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Python is my core tool for prototypes, analysis, and automation. Strong Python makes every other skill easier, from data pipelines to machine learning experiments end-to-end.

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This credential signals I can build and deploy ML on AWS. It strengthens credibility for ML roles and forces learning beyond what classes cover intentionally.

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Organizations are increasingly turning to deep learning because it allows computers to learn independently and undertake tasks with little supervision, promising extraordinary benefits for both science and industry. Unlike traditional machine learning, deep learning attempts to simulate the way our brains learn and process information by creating artificial "neural networks" that can extract complicated concepts and relationships from data. Deep learning models improve through complex pattern recognition in pictures, text, sounds, and other data to produce more accurate insights and predictions.

Using cloud computing for deep learning allows large datasets to be easily ingested and managed to train algorithms, and it allows deep learning models to scale efficiently and at lower costs using GPU processing power. By leveraging distributed networks, deep learning on the cloud allows you to design, develop and train deep learning applications faster.

Deep learning algorithms are designed to learn quickly. By using clusters of GPUs and CPUs to perform complex matrix operations on compute-intensive tasks, users can speed up the training of deep learning models. These models can then be deployed to process large amounts of data and produce increasingly relevant results.

Deep learning neural networks are ideally suited to take advantage of multiple processors, distributing workloads seamlessly and efficiently across different processor types and quantities. With the wide range of on-demand resources available through the cloud, you can deploy virtually unlimited resources to tackle deep learning models of any size.

Deep learning frameworks such as Apache MXNet, TensorFlow, the Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch and Keras can be run on the cloud, allowing you to use packaged libraries of deep learning algorithms best suited for your use case, whether it's for web, mobile or connected devices.

Deep learning is well suited for a wide variety of artificial intelligence use cases, such as:

You can get started with a fully-managed experience using Amazon SageMaker, the AWS platform to quickly and easily build, train, and deploy machine learning models at scale. You can also use the AWS Deep Learning AMIs to build custom environments and workflows for machine learning.

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Organizations are increasingly turning to deep learning because it allows computers to learn independently and undertake tasks with little supervision, promising extraordinary benefits for both science and industry. Unlike traditional machine learning, deep learning attempts to simulate the way our brains learn and process information by creating artificial "neural networks" that can extract complicated concepts and relationships from data.

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I’m drawn to applied AI in high-stakes environments like aerospace and defense. This industry pushes reliability, rigorous testing, and systems thinking far beyond classroom projects.

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Technology is transforming how humans and machines work together. That’s why we invest in the development of optionally-manned and unmanned systems that serve as a capability multiplier.

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Most company data lives in databases. Strong SQL lets me validate assumptions, find patterns fast, and build reliable inputs for dashboards, models, and AI tools.
Media: your SQL widget (keep).

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Building a neural network from scratch forces me to understand fundamentals, not just libraries. It strengthens debugging, intuition, and confidence with modern deep learning tools.

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"We can only see a short distance ahead.” This reminds me to iterate, test, and learn quickly, because progress comes from experiments, not perfect plans

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Vitalik Buterin inspires systems-level thinking and long-term curiosity. His work shows how technical design choices shape incentives, governance, and trust in open online communities globally.

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A strong master’s program deepens my ML and systems foundation. It signals commitment, expands my network, and opens doors to advanced roles long-term, for leadership.

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Leverage the latest tools and analytical methods to work with data at scale, derive insights from complex and unstructured data, and solve real-world problems.

Complete in 12-20 Months - Prepare to be a data science leader well-versed in data engineering, machine learning, statistical analysis, and more.

Graduate from a No.2-Ranked Program -The online MIDS program from the UC Berkeley School of Information (I School) is nationally recognized by Fortune magazine.1

Gain In-Demand Skills - Careers in data science are on the rise2. Our graduates are applying their skills at Amazon, Apple, Facebook, Microsoft, Google, and other top companies.

Network

Access to a Vibrant, Worldwide Network

Connections

Lifelong Connections in the Bay Area

Faculty

Learn from Leading Industry Practitioners

The UC Berkeley School of Information (I School) MIDS program prepares data science professionals to be leaders in the field. MIDS students benefit from a multidisciplinary curriculum, experienced faculty from top data-driven companies, and an accomplished network of peers. Delivered online, the WASC-accredited program brings UC Berkeley to students across the country and around the world.

Develop the skills to become a data science leader.

Build valuable relationships with faculty.

No GRE scores required.

The 27-unit, online program is designed for the working professional's schedule and can be completed on one of three paths: accelerated, standard, or decelerated. The curriculum is delivered through insightful asynchronous course work and live, face-to-face classes.

Learn More Today. Bring About Real-World Data Solutions Tomorrow.

¹ Best Online Master's in Data Science Programs, Fortune, Ranked in 2022.arrow_upward

² Computer and Information Research Scientists, Bureau of Labor Statistics. Accessed February 2022.arrow_upwardReturn to footnote reference

REQUEST INFO

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UC Berkeley's Master of Information and Data Science | Online

# data[].attributes.attachment_link.description
Leverage the latest tools and analytical methods to work with data at scale, derive insights from complex and unstructured data, and solve real-world problems. Complete in 12-20 Months - Prepare to be a data science leader well-versed in data engineering, machine learning, statistical analysis, and more.