Data Exploration & Visualization
Course Dates and Locations
March 28-29, 2014; 9:00am-4:30pm
The Westin Boston Waterfront
425 Summer Street
Boston, MA 02210
June 25-26, 2014; 9:00am-4:30pm
2025 Gateway Pl, Suite 390
San Jose, CA 95110
Participants can expect to be re-introduced to approaching data in a powerful, yet playful manner. They will see and experience how exploration and visualization can be used to answer existing questions, thereby corroborating or invalidating hunches and preconceptions. In addition to directed exploration, participants will experience first-hand the power of data exploration and visualization to reveal unexpected patterns, trends and exceptions as well as stimulate new perspectives and insights.
During the course, commercial software will be used. However, this is not a software training course. Instead, the focus is on understanding the underlying methodology and mindset of how data should be approached, handled, explored, and incorporated back into the domain of interest.
At the end of the course, participants are expected to
- have confidence to explore new data using the exploration/visualization approach;
- be able to approach and deploy interactive visualization;
- understand how to identify practically meaningful discoveries;
- experience the use of state-of-the-art visualization software, and
- think creatively about data and insights.
Day One: Exploring Data in Context
1. The Larger Context of Business Intelligence (BI) and Business Analytics (BA)
- Business analytics: goals and methods
- Data exploration and visualization in the BI/BA context
- Data exploration goals: directed navigation and exploratory navigation
- Case studies: the power of data exploration
2. Why You Need to Know the Data Exploration Process
- Data in context
- Attacking a dataset unarmed
- The challenges and pitfalls of "unarmed" exploration
3. Know Thy Data
- Data sources
- Data types
- Data structures
- Merging datasets
- Samples and sampling bias
- Data dictionaries and meta-data
4. Start Exploring
- Using software: from spreadsheets and beyond (SAS JMP)
- Getting an overview: filtering, sorting, summary statistics, and more
- Detecting and dealing with exceptions: missing values, outliers and extreme values
- Transforming variables and creating new derived variables
Day Two: Visual Analytics
5. Data Visualization
- Using visualization for directed navigation
- Using visualization for exploratory navigation
- Interactive visualization software: overview of packages and introduction to TIBCO Spotfire
- Basic charts and best practices (bar chart, line graph, histogram, box plot scatter plot)
- Specialized and advanced charts (heatmap, treemap, map chart)
- Dashboards and multiple views
- Creating a data story
6. Creating Data Stories
- Organizing information
- Beyond visualization: dimension reduction in a nutshell (clustering and PCA)
7. Evaluating Your Discoveries and Data Stories
- Is the "aha!" really an "aha?"
- Who should care, why they should care
- Implications of discoveries and data stories
8. Getting Started At Your Job
- The right mindset
- Following the exploration path
- Evaluating discoveries and data stories
- Getting colleagues and management on board
- Determining potential data exploration and visualization benefits to your job
- Resources for self-development
Stephen McDaniel is Co-Founder and Principal Data Scientist at Freakalytics, LLC where he advises Chief Data Officers and Chief Information Officers on developing and implementing successful strategies for all aspects of data utilization - from collection to integration to storage to analytics and distribution of findings. He is passionate about projects that bring together people, systems and data resulting in smarter choices, happier customers and higher profits. As an analytics educator with broad experience training thousands of students, he teaches individuals and teams that work with data, regardless of skill level or department, how to manage and analyze data to answer business questions.
Stephen has over 25 years of experience as a statistician, analyst, data architect, instructor, data miner, consultant, software innovator and author. In addition to INFORMS, he is a faculty member at the American Marketing Association (AMA) and The Data Warehouse Institute (TDWI), developing and teaching hands-on courses based on real-world case studies. Many of his courses focus on the emerging techniques of visual analytics combined with predictive analytics. He has directly led and provided vision for data warehousing, business intelligence and advanced analytic teams at Tableau Software, SAS Institute, Brio Technology, Glaxo, PharmaResearch, Paradigm Genetics, Takeda Abbott Pharmaceuticals, Netflix and Loudcloud.
Stephen is lead author of SAS for Dummies™ (two editions), Rapid Graphs with Tableau Software (four editions), and the Rapid Dashboards Reference Card and App and co-author of The Accidental Analyst: Show Your Data Who’s Boss. Working with Eileen McDaniel, PhD, at Freakalytics, he was the co-founder of Tableau’s worldwide training program, providing public and on-site hands-on training in analytics. He has worked with or been an invited instructor at leading organizations around the world including Target, State Farm, Eli Lilly, IMS Health, Boeing, American Express, Oracle, Australian Government—Intellectual Property Australia, Duke University, Fidelity Investments, US Navy CyberDefense Operations Command, and The University of Washington at Seattle. When he isn’t analyzing data, you will likely find him hiking in the mountains or visiting wineries with Eileen.
