Machine Learning for Data Analysis Data Profiling QA

Machine Learning made simple with Excel! Learn data profiling for advanced analysis & business intelligence (no coding!)

4.6/5

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

Overview

Section 1: Getting Started

Lecture 1 Course Structure & Outline

Lecture 2 READ ME: Important Notes for New Students

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

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