Machine Learning for Data Analysis Data Profiling QA

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 ProfilingPART 2: Classification ModelingPART 3: Regression & ForecastingPART 4: Unsupervised LearningThis 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 & LandscapeMachine learning process, definition, and landscapeSection 2: Preliminary Data QAVariable types, empty values, range & count calculations, left/right censoring, etc.Section 3: Univariate ProfilingHistograms, frequency tables, mean, median, mode, variance, skewness, etc.Section 4: Multivariate ProfilingViolin & 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.__________Join today and get immediate, lifetime access to the following:High-quality, on-demand videoMachine Learning: Data Profiling ebookDownloadable Excel project fileExpert Q&A forum30-day money-back guaranteeHappy learning!-Josh M. (Lead Machine Learning Instructor, Maven Analytics)__________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.”This is my fourth course from Maven Analytics and my fourth 5-star review, so I’m running out of things to say. I wish Maven was in my life earlier!” Tatsiana M.”Maven Analytics should become the new standard for all courses taught on Udemy!” Jonah M.

Overview

Section 1: Getting Started

Lecture 1 Course Structure & Outline

Lecture 2 READ ME: Important Notes for New Students

Lecture 3 About this Series

Lecture 4 DOWNLOAD: Course Resources

Lecture 5 Setting Expectations

Section 2: ML Intro & Landscape

Lecture 6 Intro to Machine Learning

Lecture 7 When is ML the right fit?

Lecture 8 The Machine Learning Process

Lecture 9 The Machine Learning Landscape

Section 3: Preliminary Data QA

Lecture 10 Introduction

Lecture 11 Why QA?

Lecture 12 Variable Types

Lecture 13 Empty Values

Lecture 14 Range Calculations

Lecture 15 Count Calculations

Lecture 16 Left & Right Censored Data

Lecture 17 Table Structure

Lecture 18 CASE STUDY: Preliminary QA

Lecture 19 BEST PRACTICES: Preliminary QA

Section 4: Univariate Profiling

Lecture 20 Introduction

Lecture 21 Categorical Variables

Lecture 22 Discretization

Lecture 23 Nominal vs. Ordinal

Lecture 24 Categorical Distributions

Lecture 25 Numerical Variables

Lecture 26 Histograms & Kernel Densities

Lecture 27 CASE STUDY: Histograms

Lecture 28 Normal Distribution

Lecture 29 CASE STUDY: Normal Distribution

Lecture 30 Univariate Data Profiling

Lecture 31 Mode

Lecture 32 Mean

Lecture 33 Median

Lecture 34 Percentile

Lecture 35 Variance

Lecture 36 Standard Deviation

Lecture 37 Skewness

Lecture 38 BEST PRACTICES: Univariate Profiling

Section 5: Multivariate Profiling

Lecture 39 Introduction

Lecture 40 Categorical-Categorical

Lecture 41 CASE STUDY: Heat Maps

Lecture 42 Categorical-Numerical

Lecture 43 Multivariate Kernel Densities

Lecture 44 Violin Plots

Lecture 45 Box Plots

Lecture 46 Limitations of Categorical Distributions

Lecture 47 Numerical-Numerical

Lecture 48 Correlation

Lecture 49 Correlation vs. Causation

Lecture 50 Visualizing Third Dimension

Lecture 51 CASE STUDY: Correlation

Lecture 52 BEST PRACTICES: Multivariate Profiling

Lecture 53 Looking Ahead

Section 6: Wrapping Up

Lecture 54 BONUS LESSON

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

Course Information:

Udemy | English | 2h 15m | 762.68 MB
Created by: Maven Analytics

You Can See More Courses in the Business >> Greetings from CourseDown.com