About This Online Course

Continue your data science learning journey by understanding the interactive exploration of data and how it is achieved using state-of-the-art data visualization software. In this online training course from statistics.com, you will explore a range of different data types and structures and about various interactive techniques for manipulating and examining data to produce effective visualizations for use when communicating your findings with decision makers.

The course is very hands-on as you will be guided through an analysis of quantitative data to discern meaningful patterns, trends, relationships and exceptions that reveal organizational performance, potential problems and opportunities.

What You Will Learn

• Apply principles of perception to data visualization
• Use software tools to interactively visualize relationships among variables
• Analyze distributions of data visually
• Use a range of displays to explore data
• Use parallel coordinate plots, scatterplots and trellising to analyze multivariate data
• Visualize hierarchical data with TreeMap

The required text for this online training course is Now You See It: Simple Visualization Techniques for Quantitative Analysis, 7th edition (Analytics Press, 2009), Few, S. (available on Amazon). Learners must purchase the book before starting the course.

Your Instructor

Madhuri Maddipatla is an analytics specialist and problem solver with 10+ years of experience in analytics consulting across multiple domains, including retail, consumer packaged goods, healthcare, finance, manufacturing, and e-commerce. Currently a specialist with McKinsey & Company, she has been an instructor and mentor in the data analytics, data visualization and business consulting space for six-plus years.

Ms. Maddipatla received her Master of Science in Data Science from the University of North Carolina at Charlotte and her Bachelor of Science in Bioinformatics from the University of Maulana Azad National Institute of Technology, India. She has won several online crowd sourcing analytics contests and is a passionate problem solver and data science mentor.

Who Should Take This Course

This course is perfect for statistical analysts and data miners who need to explore and graph multivariate data, either to form impressions of the data or as a preliminary step to performing statistical tests or building models.

Prerequisites

None.

You should be well-versed in statistics or have the equivalent understanding of topics covered in the statistics.com courses: Statistics 1 – Probability and Study Design and Statistics 2 – Inference and Association.

Course Certificate

A record of completion will be issued, along with professional development credits in the form of continuing education units upon 50-percent completion.

In addition, a Credly badge to add to your LinkedIn profile will be issued upon 80-percent completion of this online training course.

Course Format

This self-paced, online training course takes place at The Institute for Statistics Education at statistics.com for four weeks. During each session week, you can participate at times of your own choosing—there are no set times for the lessons. Participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Course Pricing

$649 (per person)

Register through FedLearn using the special promo code FedLearn22 and receive a five-percent discount on the original online course price.

Continuing Education Unit Credits

This online course provides 5.0 CEUs upon 50-percent completion.

This course is also recommended for 3.0 upper division college credits by the American Council on Education upon 80-percent completion.