Due to recent advances in technology, humanity is collecting vast amounts of data at an unprecedented rate, making the skills necessary to mine insights from this data increasingly valuable. So what does it take for a Developer to enter the world of data science?

Join me on a journey into the world of big data and machine learning where we will explore what the work actually looks like, identify which skills are most important, and design a roadmap for how you too can join this exciting and profitable industry.

Data Science is the practice of transforming data into actionable insight. This set of skills is currently in high demand and commanding significant increases in salary, as data science is fundamentally changing the world around us. However, most developers have not yet learned this valuable set of skills.

In this session, you will learn what data science is and why it’s important. In addition, you’ll learn what you need to know, as a developer, to prepare for our new data-driven economy. Expect to learn about the Internet of Things (IoT), Big Data, machine learning, and how they are converging to create fully-autonomous intelligent systems.

The rise of machine learning has produced an explosion of APIs to make your applications more intelligent. In this session, you will learn about the 20+ different Cognitive Services APIs that provide object recognition, face detection and identification, emotion recognition, OCR, computer vision, video services, speech and speaker recognition, language understanding, text analytics, sentiment analysis, knowledge exploration, search services, and more. You can also leverage these services in conjunction with the Microsoft Bot Framework to build an intelligent assistant. You will see powerful demos of these capabilities, experience the simplicity of calling this code, and walk away with ideas on how to leverage this functionality in your own applications.

Using an EPOC headset from Emotiv, I have captured 14 channels of EEG (brain waves) while subjects lied and answered truthfully to a series of questions. I fed this labelled dataset into Azure Machine Learning to build a classifier which predicts whether a subject is telling the truth or lying. In this session, I will share my results on this “lie detector” experiment. I will show my machine learning model, data cleaning process, and results, along with discussing the limitations of my approach and next steps/resources. Attendees will gain exposure to the Emotiv EPOC headset and Azure Machine Learning.

Interested in doing machine learning in the cloud? In this demo-heavy talk, I will set the stage with some information on the different types of machine learning (clustering, classification, regression, and anomaly detection) supported by Azure Machine Learning and when to use each. Then, for the majority of the session, I’ll demonstrate using Azure Machine Learning to build a model which predicts survival of individuals on the Titanic (one of the challenges on the Kaggle website). I'll talk through how I analyze the given data and why I choose to drop or modify certain data, so you will see the entire process from data import to data cleaning to building, training, testing, and deploying a model. You’ll leave with practical knowledge on how to get started and build your own predictive models using Azure Machine Learning.