Give yourself the power to drive businesses forward. When you learn data science you can make powerful predictions through analytics, machine learning, visualization, and more. Excel in this in-demand field by taking a data science bootcamp or full-time data science course at GA.


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Data Science

Full-Time Immersive Courses

Challenge yourself and change your career with a 10- to 13-week immersive learning experience.

Part-Time Courses

Enhance your professional potential. Learn in-demand skills in evening, weekend, or 1-week accelerated courses.

Learn In-Demand Skills in Our Data Science Courses and Bootcamps

Big Data

The term “big data” refers to the enormous amount of data we generate each day — from social media, to online purchases, and beyond. Get an intro to big data and its ecosystem of tools and technologies, and discover the potential and power of massive data sets.

Deep Learning

Deep learning — a subfield within machine learning — focuses on developing layers of artificial networks that mimic the brain’s activity. These groundbreaking AI developments power speech and image recognition technologies and strengthen the connection between technology and human intuition.

Machine Learning

Siri, self-driving cars, and Netflix’s on-point recommendations all depend on machine learning. Dedicated to creating algorithms and systems that learn from data, machine learning allows platforms to make informed predictions that get sharper over time.


Python is a high-level, object-oriented programming language used to collect data from multiple sources, run statistical and machine learning models, and create visualisations of those insights. Because of its ease of use and readability, Python is both a great tool for beginners and a go-to for data experts.


Joseph Kambourakis, a Data Science instructor at GA Boston, says, “Apache Spark is an open-source framework used for large-scale data processing. Apache Spark is important to the big data field because it represents the next generation of big data processing engines. The framework is made up of many components, including four programming APIs and four major libraries. By providing a flexible language platform and having concise syntax, the data scientist can write more programs, iterate through their programs, and have them run much quicker. Since Spark’s release in 2014, it has become one of Apache’s fastest growing and most widely used projects of all time.

“At GA, we teach both the concepts and the tools of data science. Because hiring managers from marketing, technology, and biotech companies, as well as guest speakers like company founders and entrepreneurs, regularly talk about using Spark, we’ve incorporated it into the curriculum to ensure students are fluent in the field’s most relevant skills. Spark is a great tool to teach because the general curriculum focuses mostly on Python, and Spark has a Python API/library called PySpark. When we teach Spark in our Data Science Immersive, we cover resilient distributed data sets, directed acyclic graphs, closures, lazy execution, and reading JavaScript Object Notation (JSON), a common big data file format."

Read “A Beginner’s Guide to Spark" by Joseph Kambourakis.


Michael Larner, a Data Analytics instructor at General Assembly Los Angeles, says, "Put simply, SQL is the language of data — it’s a programming language that enables us to efficiently create, alter, request, and aggregate data from those mysterious things called databases. It gives us the ability to make connections between different pieces of information, even when we’re dealing with huge data sets. Modern applications are able to use SQL to deliver really valuable pieces of information that would otherwise be difficult for humans to keep track of independently. In fact, pretty much every app that stores any sort of information uses a database. This ubiquity means that developers use SQL to log, record, alter, and present data within the application, while analysts use SQL to interrogate that same data set in order to find deeper insights.

"At General Assembly, we know businesses are striving to transform their data from raw facts into actionable insights. To accomplish this, we give students the opportunity to use SQL to explore real-world data such as Firefox usage statistics, Iowa liquor sales, or Zillow’s real estate prices. Our full-time Data Science Immersive and part-time Data Analytics courses help students build the analytical skills needed to turn the results of those queries into clear and effective business recommendations. On a more introductory level, after just a couple of hours of in one of our SQL workshops, students are able to query multiple data sets with millions of rows."

Read “A Beginner’s Guide to SQL" by Michael Larner.

Break into the data science industry.

Harness one of today’s most in-demand skill sets: data science. Learn the essentials in our part-time course, or make a career change with our full-time Immersive.

Related Data Science Blog Posts


How Data Maps Reveal Inequality and Equity in Atlanta

Mapping the communities of tomorrow requires a hard look at the topographies of today. Mike Carnathan, project director at Neighborhood Nexus, synthesizes big data into visual stories that chart the social, political, and economic conditions across the city of Atlanta. Part data miner, part cultural cartographer, Carnathan creates demographic maps that local leaders, advocates, and everyday citizens use to help understand and change their lives.

dc snow storm

The Data Behind a Season Without Snow Days

Because of the excitement surrounding impending snow, it seemed like a given that OPM would issue a closure, as it has in the past. It made me think about whether there was any relationship between the emotional buildup resulting from a snowy-weather forecast and the chances of the OPM actually declaring a snow day.I wondered: Are OPM’s opaque closure decisions truly, entirely based on the forecast, or are they also susceptible to human impulse? To answer this question, I turned to data science.

on demand streams by song 2016

From streaming to stream-ripping, new data signals the death of music sales.

The star who sold more albums than anyone else in 2016 left this mortal plane that April — his purpleness, Prince. The Minneapolis-born star was the sole musician who managed to sell more than 1 million albums in both digital and physical forms (i.e., CDs and vinyl) in 2016, according to just-released data from Nielsen, the global media research company. In the very first day after his death, Prince pushed more than 1 million digital songs and over 200,000 digital albums — amazing numbers in a field where hard sales have long been flagging in the face of free, or low-cost, streaming. Those Prince figures don’t involve a single stream.