Machine Learning for Data Analysis: Data Profiling & QA
What you'll learn
- Build foundational machine learning & data science skills, without writing complex code
- Use intuitive, user-friendly tools like Microsoft Excel to introduce & demystify machine learning tools & techniques
- Prepare raw data for analysis using QA tools like variable types, range calculations & table structures
- Analyze datasets using common univariate & multivariate profiling metrics
- Describe & visualize distributions with histograms, kernel densities, heat maps and violin plots
- Explore multivariate relationships with scatterplots and correlation
Requirements
- This is a beginner-friendly course (no prior knowledge or math/stats background required)
- We'll use Microsoft Excel (Office 365) for some course demos, but participation is optional
Description
This course is PART 1 of a 4-PART SERIES designed to help you build a strong, foundational understanding of Machine Learning:
PART 1: QA & Data Profiling
PART 2: Classification Modeling
PART 3: Regression & Forecasting
PART 4: Unsupervised Learning
This course makes data science approachable to everyday people, and is designed to demystify powerful Machine Learning tools & techniques without trying to teach you a coding language at the same time.
Instead, we'll use familiar, user-friendly tools like Microsoft Excel to break down complex topics and help you understand exactly HOW and WHY machine learning works before you dive into programming languages like Python or R. Unlike most Data Science and Machine Learning courses, you won't write a SINGLE LINE of code.
COURSE OUTLINE:
In this Part 1 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:
Section 1: Machine Learning Intro & Landscape
Machine learning process, definition, and landscape
Section 2: Preliminary Data QA
Variable types, empty values, range & count calculations, left/right censoring, etc.
Section 3: Univariate Profiling
Histograms, frequency tables, mean, median, mode, variance, skewness, etc.
Section 4: Multivariate Profiling
Violin & box plots, kernel densities, heat maps, correlation, etc.
Throughout the course we’ll introduce real-world scenarios designed to help solidify key concepts and tie them back to actual business intelligence case studies. You’ll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and much more.
If you’re ready to build the foundation for a successful career in Data Science, this is the course for you.
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Join today and get immediate, lifetime access to the following:
High-quality, on-demand video
Machine Learning: Data Profiling ebook
Downloadable Excel project file
Expert Q&A forum
30-day money-back guarantee
Happy learning!
-Josh M. (Lead Machine Learning Instructor, Maven Analytics)
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Who this course is for:
- Anyone looking to learn the basics of machine learning through real-world demos and intuitive, crystal clear explanations
- Data Analysts or BI experts looking to transition into data science or build a fundamental understanding of machine learning
- R or Python users seeking a deeper understanding of the models and algorithms behind their code
Instructors
![Maven Analytics](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.6 Instructor Rating
- 307,998 Reviews
- 902,966 Students
- 32 Courses
Maven Analytics helps individuals and teams build expert-level analytics & business intelligence skills.
We've helped more than 1,000,000 students around the world build job-ready skills, master sought-after tools like Excel, SQL, Power BI, Tableau & Python, and build the foundation for a successful career in data.
At Maven Analytics, we empower everyday people to change the world with data.
- 4.6 Instructor Rating
- 3,634 Reviews
- 9,826 Students
- 4 Courses
Josh has 10+ Years of applying machine learning and data science to challenging business problems like marketing mix and pricing optimization, forecasting, clustering, natural language processing, and predictive modeling. He is passionate about breaking down seemingly complex machine learning topics and explaining them in business context. He believes that diving into machine learning should be accessible to everyone.