Marketing Analytics

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Course 1 - Marketing Analytics Foundation [11H]

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Importance of Analytics in Marketing

The Role of a Marketer

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5 Main Uses of Marketing Analytics

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The Future of Marketing is Data

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Find Your Audience

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Planning and Forecasting

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Evaluate Advertising Effectiveness

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Optimize Your Marketing Strategy

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Optimize the Sales Funnel

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Marketing Data Sources

Data Related to Offline Behavior

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Data Related to Online Behavior

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Understanding Sampled vs. Non-Sampled Data

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First-Party, Second-Party and Third-Party Data

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Marketing and Data Sets

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Sources of Digital Data

How Online Interactions Generate Data

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Use of Browser Cookies

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Use of Tags or Pixels

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SDKs for Mobile Apps

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Connecting Data Through APIs

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Use of UIDs

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Example of Collecting Data for Marketing

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Analyzing and Visualizing Data

Spreadsheets in Marketing

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Visualization Tools in Marketing

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Tools to Evaluate Digital Data

Tools to Evaluate Website Data

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Understanding Google Analytics: Basics

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Google Analytics Basics

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Evaluating Marketing Outcomes With Google Analytics

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Tools for Evaluating Marketing Success

Evaluating the Results of Marketing With Facebook Ads Manager

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Evaluating the Results of Marketing With Google Ads

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Advertising on Common Platforms

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Data and Privacy from the Consumer Perspective

Data Fuels Our Online Experience

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Consumers in the Data Driving Seat

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Data and Privacy from the Advertiser Perspective

Data and the Responsible Advertiser

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The Advertising Ecosystem and the Role of Data

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Who Owns the Data and the Relationship with the User

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Protection and Regulation

The Need for Data Protection

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Regulations to Protect Users and Their Data

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Industry Organizations and Privacy

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Course 2 - Introduction to Data Analytics [14H]

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Working With Data

Introduction: The Power of Data

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What is Data Analytics?

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What Does a Data Analyst Do?

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Data Analytics vs. Data Science

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The OSEMN Framework

The OSEMN Framework

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Start with a Goal in Mind

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Understand the KPIs

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Using the OSEMN Framework: Example

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Obtaining Data

Where to Look for Data

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Common Data Formats

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Sampled Data

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First and Third Party Data

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Evaluating the Validity of Data Sources

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An Overview of Helpful Free Datasources

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Case Study - Obtaining Data

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Scrubbing Data

Scrubbing Your Data Clean

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Remove Duplicate Records

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Format Your Records

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Handle Missing Values

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Check for Wrong Values

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Case Study - Scrubbing Data

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Exploring Data

Getting to Know Your Data Better

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The Language of Data

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Creating Visualizations

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Examine Data Distributions

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Examine Data Distributions

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Feature Engineering

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Case Study - Exploring Data

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Modeling Data

What Are Models and Why Use Them?

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How Do Models Work?

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Different Types of Models

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Case Study - Modeling Data

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Interpreting Data

Answer Your Business Question with Your Data

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Understand the Results of Your Model

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Explain Your Findings

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Storytelling With Data

The Power of Stories

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Explain, Enlighten, and Engage

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Telling a Compelling Story

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Course 3 - Data Analysis with Spreadsheets and SQL [25H]

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Working With Data in Spreadsheets: Spreadsheet Fundamentals

What are Spreadsheets and What Can They Do

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Uses of Spreadsheets

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Commonly Used Spreadsheet Tools

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The OSEMN Framework and Spreadsheet

The Use of Spreadsheets in Data Analysis

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Scrubbing Data

Accessing and Labeling Data

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Opening Spreadsheets (.csv, .xlsx) in Google Sheets

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Sampled Data

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Filtering and Sorting Data

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Cleaning Data

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Formatting Data

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Exploring Data

Introduction to Functions

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Calculating a Total

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The Average and Median Functions

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Finding Minimum and Maximum Values

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Evaluating Correlations

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Exploring Data Visually

Compare Variables with Charts

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Evaluating Trends

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Inspecting Relationships between Variables

