape. Plus, we’ll cover the basics of feature engineering, cross validation, and linear regressions.

UNIT 2: Linear Regression Advanced

Discover advanced linear regression methods, including regularization and stochastic gradient descent. Plus, become familiar with time series regression methods.

Project Due:

Show off your new skills by solving a linear regression problem. You’ll gather data through web scraping, go deep into regression theory, and practice using python modules such as scikit-learn. You’ll show your ability to apply foundational machine learning techniques like feature engineering and validation.

UNIT 1: Business Analysis

We’ll show you how to leverage data visualization best practices and spreadsheet tools for data analysis.

UNIT 2: Presentations, Project Management and Ethics

Explore expert techniques for presentations, project management basics, and the ethical complexities of working with data.

Project Due:

You’ll choose a business problem to solve using data in a practical, ethical way. This will involve the application of iterative design techniques and project management practices. You’ll also explore, describe, and extract insights from tabular data sets in addition to building interactive dashboards for that data.

UNIT 1: Classification Basics

In unit seven, you’ll explore the basics of classification models and metrics, as well as feature engineering for classification problems.

UNIT 2: Classification Advanced

Next, you’ll expand your classification knowledge by working with imbalanced datasets.

Project Due:

Show off your just-learned skills by solving a classification problem. You’ll use algorithms like KNN, logistic regression, decision trees, gradients boosting and more. Then, you’ll dive deeper into the concepts and techniques for supervised machine learning, address the challenges involving class imbalance, and determine the metrics used for your modeling problem.

UNIT 1: Natural Language Processing and Unsupervised Learning Basics

Explore the NLP basics, recommendation systems, and dimensionality reduction. Then, we’ll introduce you to techniques used for basic clustering.

UNIT 2: Natural Language Processing and Unsupervised Learning Advanced

Learn and apply NLP techniques and advanced algorithms for clustering.

Project Due:

This project will involve text data analysis using NLP algorithms. You’ll use different techniques for dimensionality reduction like Principal Component Analysis, apply topic models like Latent Dirichlet Allocation and clustering algorithms like K-means.

UNIT 1: Neural Networks, Embeddings and Convolutional Neural Networks

Learn the basics of deep learning and neural networks. This includes transfer learning, embeddings, and convolutional neural networks.

UNIT 2: Sequence Modeling

We’ll explore methods for modeling sequential data and deep learning.

Project Due:

You’ll select a problem related to text, time series, images, or other complicated data formats, and train your models or apply transfer learning techniques. This is your opportunity to show that you can solve data science problems using deep learning algorithms.

UNIT 1: Advanced Coding and Cloud Computing

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

UNIT 2: Big Data

Learn how to use big data handling techniques and tools.

Project Due:

You’ll show that you can develop a modularized data processing pipeline, implementing tools like relational and non-relational databases, big data handling tools, cloud computing, and web application deployment.

On the Fence About Bootcamp?

All of the modules you’d complete at bootcamp are offered as Short Immersive Courses. If you’re only interested in a specific topic or if you want to get a feel for the experience before committing, this is a great option.

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