Description

In the past 10 years we have seen the explosion of big data in industry. It is one of the hottest buzzwords in any sector. All companies claim to be data-driven or data-centric, but what does that mean?

This course focuses on the professional who has never worked with data and is now interacting with an analytics team in their company. There are no prerequisites to enroll in this 101 Data Science class. We will go through the philosophy of what your analysts are trying to do and how to effectively interact with them to get the results you want.

After finishing this course you will not only have a better understanding of Data Science, but you will have done some coding exercises to create your very own machine learning project in the powerful MatrixDS platform, design specifically for getting new users up and running faster.

What you will Learn

1. Data Science background

• What is it and why should I care

• What can we do with it?

• Types of problems that we can solve

• The Data Science process

• Our class project

2. Munging

• Ingest Data

• Visualization why it is so important

• How do I collaborate in this stage with my analyst?

• My first lines of code

3. Modeling

• Different types of models (does everything work everywhere?)

• Deterministic vs Stochastic (What do those even mean)

• Choosing a model

• Another couple of lines of code

4. Present

• Results Visualization

• Creating a dashboard

• Static vs Dynamic visualizations

5. All the buzzwords explained

• Machine Learning

• AI

• Computer Vision

• Big Data

• Neural Networks

• Deep learning

Prerequisites

This course is designed to get you up and running and even writing a bit of code in a weekend. All you need is a desire to learn.

Setup

Bring your computer (any kind), a modern browser (preferably chrome) and a power chord. No need to install anything. Our MatrixDS platform gets you up and running in no time without any installations.

Instructors

Isaac Faber:

PhD Candidate at Stanford University in the Decision and Risk Analysis group, his research focus is a machine learning approach to early warning systems in cyber security. Before coming to Stanford, he was the lead Data Scientist at Army Cyber Command and worked closely with the NSA. He was the lead architect of the largest operational Big Data System in use in the Department of Defense. Isaac was also an assistant professor at the United States Military Academy at West Point in the Systems Engineering department

Alejandro Martinez:

PhD Candidate at Stanford University bridging the gap between Operations Management and Decision Analysis, his research focus is how to operationalize decision making in industry. Before coming to Stanford, he was an Operations consultant and an Operations Manager for several manufacturing businesses in his native Venezuela. He has lead large teams in delivering results in operations rich fields such as food and heavy metal manufacturing.

Doug Gibbons:

20 years of experience in building technical products that stand out. In his native UK, he led teams that broke new ground in software production, saving organizations over $100M by reducing time-to-market and improving their operating efficiency. Moving to infrastructure engineering, he helped build the largest domestic IoT system in the country, and creating a virtualization strategy for the largest pay-TV broadcaster in Europe. He is also a contributor to the Kubernetes infrastructure orchestration platform used by many of the world’s largest corporations