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Modeling Data With Spreadsheets

Modeling and Functions

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Examples of Modeling Using Spreadsheets

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Limitations of Spreadsheets for Modeling

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Extracting Data With SQL: SQL Fundamentals

Why Use SQL

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Examples: Using SQL

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SQL in Google Sheets

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Writing SQL Queries

SELECT Statements

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AND, OR, and WHERE Clauses

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ORDER BY and LIMIT Clauses

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ORDER BY and LIMIT Clauses

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SQL Commands Walkthrough

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ORDER BY and LIMIT Clauses

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Functions in SQL

Writing Functions in SQL

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Arithmetic Functions

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Aggregate Functions

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Group by Functions

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SQL Functions Walkthrough

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Overview Important Functions in SQL

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The Power of Data Visualization

Why Visualization

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Different Tools for Data Visualization

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Introduction to Common Chart Types

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Data Visualizations in Google Sheets

Bar Charts

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Pie Charts

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Trend Charts

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Scatter Plots

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Data Visualizations With Tableau

Tableau Introduction

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Download Tableau Public

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Connecting Data

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Creating Your First Chart

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Creating More Complex Charts in Tableau

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Summarizing Data With Dashboard

The Power of Dashboards

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Dashboards and Business Goals

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Creating Dashboards With Tableau

Designing a Dashboard

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Building a Dashboard in Tableau

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Adding Interactivity

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Storytelling With Data Visualization

Case Study: Do's and Don'ts of Storytelling with Data Visualizations

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Crafting a Story

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Creating Your First Chart

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Creating More Complex Charts in Tableau

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Course 4 - Python Data Analytics [28H]

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Why a Programming Language for Data Analysis

Approaching Data Analysis with the OSEMN Framework

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Why Python for Data Analysis

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Introducing Python With Jupyter Notebook

Basics of Using Jupyter Notebook

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Using Jupyter Notebook on Coursera

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Python Concepts: Variables

What Does a Variable Mean in Python?

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Variable Types

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Working with Types in Python

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Python Concepts: Data Structures

Lists & Tuples

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Dictionaries

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Other Python Data Structures

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Python Concepts: Conditional Statements

Booleans in Python

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Conditional Statements

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Introduction to Libraries in Python

Introduction to Libraries

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Loading Data With Pandas

What is Pandas?

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Working With Pandas Series & DataFrames

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Python Syntax and Dot Notation Reference

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Subsets With Pandas

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Scrubbing: Removing and Modifying Data With Pandas

Removing Data

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Modifying Values

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Replacing Values

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Exploration With Pandas

Exploration: Basic Statistics

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Exploration: Filtering Data

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Exploratory Visualization

A Picture is Worth a Thousand Words

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Types of Exploratory Visualizations: Distribution

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Types of Exploratory Visualizations: Category

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Types of Exploratory Visualizations: Relationship

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Using Pandas and Matplotlib to Create Visualizations

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Understanding Visualizations for Exploration

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Grouping & Aggregations With Pandas

Where Aggregations Help Us Understand Data

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Working With Groups in Pandas

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Introduction: Modeling and Interpreting

Ovewview of Modeling

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Modeling With Python

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Parts of Interpreting

Interpreting Model Results

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Exploratory vs. Explanatory Visualizations

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Creating Explanatory Visualizations

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Tying It All Together

OSEMN: Tying It All Together

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Reviewing Full OSEMN Activity

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Course 5 - Statistics for Marketing [16H]

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Descriptive Statistics: Measures of Central Tendency

Using Measures of Central Tendency to Find the Middle

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When to Use Different Measures of Central Tendency

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Finding the Middle with Spreadsheets

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Descriptive Statistics: Measures of Dispersion

Variance and Range in Data Analytics

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Standard Deviation in Data Analytics

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Using Z-Scores to Judge a Value

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Standard Deviation in Spreadsheets

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Descriptive Statistics: Frequency Tables

Frequency Tables in Marketing Analytics

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How to Use Contingency Tables

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Conditional Probability: Bayesian Statistics

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Understanding Scatter Plots and Correlation

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Inferential Statistics: Sampling

Why Using Sampling?

