Data Tech

The analytic landscape is changing faster than ever, and companies who don’t embrace that change are quickly being left by the roadside. Join data evangelist Doug Bordonaro to learn how world-class companies are thinking about data, how you can get started, and why in this new analytic world it’s more important to think about people than features.

A major part of persuading with data is how you present it. For many data scientists and related professionals, the heavy lifting of data crunching is done in Python, R, Julia, Scala, etc. But making interactive visualizations of data and serving up the results in an app yourself means working with JavaScript. If you are like the presenter, starting a second side-career as a front-end developer is not a realistic option. Fortunately, it has gotten easier in the past few years to get the benefits of interactive plotting without having to become a JavaScript expert (and without paying for an expensive licensed product). I will walk through several examples of how to take data from Python code and serve it up for free as interactive plots in easily deployable apps. I will provide examples on github for trying out afterwards.

In the past, 75% of data science dealt with the "janitorial" work of getting clean well-connected data. Enterprise Knowledge Graphs are changing this. Now data scientists can get direct access to high-quality connected data sets that have been validated and are continuously groomed by machine learning agents.

AutoML can automate data preparation, feature extraction, model selection, and model tuning. This can save a Data Scientist loads of time. So instead of hiring four Data Scientists, you may only need two, right?

It’s no secret the shortage of data science talent to help companies produce advanced analytics from their stockpiles of data. There is also a plethora of vendor tools available making promises of turning an analyst into the next great data scientist (which BTW, is possible).

From the depths of the hardcore mathematicians, statisticians and computer scientists (who created this stuff in the first place), have created more advanced tools automate the model creation process to help Data Scientists become more efficient, and (hopefully) better at our jobs.

I will demo AutoML, discuss some pros/cons, and what it can do for you.

The pace of technology is changing, and the next wave will be bigger than ever. The presentation gives a preview of what's to come in the next 5-10 years.

Incorporating consumer feedback into business processes is essential when prioritizing future work and direction for many companies. While some information from consumers comes in the form of standardized question/answer which can be easily curated for analysis, a large amount of the information exists solely in free text fields. Application of NLP techniques to free text processing and classification can reduce human bias and improve scalability by minimizing manual efforts. Topic modeling or clustering text can help to understand broad themes. Scoring algorithms can assist to find text responses that are similar to one another. Supervised machine learning is useful to assign documents to predetermined classifications. We will provide implementation examples of NLP techniques to drive insight from consumer data such as Net Promotor Score (NPS) Survey and other customer feedback.

We all know the power of data science and predictive modeling to help businesses make strategic and informed decisions. However, so many times, the focus begins and ends on the data and the models we fit. At Sprocket, we take an innovative approach to data science, infusing a human-centered collaboration process at the bookends of every project. In this session, we'll walk you through how we approach data science and how you too can improve the predictive capability and implementational success of your data science projects.

Whether you are a Chief Data/Analytics Officer, data scientist, data analyst, or data engineer you are doing a lot of work to provide value from data in your organization. Yet your business partners still aren't making decisions off your data and analytics assets. Your organization's maturity as a data culture could be holding you back from getting the ROI on your hard work.

As a data person you can help close this gap. Reducing your partner's data literacy gap will help you spend less time doing basic reporting and answering basic questions. Increasing the whole organization's emphasis on data, and making data-informed decisions.

In this session we will show you what data literacy is and how your business partners can become data literate. We will also explore ways to foster a business-side community to help encourage data literacy.