Data
Science

Part-Time Data Course

Talk to Admissions +1 (917) 722-0237
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

    Instructors


    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: Programming Basics

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

Your Development Environment

  • Navigate through directories using the command line
  • Use git and GitHub to share repositories

Python Foundations

  • Conduct arithmetic and string operations in Python
  • Assign variables
  • Implement loops and conditional statements
  • Use Python to clean and edit datasets

Unit 2: Research Design and Exploratory Data Analysis

Exploratory Data Analysis

  • Use DataFrames and Series to read data
  • Rename, remove, combine, select, and join data
  • Identify and handle null and missing values

Experiments and Hypothesis Testing

  • Determine causality and sampling bias
  • Test a hypothesis using a sample case study
  • Validate your findings using statistical analysis (p-values, confidence intervals)

Data Visualization in Python

  • Define key principles of data visualization
  • Create line plots, bar plots, histograms and box plots using Seaborn and Matplotlib

Statistics in Python

  • Use NumPy and Pandas libraries to analyze datasets using basic summary statistics
  • Create data visualization – scatter plots, scatter matrix, line graph, box plots, and histograms – to discern characteristics and trends in a dataset
  • Identify a normal distribution within a dataset using summary statistics and visualization

Unit 3: Foundations of Data Modeling

Linear 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

KNN and 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

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

Unit 4: Machine Learning

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

Working with API Data

  • Access public APIs and get information back
  • Read and write data in JSON
  • Use the requests 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

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

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, Amazon.com

Frank Kane, Amazon.com

Meet your instructors

Learn from skilled instructors with professional experience in the field.

Ivan Corneillet

San Francisco

Ivan Corneillet

Technology and Startup Advisor,

Self-Employed

Arun Ahuja

New York City

Arun Ahuja

Data Scientist,

Mount Sinai

Seth Weidman

Chicago

Seth Weidman

Data Scientist,

Trunk Club

Armen Donigian

Los Angeles

Armen Donigian

Lead Data Engineer,

ZestFinance

Sinan Ozdemir

San Francisco

Sinan Ozdemir

Founder,

Kylie.ai

Mart van de Ven

Hong Kong

Mart van de Ven

Partner / Principal Data Scientist,

Droste

Learn In

Set as default location

Oct 15 – Jan 7

Except: Nov 12, Nov 21, Dec 24, Dec 26, Dec 31

Mon & Wed


6:30pm - 9:30pm


$3,950 USD

ONLINE

Oct 23 – Jan 10

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

Tue & Thu


7pm - 10pm


$3,950 USD

Nov 13 – Jan 31

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

Tue & Thu


6:30pm - 9:30pm


$3,950 USD

ONLINE

Nov 13 – Jan 31

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

Tue & Thu


9:30pm - 12:30am


$3,950 USD

ONLINE

Dec 3 – Feb 25

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

Mon & Wed


7pm - 10pm


$3,950 USD

Dec 10 – Mar 4

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

Mon & Wed


6:30pm - 9:30pm


$3,950 USD

Explore New Opportunities

Find out if this course is right for you and your goals. Chat with the GA team, discover the curriculum details, and get a glimpse into student life in an upcoming info session.

Data science info session

Data Science Info Session

GA NYC (Manhattan), Classrooms, 10 East 21st Street, New York, NY 10010, United States

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Thanks!

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

25
Thursday, 25 October 6:30pm
GA NYC (Manhattan), Classrooms, 10 East 21st Street, New York, NY 10010, 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 courses. Contact a local admissions officer for more info.

Get Answers

Have questions? We’ve got the answers. Get the details on how you can grow in this course.

  • 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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