Machine Learning for BI PART 1 Data Profiling

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 54 lectures (2h 14m) | Size: 654.7 MB
Demystify the world of machine learning & build core data science skills, without writing a single line of code

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
If you’re excited to explore data science & machine learning but anxious about learning complex programming languages or intimidated by terms like “naive bayes”, “logistic regression”, “KNN” and “decision trees”, you’re in the right place.

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

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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Looking for our full business intelligence stack? Search for “Maven Analytics” to browse our full course library, including Excel, Power BI, MySQL, and Tableau courses!

See why our courses are among the TOP-RATED on Udemy:

“Some of the BEST courses I’ve ever taken. I’ve studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I’ve seen!” Russ C.

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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

Machine Learning for BI, PART 1: Data Profiling