Self-paced course
Self-paced course
Machine Learning 1: Data Profiling
Machine Learning 1: Data Profiling
Course Description
This course is PART 1 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
In this course we’ll introduce the machine learning landscape and workflow, and review critical QA tips for cleaning and preparing raw data for analysis, including variable types, empty values, range & count calculations, table structures, and more.
We’ll cover univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation.
Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll clean up product inventory data for a local grocery, explore Olympic athlete demographics, visualize traffic accident frequency in New York City, and more.
NOTE: This is NOT a coding course, and doesn’t cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
Course Description
This course is PART 1 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
In this course we’ll introduce the machine learning landscape and workflow, and review critical QA tips for cleaning and preparing raw data for analysis, including variable types, empty values, range & count calculations, table structures, and more.
We’ll cover univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation.
Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll clean up product inventory data for a local grocery, explore Olympic athlete demographics, visualize traffic accident frequency in New York City, and more.
NOTE: This is NOT a coding course, and doesn’t cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
Course Content
4 course hours
3 quizzes
Who should take this course
Data Analysts or BI experts looking to transition into a data science role or build a fundamental understanding of core ML topics
R or Python users seeking a deeper understanding of the models and algorithms behind their code
Anyone looking to learn the basics of machine learning through hands-on demos and intuitive, crystal clear explanations
Meet your instructors
Josh MacCarty
Lead Machine Learning Instructor
Josh brings over a decade of applied Machine Learning experience to the Maven team, specializing in forecasting, predictive modeling, natural language processing, cluster analysis, and pricing optimization. He has a Bachelor's degree in Economics and was a Graduate Fellow for his Master's degree in Global Political Economy.
Chris Dutton
Founder & CPO
Chris is an EdTech entrepreneur and best-selling Data Analytics instructor. As Founder and Chief Product Officer at Maven Analytics, his work has been featured by USA Today, Business Insider, Entrepreneur and the New York Times, reaching more than 1,000,000 students around the world.
Featured review
"This course demystified machine learning concepts and made it easy for beginners to learn. Many people rush to start writing code or building models, but this course helps you understand what they are actually doing and how to interpret the results. Thanks again Maven Analytics, Chris and Josh did an amazing job with this one!"
Favour O.
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Machine Learning 1: Data Profiling
Machine Learning 1: Data Profiling
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
Course Description
This course is PART 1 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
In this course we’ll introduce the machine learning landscape and workflow, and review critical QA tips for cleaning and preparing raw data for analysis, including variable types, empty values, range & count calculations, table structures, and more.
We’ll cover univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation.
Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll clean up product inventory data for a local grocery, explore Olympic athlete demographics, visualize traffic accident frequency in New York City, and more.
NOTE: This is NOT a coding course, and doesn’t cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
Course Description
This course is PART 1 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:
QA & Data Profiling
Classification
Regression & Forecasting
Unsupervised Learning
In this course we’ll introduce the machine learning landscape and workflow, and review critical QA tips for cleaning and preparing raw data for analysis, including variable types, empty values, range & count calculations, table structures, and more.
We’ll cover univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation.
Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll clean up product inventory data for a local grocery, explore Olympic athlete demographics, visualize traffic accident frequency in New York City, and more.
NOTE: This is NOT a coding course, and doesn’t cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.
If you’re ready to build the foundation for a successful career in data science, this is the course for you.
Curriculum
Meet your instructors
Josh MacCarty
Lead Machine Learning Instructor
Josh brings over a decade of applied Machine Learning experience to the Maven team, specializing in forecasting, predictive modeling, natural language processing, cluster analysis, and pricing optimization. He has a Bachelor's degree in Economics and was a Graduate Fellow for his Master's degree in Global Political Economy.
Chris Dutton
Founder & CPO
Chris is an EdTech entrepreneur and best-selling Data Analytics instructor. As Founder and Chief Product Officer at Maven Analytics, his work has been featured by USA Today, Business Insider, Entrepreneur and the New York Times, reaching more than 1,000,000 students around the world.
Student reviews
This course demystified machine learning concepts and made it easy for beginners to learn. Many people rush to start writing code or building models, but this course helps you understand what they are actually doing and how to interpret the results. Thanks again Maven Analytics, Chris and Josh did an amazing job with this one!
Favour O.
A really excellent introduction to machine learning. Exactly what is needed. Machine learning is a scary subject for a lot of people, so jumping right into it without providing basic information is not a good idea. Unfortunately, this is exacly what most courses do. Really happy to once again see that Maven Analytics is not afraid to guide the student by their hand and lay strong foundations for future courses. Thank you so much!
Ewa Devaney
As a non-engineer who works with Data Scientist teams on Machine Learning Projects, I found this greatly helpful to understand the core concepts that define a good foundation for ML projects. I felt so out-of-the-loop when the data scientist teams were talking about how important it was to clean up data to feed the model. Before I even finished the course, I already started to apply concepts to my work (Almost didn't complete it because of how excited I was to provide value to the team). If you're in the same shoes, someone in finance who works on ML projects, and you're having a tough time understanding your colleagues, this course is for you. I plan to keep taking this course and keep applying it to my work! Thanks Maven Team!
Jed Tabernero
Included learning paths
Course credential
You’ll earn the course certification by completing this course and passing the assessment requirements
Machine Learning 1: Data Profiling
Machine Learning 1: Data Profiling
CPE Accreditation
CPE Credits:
0
Field of Study:
Information Technology
Delivery Method:
QAS Self Study
Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
For more information regarding administrative policies such as complaints or refunds, please contact us at admin@mavenanalytics.io or (857) 256-1765.
*Last Updated: May 25, 2023
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