Meta Marketing Analytics

This course provides a practical understanding and framework for basic analytics tasks, including data extraction, cleaning, manipulation, and analysis. It introduces the OSEMN cycle for managing analytics projects and you'll examine real-world examples of how companies use data insights to improve decision-making. By the end of this course you will be able to: • Formulate business goals, KPIs and associated metrics • Apply a data analysis process using the OSEMN framework • Identify and define the relevant data to be collected for marketing • Compare and contrast various data formats and their applications across different scenarios • Identify data gaps and articulate the strengths and weaknesses of collected data You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally you have already completed course 1: Marketing Analytics Foundation in this program.

This course explores Meta Marketing Analytics Tools. You'll learn how to create ads using Meta Ads Manager, utilize Meta experiments, optimize ads through A/B testing, integrate data from campaigns and perform an analysis to evaluate the results. By the end of this course you will be able to: • Create an ad in Meta Ads Manager • Evaluate campaign results • Conduct an A/B Test to compare ad campaigns and see which performs best • Conduct a Brand Lift test to measure how your ads impact brand awareness or recall • Conduct a Conversion Lift test to measure the incremental impact your ad has on conversions • Identify how and when to use Marketing Mix Modeling to achieve your desired outcomes • Implement a full analysis process from hypothesis to recommending measurement solutions, performing an analysis, generating insights and presenting results This course is for people who want to use Meta Ads Manager to run tests, learn what works, and optimize advertising strategies to improve ad performance.

This course helps you prepare for the Meta Marketing Science Certification exam. You’ll be guided through scheduling and taking the exam through Meta Blueprint. You’ll get access to the study guide and other resources to help you prepare for the exam. This course is only accessible to learners who have successfully completed Course 1: Marketing Analytics Foundation, Course 2: Introduction to Data Analytics, Course 3: Data Analysis with Spreadsheets and SQL, Course 4: Python Data Analytics, Course 5: Statistics for Marketing, Course 6: Data Analytics for Marketing and Course 7: Marketing Analytics with Meta in this program.

This course lays the foundation of marketing analytics. You’ll learn the basic principles of marketing, the role data analysis plays in digital marketing and how data is collected and managed. By the end of this course you will be able to: • Describe how marketers use data to inform campaign decisions • Describe the basic principles of marketing • Identify why data analysis matters in digital marketing • Implement the Meta pixel to capture data used to track visitor activity on a website • Explain how an API connects data captured offline to an online platform • Describe common platforms for online data management and evaluation • Navigate Google Analytics and Meta Ads Manager reports • Explain the significance of the privacy regulations that govern the online marketing space Regardless of your current marketing and analytics experience, this course will help you build a solid foundation for incorporating data into your marketing efforts. Learners should have basic internet navigation skills and be eager to participate.

This course explores common analytics methods used by marketers such as audience segmentation, clustering and marketing mix modeling. . You'll explore how to use linear regression for marketing planning and forecasting, and how to assess advertising effectiveness through experiments. By the end of this course you will be able to: • Understand your audience using analytics and variable descriptions • Define a target audience using segmentation with K-means clustering • Use historical data to plan your marketing across different channels • Use linear regression to forecast marketing outcomes • Describe marketing mix modeling and apply different attribution models • Assess advertising effectiveness • Explain how A/B testing works and how you can use it to optimize ads • Evaluate experiment results and assess the strength of the experiment • Optimize your sales funnel This course is for people who want to learn how to plan, forecast and optimize marketing efforts using marketing mix modeling, attribution models and A/B tests.

This course takes a deep dive into the statistical foundation upon which data analytics is built. The first part of this course will help you to thoroughly understand your dataset and what the data actually means. Then, it will go into sampling including how to ask specific questions about your data and how to conduct analysis to answer those questions. Many of the mistakes made by data analysts today are due to a lack of understanding the concepts behind the tests they run, leading to incorrect tests or misinterpreting the results. This course is tailored to provide you with the necessary background knowledge to comprehend the "what" and "why" of your actions in a practical sense. By the end of this course you will be able to: • Understand the concept of dependent and independent variables • Identify variables to test • Understand the Null Hypothesis, P-Values, and their role in testing hypotheses • Formulate a hypothesis and align it to business goals • Identify actions based on hypothesis validation/invalidation • Explain Descriptive Statistics (mean, median, standard deviation, distribution) and their use cases • Understand basic concepts from Inferential Statistics • Explain the different levels of analytics (descriptive, predictive, prescriptive) in the context of marketing • Create basic statistical models for regression using data • Create time-series forecasts using historical data and basic statistical models • Understand the basic assumptions, use cases, and limitations of Linear Regression • Fit a linear regression model to a dataset and interpret the output using Tableau • Explain the difference between linear and multivariate regression • Run a segmentation (cluster) analysis • Describe the difference between observational methods and experiments This course is designed for people who want to learn the basics of descriptive and inferential statistics.

This course introduces you to how to use spreadsheets and SQL queries to analyze and extract data. You will learn how to practically apply the OSEMN data analysis framework and spreadsheet functions to clean data, calculate summary statistics, evaluate correlations, and more. You’ll also dive into common data visualization techniques and learn how to use dashboards to tell a story with your data. By the end of this course you will be able to: • Clean data with spreadsheets • Use common spreadsheet formulas to calculate summary statistics • Identify data trends and patterns • Write foundational SQL statements and queries to extract data in spreadsheets • Create charts in Google Sheets and use Tableau to visualize data • Use dashboards to create data visualizations You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally you have already completed course 1: Marketing Analytics Foundation and course 2: Introduction to Data Analytics in this program.