Welcome to Code Free Data Science, a class offered with the support of KNIME. Hi, I'm Natasha Baloch and I work here at the Qualcomm Institute located on the campus of the University of California San Diego. Here at QI, I'm a Director of the Interdisciplinary Center for Data Science and I teach across campus from QI to Rady School of Business at UCSD. I'm also the President of the Data Inside Discovery Consultancy specializing in utilizing predictive analytics and big data technologies to enable businesses to discover actionable insights from vast amounts of data. I hope to bring my expertise and experience from both of these rolled into this class. I have received my Masters and PhD in Computer Science from Vanderbilt University with an emphasis of machine learning from large datasets. My dissertation focused on creating and applying novel data mining techniques to mobile robots and real time sensor data. So you can say, I worked on problems in big data and IoT before it was ever called that. I have been with UCSD since 2003, and I have also led and participated in multiple collaborations across a wide range of organizations and industry, government, and academia. Over the past five years, my colleagues here at QI and I have developed and offered numerous training for students and professionals interested in learning how to leverage the power of big data and data science in order to solve the problems they encounter in their jobs. Now, as much as we know, many of you might like to come and visit us in San Diego, which we would love, we know that it is not an option for everyone. As Courserians, we know you'll live everywhere around the world, and that you may already be employed full-time and busy with your work and your life. So we're really excited to be able to take our show on the road as it were, and bring the expertise and experience in data science and big data analytics from here in San Diego to you, wherever you might be. Just to give you a quick idea of what you will learn in this class, we will have four modules that will follow on from this short introduction. We will start with introduction to big data which is just designed to set the stage and help you understand the need society has for big data technology. We will explore basic concepts in data science and the incredible need for more trained data scientists. After this module, we will jump right into the technical platform we will be working with, the KNIME Analytics Platform. Here in this module, you will have an opportunity to install and set up KNIME on your own machine, and prepare some basic workflows and see what analyses KNIME enables us to do. Now, it is important to know, you do not need to be a software engineer or someone with program that's doing programming every day in order to succeed in this class. As a matter of fact, you don't need any programming experience at all whatsoever. There are no required prerequisites for this class. This is the beauty of using an analytics platform like KNIME. We can focus on learning data science concepts and methods and performing those in practical hands-on experiences without any programming or debugging distractions. In the third data analytics module, we will demonstrate how to manipulate data and apply basic data analytics by utilizing KNIME's manipulation, functionalities, and nodes. We will use KNIME to create your first analytics data workflow. This is exciting. I think you'll really enjoy this. In our last module, building predictive models module, you will learn about several core machine learning methods, and we'll have an opportunity to apply these machine learning algorithms and techniques through a KNIME workflow, again, without any programming requirements, and at the end, we have a really exciting project. At the end of the fourth module, we have a project plan that we're offering in collaboration with KNIME that will kick off your data science experience portfolio. As you can probably tell by now, I take data science very seriously and I hope you will too. However, that doesn't mean that we shouldn't have fun learning. In that spirit, we will start each module with a funny, maybe a little geeky data joke. So what are we waiting for? Let's get going.
