About Statology

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail.

I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. 

My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

You can find me on LinkedIn here.

Our Content Team


LinkedIn Twitter Github Email 		Matthew Mayo, Contributing Editor
Matthew holds a Master’s degree in computer science and a graduate diploma in data mining.  Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.
 

LinkedIn Twitter Website		 Abid Ali Awan, Contributing Editor
Abid is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
 

LinkedIn Twitter		 Cornellius Yudha Wijaya, Technical Content Specialist
Cornellius is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.
 

LinkedIn 		 James P. Carmichael, PhD
James P. Carmichael, PhD is an artificial intelligence scientist and researcher.  His focus of research includes artificial intelligence and reinforcement learning applied to power systems, cryptography, control system optimization and cybersecurity.  James was recognized as a SMART (Science, Mathematics, and Research for Transformation) Scholar by the U.S. Department of Defense.

Why Learn Statistics

In an increasingly data-driven world, it’s more important than ever to know how to read and work with data.

It also pays to understand data! Some of the fastest growing professions in the world are centered around working with data, including:

  • Statisticians
  • Data Scientists
  • Data Engineers
  • Data Visualization Experts
  • Database Administrators

Best of all, these types of jobs are associated with high salaries and low stress, according to the U.S. News & World Report.

 

Tutorials

If you’re just getting started with statistics, I recommend checking out this page that lists all of the basic stats tutorials on Statology.

If you’re interested in machine learning, I recommend checking out this list of machine learning tutorials.

And if you want to learn how to use statistical software, I recommend the following guides:

 

Helpful Products

I’ve created the following products to make your life easier with statistics:

Introduction to Statistics Course: An online course that includes 19 videos with 2 hours of total content that teaches you the core concepts taught in introductory statistics.

Statology Study: An online study guide with over 100 practice problems and solutions that helps you understand all of the core concepts taught in any introductory statistics course.

Elementary Statistics Formula Sheet: A printable formula sheet that contains the formulas for the most common confidence intervals and hypothesis tests in Elementary Statistics, all neatly arranged on one page.

Statistics in Excel Made Easy: A collection of 16 Excel spreadsheets that contain built-in formulas to perform the most commonly used statistical tests.

Questions or Comments

If you have questions, comments, or just want to say hello, feel free to drop me a line at hello [@] statology.org.