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Sample Size in Statistics

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Practical Sampling Techniques

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Inferential Statistics: Distributions

Finding a Distribution

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Finding a Distribution in a Spreadsheet

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Common Distributions in Data Analytics

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Data Shapes

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Inferential Statistics: Variable Types

Quantitative Variables

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Qualitative Variables

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Independent and Dependent Variables

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Experiment Design and Hypothesis

Research Question

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Hypothesis Writing

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Observational vs. Experimental Studies

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Experimental Design for Data Analysis

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Hypothesis and AB Testing

Hypothesis Testing and AB Testing

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Understanding P-Values

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Confidence Intervals in Data Analytics

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Confidence Intervals in a Spreadsheet

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Hypothesis Testing in a Spreadsheet

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Common Mistakes in Statistics

Being Fair: Avoiding Bias

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Types of Errors: Types I and II

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Assumptions

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Data Modeling: Statistical Modeling

What is Statistical Modeling

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Modeling in Data Analytics

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Common Types of Statistical Modeling

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Data Modeling: Simple Linear Regression and Classification Methods

Simple Linear Regression

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Simple Linear Regression in Tableau

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Classification Methods in Data Modeling

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Data Modeling: Cluster Analysis

Cluster Analysis

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Cluster Analysis in Tableau

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Data Modeling: Time Series

Time Series

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Time Series in Tableau

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Data Modeling: Choosing a Model

Choosing a Model

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Data Analysis Case Studies

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Marketing Analyst Interview

Marketing Analyst on Descriptive Statistics

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Marketing Analyst on Sampling, Distributions, and Variables

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Marketing Analyst on Questions and Hypotheses

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Marketing Analyst on Modeling

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Course 6 - Data Analytics Methods for Marketing [16H]

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Find Your Audience With Segmentation

The Importance of Segmentation in Marketing

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Examples of Segmenting Audiences

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Programmatic Creative From Audience Segments

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Customer Data Platforms (CDP)

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Segmentation With Cluster Analysis

Segmentation Using Clustering

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K-Means Clustering

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Determining the Number of Clusters

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Describing Segments

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K-Means Clustering Applications

Example: Finding the Target Audience for Snackwall

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K-Means Clustering Approach

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Describing Segments (Application)

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Planning and Forecasting: Descriptive Metrics For Marketing

The Sales Funnel & Descriptive Marketing Metrics

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Descriptive vs. Predictive Metrics

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Return on Ad Spend (ROAS) And Return on Investment (ROI)

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Customer Profit

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KPIs, Reports and Dashboards

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Customer Lifetime Value

Customer Lifetime Value and Why it Matters

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Calculating Customer Lifetime Value

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Customer Lifetime Value: Example

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Forecasting With Linear Regression

Linear Regression

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Using Linear Regression to Forecast Marketing Outcomes

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Different Forms of Regression Analysis

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TITLE

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Advertising Effectiveness Measurement Introduction

Understanding Advertising Effectiveness

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Observational and Experimental Methods for Ad Effectiveness Evaluation

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Measuring Advertising Effectiveness With Experiments

Formulating a Hypothesis

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What is an Experiment?

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Random Controlled Trials

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Intention to Treat

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Interpreting Results of an Experiment

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Optimizing With A/B Testing

A/B Testing

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Single Cell, Multi-Cell and Nested Tests

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Marketing Mix Modeling

What are Marketing Mix Models?

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How does Marketing Mix Modeling Work?

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Evaluating Results of Marketing Mix Modeling

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Making Recommendations Based on Marketing Mix Modeling

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Attribution Modeling

What is Attribution?

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Single Touch Attribution

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Multi-Touch Attribution

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Challenges When Measuring Across Channels

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Sales Funnel Analysis

Dropoff in Sales Funnels

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Sales Funnel Shapes

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Visualizing a Sales Funnel in Tableau

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Course 7 - Marketing Analytics with Meta [13H]

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