Coursera

AI-Driven Attribution Testing (Coursera)

Offered by Board Infinity,

Welcome to AI-Driven Attribution Testing course an engaging and comprehensive course designed to guide you through the fundamental concepts and practical applications of attribution testing powered by artificial intelligence. This course is most suitable for marketers, data analysts, data scientists, and business leaders who aim to leverage data-driven insights for decision-making. It's also beneficial for students and professionals with a keen interest in the convergence of AI, data analysis, and marketing.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

In Module 1: Attribution Testing - Fundamentals, we will introduce you to AI-Driven Attribution Testing, explaining its purpose and significance in today's data-driven world. The module will further equip you with a strong understanding of the fundamentals of Attribution Modeling, essential for anyone venturing into this field.
Next, in Module 2: AI-Driven Attribution Testing - Implementation, you will apply your understanding to real-world scenarios, learning how to implement AI-Driven Attribution Testing effectively. You'll also explore best practices and case studies to solidify your learning. The course concludes with a glimpse into the future trends in attribution testing and an important discussion about ethical considerations in the field.
By the end of this course, you'll have a thorough understanding of AI-Driven Attribution Testing, know how to implement it effectively and be familiar with ethical guidelines that govern this field. Your newly gained knowledge and skills in AI-Driven Attribution Testing can empower you to make data-informed decisions and bring considerable value to your organization or future career. Second-year undergraduates interested in engineering or science, along with high school students and professionals interested in programming.

What you'll learn

Define the relevance of attribution testing in a data-driven world.
Implement AI-Driven Attribution Testing in a real-world context.
Hands-On Experience with Machine Learning Algorithms.
Ethical and Future Considerations.

Syllabus

Attribution Testing - Fundamentals
Module 1
This foundational module offers an introduction to AI-Driven Attribution Testing and delves into the basics of Attribution Modeling. It provides a strong theoretical foundation to understand the importance and process of attribution testing in the era of AI.

AI-Driven Attribution Testing - Implementation
Module 2
The second module shifts gears towards practical application, teaching you how to implement AI-Driven Attribution Testing effectively. It also shares best practices through relevant case studies. The module wraps up with an exploration of future trends and ethical considerations in AI-Driven Attribution Testing.

Go to Class

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Coursera

University of Washington

Machine Learning: Clustering & Retrieval (Coursera)

Statistics & Data Analysis Data Science

Case Studies: Finding Similar Documents. A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?

Jun 8th 2026

5-12 Weeks

Machine Learning Clustering MapReduce

Coursera

University of California, San Diego,Higher School of Economics - HSE University

Advanced Algorithms and Complexity (Coursera)

CS: Software Engineering CS: Theory

You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision.

Jun 8th 2026

5-12 Weeks

Algorithms Data Structures Machine Learning

Coursera

Johns Hopkins University

Practical Machine Learning (Coursera)

Statistics & Data Analysis Data Science

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates.

Jun 8th 2026

4 Weeks

Algorithms Machine Learning Regression

Coursera

University of Illinois at Urbana-Champaign

Managing the Organization (Coursera)

Management & Leadership

This course is intended to help you become a better manager by helping you to more fully understand and deal with some of the complexities and challenges associated with managerial life in organizations. In this course, you will learn theories, principles, and frameworks that will help you to more effectively manage and lead the organizations that you belong to. We will view organizations from different perspectives that we will use as lenses to help us highlight common managerial challenges and point us toward solutions to those challenges. Some of these common challenges that we will explore in this course include using power effectively, implementing organizational change, understanding and managing organizational culture, decision-making including decision-making pitfalls and ethical traps, and leadership. As you learn and apply the principles from this course, you will be better prepared to navigate some of the complex challenges that you face as a manager.

Jun 10th 2026

4 Weeks

Management Ethics Leadership

Coursera

Copenhagen Business School

Strategic Management (Coursera)

Management & Leadership Business

The world of business strategy is in transition. What used to work doesn't anymore -- not necessarily. This course prepares you to think strategically in an age when companies like Apple, Google, and Microsoft have become more valuable (in market cap terms) than companies like Exxon. Today, business value and competitive advantage arise more often from consumer perceptions of what is "cool" than from physical assets or economies of scale.

Jun 8th 2026

4 Weeks

Strategy Business Strategy Strategic Management

Coursera

University of Illinois at Urbana-Champaign

Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud (Coursera)

Security & Networking

Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data! In this second course we continue Cloud Computing Applications by exploring how the Cloud opens up data analytics of huge volumes of data that are static or streamed at high velocity and represent an enormous variety of information. Cloud applications and data analytics represent a disruptive change in the ways that society is informed by, and uses information.

Jun 8th 2026

4 Weeks

Cloud Machine Learning Big Data

Coursera

Stanford University

Probabilistic Graphical Models 3: Learning (Coursera)

Statistics & Data Analysis Data Science

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Jun 8th 2026

5-12 Weeks

Algorithms Machine Learning PGM

Coursera

Universidad Nacional Autónoma de México

Autoridad, dirección y liderazgo (Coursera)

Management & Leadership Business

En este curso, identificarás las principales responsabilidades y actividades que realiza un directivo en cualquier nivel de mando. Revisarás ejemplos que reflejan la importancia del ejercicio del liderazgo resaltando el valor del manejo adecuado de la comunicación, toma de decisiones, motivación, delegación, supervisión y coordinación del equipo de trabajo. Por otra parte, reconocerás los diferentes estilos de liderazgo y como éstos deben ser utilizados dependiendo de las particularidades de cada situación.

Jun 8th 2026

5-12 Weeks

Management Leadership Motivation

Coursera

Google

Project Execution: Running the Project (Coursera)

Management & Leadership Business

This is the fourth course in the Google Project Management Certificate program. This course will delve into the execution and closing phases of the project life cycle. You will learn what aspects of a project to track and how to track them. You will also learn how to effectively manage and communicate changes, dependencies, and risks. As you explore quality management, you will learn how to measure customer satisfaction and implement continuous improvement and process improvement techniques. Next, you will examine how to prioritize data, how to use data to inform your decision-making, and how to effectively present that data.

Jun 10th 2026

5-12 Weeks

Management Project Management Risk Management

Coursera

Johns Hopkins University

The Unix Workbench (Coursera)

Data Science

Unix forms a foundation that is often very helpful for accomplishing other goals you might have for you and your computer, whether that goal is running a business, writing a book, curing disease, or creating the next great app. The means to these goals are sometimes carried out by writing software. Software can’t be mined out of the ground, nor can software seeds be planted in spring to harvest by autumn. Software isn’t produced in factories on an assembly line. Software is a hand-made, often bespoke good. If a software developer is an artisan, then Unix is their workbench.

Jun 8th 2026

4 Weeks

Programming Unix Machine Learning

Coursera

University of Washington

Machine Learning: Classification (Coursera)

Statistics & Data Analysis Data Science

Case Studies: Analyzing Sentiment & Loan Default Prediction. In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.

Jun 8th 2026

5-12 Weeks

Python Machine Learning Classification

Coursera

University of Washington

Machine Learning: Regression (Coursera)

Statistics & Data Analysis Data Science

Case Study - Predicting Housing Prices. In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.

Jun 8th 2026

5-12 Weeks

Python Algorithms Machine Learning