The predictive power of data science is limitless. Here in Austin, Dell is leveraging machine learning and artificial intelligence in its state-of-the-art cybersecurity suite. Enroll in GA’s data science courses in Austin, and start applying data to advance your own projects and career.

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Big Data

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

Python is a high-level, object-oriented programming language used to collect data from multiple sources, run statistical and machine learning models, and create visualizations 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.

Spark

Joseph Kambourakis, a Data Science instructor at General Assembly 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 Joseph’s “Beginner’s Guide to Spark" here.

SQL

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 Michael's “Beginner’s Guide to SQL" here.

Break into Austin's 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.