Part-Time Data Course

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Math and programming skills

Skills & Tools

Use Python to mine datasets and predict patterns.

Data manipulation tools

Production Standard

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

Learn to make predictions with modeling

The Big Picture

Master the basics of machine learning and harness the power of data 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 kept them motivated throughout the course. You can reach out to yours for support anytime.

See What You’ll Learn

Unit 1: Research Design and Exploratory Data Analysis

What is Data Science

  • Describe course syllabus and establish the classroom environment
  • Answer the questions: "What is Data Science? What roles exist in Data Science?"
  • Define the workflow, tools and approaches data scientists use to analyze data

Research Design and Pandas

  • Define a problem and identify appropriate data sets using the data science workflow
  • Walkthrough the data science workflow using a case study in the Pandas library
  • Import, format and clean data using the Pandas Library

Statistics Fundamental I

  • Use NumPy and Pandas libraries to analyze datasets using basic summary statistics: mean, median, mode, max, min, quartile, inter-quartile, range, variance, standard deviation and correlation
  • Create data visualization – scatter plots, scatter matrix, line graph, box blots, and histograms – to discern characteristics and trends in a dataset
  • Identify a normal distribution within a dataset using summary statistics and visualization

Statistics Fundamental II

  • Explain the difference between causation vs. correlation
  • Test a hypothesis within a sample case study
  • Validate your findings using statistical analysis (p-values, confidence intervals)

Instructor Choice

  • Focus on a topic selected by the instructor/class in order to provide deeper insight into exploratory data analysis

Unit 2: Foundations of Data Modeling

Introduction to Regression

  • Define data modeling and linear regression
  • Differentiate between categorical and continuous variables
  • Build a linear regression model using a dataset that meets the linearity assumption using the scikit-learn library

Evaluating Model Fit

  • Define regularization, bias, and errors metrics;
  • Evaluate model fit by using loss functions including mean absolute error, mean squared error, root mean squared error
  • Select regression methods based on fit and complexity

Introduction to Classification

  • Define a classification model
  • Build a K–Nearest Neighbors using the scikit–learn library
  • Evaluate and tune model by using metrics such as classification accuracy ⁄ error

Introduction to Logistic Regression

  • Build a Logistic regression classification model using the scikit learn library
  • Describe the sigmoid function, odds, and odds ratios and how they relate to logistic regression
  • Evaluate a model using metrics such as classification accuracy ⁄ error, confusion matrix, ROC ⁄ AOC curves, and loss functions

Communicate Results from Logistic Regression

  • Explain the tradeoff between the precision and recall of a model and articulate the cost of false positives vs. false negatives.
  • Identify the components of a concise, convincing report and how they relate to specific audiences ⁄ stakeholders
  • Describe the difference between visualization for presentations vs. exploratory data analysis

Flexible Class Session

  • Focus on a topic selected by the instructor ⁄ class in order to provide deeper insight into data modeling

Unit 3: Data Science in the Real World

Decision Trees and Random Forest

  • Describe the difference between classification and regression trees and how to interpret these models
  • Explain and communicate the tradeoffs of decision trees vs regression models
  • Build decision trees and random forests using the scikit-learn library

Natural Language Processing

  • Demonstrate how to tokenize natural language text using NLTK
  • Categorize and tag unstructured text data
  • Explain how to build a text classification model using NLTK

Dimensionality Reduction

  • Explain how to perform a dimensional reduction using topic models
  • Demonstrate how to refine data using latent dirichlet allocation (LDA)
  • Extract information from a sample text dataset

Working with Time Series Data

  • Explain why time series data is different than other data and how to account for it
  • Create rolling means and plot time series data using the Pandas library
  • Perform autocorrelation on time series data

Creating Models with Time Series Data

  • Decompose time series data into trend and residual components
  • Validate and cross-validate data from different data sets
  • Use the ARIMA model to forecast and detect trends in time series data

The Value of Databases

  • Describe the use cases for different types of databases
  • Explain differences between relational databases and document-based databases
  • Write simple select queries to pull data from a database and use within Pandas

Moving Forward with your Data Science Career

  • Specify common models used within different industries
  • Identify the use cases for common models
  • Discuss next steps and additional resources for data science learning

Flexible Class Session

  • Focus on a topic selected by the instructor⁄class in order to provide deeper insight into data science in the real world

Final Presentations

  • Present final presentation to peers, instructor, and guest panelists who will identify strengths and areas for improvement

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.

Ivan Corneillet

San Francisco

Ivan Corneillet

Technology and Startup Advisor,


Arun Ahuja

New York City

Arun Ahuja

Data Scientist,

Mount Sinai

Seth Weidman


Seth Weidman

Data Scientist,

Trunk Club

Armen Donigian

Los Angeles

Armen Donigian

Lead Data Engineer,


Sinan Ozdemir

San Francisco

Sinan Ozdemir


Mart van de Ven

Hong Kong

Mart van de Ven

Partner / Principal Data Scientist,


Learn In


Aug 6 – Oct 15

Except: Sep 3

Mon & Wed

4pm - 7pm

$3,950 USD

Aug 13 – Oct 17

Except: Sep 3

Mon & Wed

6:30pm - 9:30pm

$3,950 USD


Sep 10 – Nov 19

Except: Nov 12

Mon & Wed

5pm - 8pm

$3,950 USD


Oct 23 – Jan 1

Except: Nov 22, Dec 25, Dec 27, Jan 1

Tue & Thu

4pm - 7pm

$3,950 USD


Dec 3 – Feb 25

Except: Dec 24, Dec 26, Dec 31, Jan 21, Feb 18

Mon & Wed

4pm - 7pm

$3,950 USD

Visit Campus

See if this program is a fit for you. Meet the GA team, get an overview of the program curriculum, and chat with other students thinking about the course.

Data science info session

Data Science Info Session

GA Seattle, 1218 Third Avenue, 3rd Floor, Seattle, WA 98101, United States

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We look forward to meeting you. In the meantime, our admissions team will reach out soon to discuss our courses and your goals.

Wednesday, 1 August 5:30pm
GA Seattle, 1218 Third Avenue, 3rd Floor, Seattle, WA 98101, United States
data science student working

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.

¹ Must be a US citizen or permanent resident; approval pending state of residency.

Financing options differ in each market and are only available to students accepted into our programs. 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.

  • Whom 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.

  • Are there any prerequisites?

    A basic understanding of statistics

    A basic understanding of variables, functions, and lists in Python

Dig Deeper Into The Curriculum

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