11-Week Part-Time Course

Math and programming skills

Skills & Tools

Use Python to mine datasets and predict patterns.

Data manipulation tools

Production Standard

Build statistical models — regression, classification, clustering — that generate usable information from raw data.

Learn to make predictions with modeling

The Big Picture

Master the basics of machine learning and harness large datasets to forecast what’s next.

Meet your support team

Our educational excellence is a community effort. When you learn at GA, you can always rely on an in-house team of experts to provide guidance and support, whenever you need it.

  • instructor


    Learn industry-grade frameworks, tools, vocabulary, and best practices from a teacher whose daily work involves using them expertly.

  • teaching assistant

    Teaching Assistants

    Taking on new material isn’t always easy. Through office hours and other channels, our TAs are here to provide you with answers, tips, and more.

  • producer

    Course Producers

    Our alumni love their Course Producers, who keep them motivated throughout the course. You can reach out to yours for support anytime.

Embrace The Details

Unit 1: The Basics

Introduction to Data Exploration

  • Describe the data mining workflow and the key traits of a successful data scientist.
  • Extract, format, and preprocess data using UNIX command-line tools.
  • Explore & visualize data.

Introduction to Machine Learning

  • Explain the concepts and applications of supervised & unsupervised learning techniques.
  • Describe categorical and continuous feature spaces, including examples and techniques for each.
  • Discuss the purpose of machine learning and the interpretation of predictive modeling results.

Unit 2: Fundamental Modeling Techniques

K-Nearest Neighbors Classification

  • Describe the setting and goal of a classification task.
  • Minimize prediction error using training & test sets, optimize predictive performance using cross-validation.
  • Understand the kNN classification algorithm, its intuition and implementation.
  • Implement the "hello world" of machine learning (kNN classification of iris dataset).

Naive Bayes Classification

  • Outline the basic principles of probability, including conditional probability and Bayes’ theorem.
  • Describe inference in the Bayesian setting, including the prior and posterior distributions and the likelihood function.
  • Understand the naive Bayes classifier and its assumptions.
  • Implement a spam filter using the naive Bayes technique.

Regression & Regularization

  • Explain the concepts of regression models, including their assumptions and applications.
  • Discuss the motivation for regularization techniques and their use.
  • Implement a regularized fit.

Logistic Regression

  • Describe the applications of logistic regression to classification problems and probability estimation.
  • Introduce the concepts underlying logistic regression, including its relation to other regression models.
  • Predict the probability of a user action on a website using logistic regression.

K-Means Clustering

  • Explain the purpose of exploratory data analysis, its applications in continuous and categorical feature spaces, and the interpretation and use of clustering results.
  • Discuss the importance of the distance function in cluster formation, as well as the importance of scale normalization.
  • Implement a k-means clustering algorithm.

Unit 3: Further Modeling Techniques

Ensemble Techniques

  • Describe general ensemble techniques such as bagging and boosting.
  • Build an enhanced classification algorithm using AdaBoost.

Decision Trees & Random Forests

  • Describe the use and construction of decision trees for classification tasks.
  • Create a random forest model for ensemble classification.

Dimensionality Reduction

  • Explain the practical and conceptual difficulties in working with very high-dimensional data.
  • Understand the application and use of dimensionality reduction techniques.
  • Draw inferences from high-dimensional datasets using principal components analysis.

Recommendation Systems

  • Explain the use of recommendation systems, and discuss several familiar examples.
  • Understand the underlying concepts, including collaborative & content-based filtering.
  • Implement a recommendation system.

Unit 4: Other Tools

Database Technologies

  • Introduce concepts and use of relational databases, alternative database technologies such as NoSQL, and popular examples of each.

Network Analysis

  • Describe the use of graphs and graph theory to analyze problems in network analysis.
  • Explore network visualization.


  • Describe the concepts of parallel computing and applications to problems in big data.
  • Introduce the map-reduce framework.
  • Implement and explore examples of map-reduce tasks.

Request a detailed syllabus

Get Syllabus

My team at Amazon couldn't have built its recommendation system without the foundational data mining and machine learning skills taught in this course. When contributing to the curriculum, I was careful to balance the theory with the real-world challenges of applying it to big data.

Frank Kane / Former Senior Manager,

Frank Kane,

Meet your instructors

Learn from skilled instructors with professional experience in the field.

Rob Hall

San Francisco

Rob Hall

Head of Product Management,


Arun Ahuja

New York City

Arun Ahuja

Data Scientist,

Mount Sinai

Kevin Markham

Washington D.C.

Kevin Markham



Francesco Mosconi

San Francisco

Francesco Mosconi

Data Scientist,

Catalit LLC

Sinan Ozdemir

San Francisco

Sinan Ozdemir


Legion Analytics

Mart van de Ven

Hong Kong

Mart van de Ven

Data Architect, Technologist,

Learn In

Nov 30 – Feb 22

Except: Dec 23

Mon & Wed

6:30pm - 9:30pm

$4,000 USD

Jan 12 – Mar 31

Tue & Thu

6:30pm - 9:30pm

$4,000 USD

Mar 21 – Jun 6

Mon & Wed

6:30pm - 9:30pm

$4,000 USD

Join an Info Session

See if this program is a fit for you. Meet the GA team and speak to an instructor, get an overview of the program curriculum, and learn the benefits of being a student at GA.

Data Science Info Session

Data Science Info Session

GA Washington D.C. (1776 8th Floor), 1133 15th Street NW, 8th Floor, Washington, DC 20005, USA

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Financing Options

Need payment assistance? Our financing options allow you to focus on your goals instead of the barriers that keep you from reaching them.

Let us figure out the best option for you.

¹Must be a US citizen; approval pending state of residency.
⁵Must be a US citizen; approval pending state of residency.

Financing options differ in each market. Contact a local admissions officer for more info.

Get Answers

We love questions, almost as much as we love providing answers. Here are a few samplings of what we’re typically asked, along with our responses:

  • Why is this course relevant today?

    Given the prevalence of technologies and the amount of data available in the online world about users, products, and the content that we generate, businesses can be making so much more well-informed decisions if this vast amount of data was more deeply analyzed through the use of data science. The data science course provides the tools, methods, and practical experience to enable you to make accurate predictions about data, which ultimately leads to better decision-making in business, and the use of smarter technology (think recommendation systems or targeted ads).

  • What practical skill sets can I expect to have upon completion of the course?

    This course will provide you with technical skills in machine learning, algorithms, and data modeling which will allow you to make accurate predictions about your data. You will be creating your models using Python so you will gain a good grasp of this programming language. Furthermore, you will learn how to parse and clean your data which can take up to 70% of your time as a data scientist.

  • Who will I be sitting next to in this course?

    Individuals who have a strong interest in manipulating large data sets, finding patterns in data, and making predictions.

    Software developers who want to solve problems that involve large data sets, such as predicting user behavior on their website, making decisions, or the best way to classify content.

    Individuals with a good grasp of programming, a solid knowledge of statistics and probability but missing the intersection of them both.

  • Are there any prerequisites?
    • A good grasp of college-level statistics and probability.
    • Ability to program in a scripting language such as Python or R.

Dig Deeper Into The Curriculum

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