Online Data Science Bootcamp from Metis

Typical Data Science Bootcamp Curriculum

MODULE 1 -

Python and Math Fundamentals

MODULE 2 -

Exploratory Data Analysis

MODULE 3 -

Business Fundamentals for Data Practitioners

MODULE 4 -

Linear Regression & Data Acquisition

MODULE 5 -

Machine Learning Classification

MODULE 6 -

Natural Language Processing (NLP) & Unsupervised Learning

UNIT 1: Programming Fundamentals

Students begin this module by learning general coding concepts and then develop programming skills using Python as well as the command line.

UNIT 2: Mathematics Fundamentals

In the second unit, students learn and apply relevant math skills from the fields of probability, statistics, linear algebra, and calculus.

Project Due:

Students develop their new Python programming skills by creating a computer program to play a well-known game. They implement game play logic through coding constructs like loops and conditional statements. They then simulate several rounds of game play under different strategic assumptions and use probability and statistics to evaluate each approach.

UNIT 1: Exploratory Data Analysis Basics

Throughout the first unit, students learn the basics of exploratory data analysis and learn how to use tools such as SQL and Python libraries.

UNIT 2: Exploratory Data Analysis Advanced

During the second unit, students learn about advanced SQL and Python techniques.

Project Due:

The EDA project includes extracting insights from a messy data set, writing code in Jupyter notebooks, and performing exploratory data analysis and visualizations using Python packages. Students also work with a relational database to ingest and query data.

UNIT 1: Business Analysis

Students learn how to identify, design, and scope data science projects, including the basics of using spreadsheet tools for data analysis and the best practices in data visualization.

UNIT 2: Presentations, Project Management and Ethics

Students learn best practices of delivering presentations and dive into the ethical implications of working with data. They also learn the basics of project management.

Project Due:

Students explore a business problem that interests them to develop a solution with data in a practical and ethical way. They explore, describe, and extract insights from tabular data sets while building interactive dashboards to present those insights.

UNIT 1: Linear Regression Basics & Data Acquisition

Students learn the basics of linear regression as well as feature engineering and model validation. Additionally, they learn how to acquire data from APIs.

UNIT 2: Linear Regression Advanced

Students learn about advanced methods in linear regression, including regularization and gradient descent. Students also get an introduction to time series regression methods.

Project Due:

Students show off their new skills by solving a linear regression problem. They gather data through an API, go deep into regression theory, and practice using Python libraries such as scikit-learn. They demonstrate their ability to apply foundational machine learning techniques like feature engineering and cross-validation.

UNIT 1: Classification Basics

Students learn the basic classification models, classification metrics, as well as feature engineering for classification problems.

UNIT 2: Classification Advanced

Students learn advanced classification models and learn how to work with imbalanced data sets.

Project Due:

Students solve a classification problem using algorithms such as KNN, logistic regression, naive Bayes, decision trees, random forests, and gradient boosting. This module’s project allows them to further explore the foundational concepts and techniques in supervised machine learning, determine the proper metrics to use for their modeling problem, and address potential challenges involving class imbalance.

UNIT 1: Natural Language Processing & Unsupervised Learning Basics

Students learn the fundamental concepts in natural language processing, dimensionality reduction, and recommendation systems.

UNIT 2: Natural Language Processing & Unsupervised Learning Advanced

Students are introduced to clustering algorithms and advanced natural language processing techniques.

Project Due:

In preparation for the project of this module, students learn the fundamentals of NLP and unsupervised learning, as well as how to properly apply each. With the project goal of analyzing text data using NLP algorithms, students utilize different techniques for dimensionality reduction such as principal component analysis, apply clustering algorithms such as k-means, and build topic models.

Program Benefits

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

ONLINE FLEX FORMAT

Part-Time

On-demand lectures let you work on your own time, so you can fit studying into your daily life. You’ll also get dedicated 1:1 instructor support, an organized study plan, and deadlines to keep you on track.

Length

Weekly Time Commitment

Schedule

Lectures

Instructors

Office Hours

Instructor 1:1s

Projects

Project Presentations

Career Support

Online Flex