Everyone once in a long while I break away for a day to share my analytics knowledge with peers. In a few weeks, I’ll be teaching an introduction to data science class in Atlanta, Georgia. Unlike typical courses that follow rigid step-by-step exercises with a specific tool or programming language, I have structured my class to be flexible, fast-paced, and fun. Best of all, you can bring your own data. While learning the art of advanced analytics, you might just build a useful model to take back to the office.


Date: June 21, 2017
Location: Georgia Tech Global Learning Center, 84 5th St NW, Atlanta, Georgia.
Registration: www.PASSBAday.com
Outline: Data Science Class Outline

What to Expect

To make sure you get the most value out of our limited time together, I have collected a plethora of my favorite data science resources, “hands-on” lab exercises, data set examples and cheat sheets for you to continue learning. Successful analysts are usually curious creatures. That is a blessing and curse. Don’t worry if you struggle to pay attention in a full-day class. I’ll be providing guided direction and then allowing you to play, experiment with data, and delve into provided content that you find most interesting.

During data science day, I’ll be covering a range of popular open source tools, R, Python, Weka and even Excel. If you want to bring your own predictive analytics tool, that is fine with me. The concepts that we will learn are truly universal.

We’ll begin with an overview of advanced analytics techniques and machine learning algorithms including but not limited to forecasting, regression, classification, clustering, association, neural networks, deep learning, optimization and simulation. Then we will run through a quick refresh of statistical concepts that are the foundation behind many algorithms. We’ll also share tips on selecting appropriate algorithms.

Throughout the day we will play with data in “hands-on” exercises using provided sample data sets or your own data. We’ll analyze with descriptive analytics, prepare and shape data samples, perform feature engineering and run machine learning algorithms. After evaluating model results, we’ll iteratively continue experimenting with different feature combinations, model settings, parameters, and model types. To put what we learn from each section all together, we’ll work on a case study. To wrap up the day, we’ll review prescriptive analytics – specifically optimization and simulation.

If you want to quickly understand the data science lifecycle, how algorithms work, where they can be applied, and where to go to learn more later at a less rapid pace, my Data Science Day class might be a perfect way to dive on in, pick my brain or just get away from the office to network with other data enthusiasts.

To sign up, please visit PASS’ Business Analytics Day  website. To nominate yourself or a peer for the one free data science registration mentioned in my Secrets to Staying Relevant article, please contact me and share why you are recommending the candidate. This is a unique learning opportunity to learn the hottest advanced analytics topics in the industry right now in a manner where you can use this knowledge immediately.