Stephen’s six-word bio is “so much data, so little time!”
Eileen McDaniel, Ph.D., lead author of The Accidental Analyst: Show Your Data Who’s Boss, is Director of Analytic Communications at Freakalytics, LLC. As co-founder of Freakalytics, she specializes in translating technical information to educate and inform people from all skill levels and backgrounds on how to analyze and present data. She develops educational materials and analytical training courses that empower people to get the most out of their data and take decisive action to solve problems in their daily work. She also focuses on how to effectively communicate analysis results and recommendations, incorporating both the art and the science behind creating simple yet compelling presentations and reports that can engage and inform colleagues, clients and the public.
Eileen has years of experience telling stories with data, beginning as a scientific researcher in biology and ecology. She investigated various topics such as food contamination and how people interact with natural resources. Along the way, she won multiple awards for excellence in research and teaching, including one presented on Capitol Hill from the U.S. Congress. In her research, she designed and implemented multiple studies funded by state and federal sources, collecting and analyzing data from disparate sources to offer innovative approaches to resource management.
Eileen’s scientific work included presenting experimental plans and findings to the general public, funding agencies, resource managers and users, politicians, academics and students. Working with such a diverse audience greatly influenced her approach to simplifying and explaining data analysis. Her research and communication experience evolved into an interest in green or eco-marketing analytics and completion of an MBA Certificate in Marketing Analytics.
Since co-founding Freakalytics in 2009, she consults on projects involving technical writing and communication of analysis results. She also leads the development of books, seminars and presentations. Eileen co-authored Rapid Graphs with Tableau Software (three editions), and the Rapid Dashboards Reference Card and App with Stephen McDaniel. Eileen’s unique expertise in science and business led her to realize that although scientists have a formal, step-by-step method to collect and analyze their data, business analysts lack a similar plan. This realization inspired the framework The Seven C’s of Data Analysis used in The Accidental Analyst, which also explains best practices of the emerging field of visual analytics.
Galit Shmueli is SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics & Information Systems at the Indian School of Business. She is best known for her research and teaching in business analytics, with a focus on statistical and data mining methods for contemporary data and applications in information systems and healthcare.
Dr. Shmueli's research has been published in the statistics, management, information systems, and marketing literature. She authors over seventy journal articles, books, textbooks and book chapters, including the popular textbook Data Mining for Business Intelligence and Practical Time Series Forecasting. Dr. Shmueli is an award-winning teacher and speaker on data analytics.
She has taught at Carnegie Mellon University, University of Maryland, the Israel Institute of Technology, Statistics.com and the Indian School of Business. Her experience spans business and engineering students and professionals, both online and on-ground. Dr. Shmueli teaches courses on data mining, statistics, forecasting, data visualization, and industrial statistics.
More information about Galit can be found at galitshueli.com.
David R. Hardoon is Associate Director of Business Analytics at Ernst & Young Singapore, Advisory Services. He is leading the analytics practice and is responsible for the positioning of business analytics advisory and services to clients across different business sectors. He is also an Adjunct Faculty Member of School of Information Systems and Singapore Management University in Singapore and an Honorary Senior Research Associate at the Centre for Computational Statistics & Machine Learning, University College London in the United Kingdom.
Dr. Hardoon has been engaged at various conferences and workshops to speak on research and business related topics in machine learning, data mining and business analytics. He also regularly tutors, advises and provides consulting support in his field of expertise, Analytics and Business Analytics. He also has established expertise in developing and applying computational analytical models for business knowledge discovery and analysis and has been involved in research projects in the domains of taxonomy, neuroscience, aerospace and finance.
Dr. Hardoon received his doctorate in Computer Science in the field of Machine Learning from the University of Southampton in 2006 and was awarded first class honors in B.Sc. Computer Science and Artificial Intelligence at Royal Holloway, University of London in 2002.
More information about David can be found at davidroihardoon.com.
Early (by March 7, 2014 for the March session / by June 4, 2014 for the June session)
Members - $1,295
Non-member - $1,495
Members - $1,395
Non-members - $1,595
Cancellations - cancellations must be in writing and received 21 days or more prior to the start of the course. A refund will be issued less a $100 processing fee. Cancellations less than 21 days prior to the start of the course will not be eligible for a refund.
Substitutions - if you cannot attend you may send a substitute without incurring a fee provided notice is given in writing (please include substitutes' name) at least 72 hours prior to the start of the course.
Transfers - you may transfer to an earlier or later course date provided the request is received in writing at least 14 days prior to the start date of the course originally booked. There will be a $50 rebooking fee.
*Discounts available for 3 or more from the same organization.