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Arifa Garman and Ada Crutchfield

Businesses today have access to an increasingly large amount of detailed customer data, and this influx of “big data” is only going to continue. Combined with a detailed history of marketing actions, there is a newfound potential for deriving actionable insights, but you need the tools to do so. Using real-world applications from various industries, this course will help you understand the tools and strategies used to make data-driven decisions that you can put to use in your own company or business.

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Businesses today have access to an increasingly large amount of detailed customer data, and this influx of “big data” is only going to continue. Combined with a detailed history of marketing actions, there is a newfound potential for deriving actionable insights, but you need the tools to do so. Using real-world applications from various industries, this course will help you understand the tools and strategies used to make data-driven decisions that you can put to use in your own company or business.

This valuable data may include in-store and online customer transactions, customer surveys, web analytics, as well as prices and advertising. You’ll also learn how to assess critical managerial problems, develop relevant hypotheses, analyze data and, most importantly, draw inferences to create convincing narratives which yield actionable results. Artificial intelligence and machine learning will be explored as tools to deepen analytical skills and acumen and hone decision-making.

This comprehensive exploration into digital marketing analytics tools and techniques is critical knowledge for marketing influencers, digital marketing analysts, and product and brand decision-makers within small and medium businesses as well as larger organizations with international reach.

What You'll Learn in this Course:

Learn how to leverage leading tools and approaches to digital marketing data analysis. Dive into Search Engine Marketing and Website analytics, online testing, machine learning, and AI/Big Data applications to strengthen your digital marketing efforts and leverage your resources most effectively.

Course Objectives:

This course will cover the fundamentals of digital marketing.

By the end of this course, you will be able to:

1- Analyze and assess the performance of paid search campaigns, diagnose potential problems, and recommend adjustments to the digital marketing campaign.

2- Describe the importance of Search Engine Optimization and Recommendation Systems in digital environments.

3- Evaluate campaign analytics and use online testing to determine how design affects the performance of a digital marketing campaign.

4- Describe the Paradigm shift in machine learning methods.

5- Identify the process of evaluating the performance of machine learning algorithms.

6- Describe the expanding application of big data as they apply to neural networks.

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What's inside

Syllabus

SEO, Paid Search, and Web Analytics
Welcome to Week 1 of your course. Each week will have a similar format. The weekly page will first provide an overview of the content we will cover. As you work your way down the page, you will see that the content is divided into sections. Navigate through each lesson on the page to complete the assigned work. Work your way through each item in the lessons to watch videos, read assigned articles, participate in discussions, and complete assignments. You should expect to spend at least 30-minutes in total watching seven short videos. In addition to the videos and readings, you will have practice questions, a few activities, and a scenario at the end of the week. These activities and scenarios are essential to helping you learn and apply the skills you will need to demonstrate and master Digital Marketing Analytics.
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what should give you pause
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Examines how rich data enhances analytical capacity to make qualitative to quantitative analytical data and machine learning to deep learning AI, resulting in a better understanding of consumer behavior
Introduces big data and artificial intelligence, along with the use of rich data and enhanced analytical capacity to move beyond qualitative to quantitative analytical data
Suitable for marketing influencers, digital marketing analysts, and product and brand decision-makers in small and medium businesses and large organizations
Led by instructors Arifa Garman and Ada Crutchfield
Taught by Arifa Garman and Ada Crutchfield, who are recognized for their work in digital marketing analytics
Leverages a range of real-world applications from multiple industries

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Reviews summary

Data-driven digital marketing mastery

According to students, Digital Marketing 2 is a highly valuable course for professionals seeking to enhance their data analytics skills in marketing. Learners praise the program for its practical, real-world applications, particularly in areas like SEO, paid search, and web analytics. Many found the focus on machine learning and AI to be particularly forward-thinking and relevant. While some reviewers noted the pace can be fast or that it assumes some prior knowledge, the overall consensus is that the course provides actionable insights and tools for making data-driven decisions. The instructor's insights and hands-on activities are frequently highlighted as strengths, making it a solid choice for career development.
Instructor provides clear explanations and updates course content.
"The instructor's explanations were clear and concise, making complex topics easier to grasp and apply."
"It's great to see that the course content has been updated, showing the instructor is responsive to feedback and current trends."
"I really valued the instructor's insights and the way they presented the material, making it very engaging."
Provides a broad and fundamental understanding of digital marketing analytics.
"This course covers a wide array of topics from SEO to AI, providing a very comprehensive understanding of the field."
"I feel like I got a really good grasp of the fundamentals across different aspects of digital marketing analytics."
"The structure covering SEO, Paid Search, Web Analytics, ML, and AI was well-thought-out and thorough, covering key areas."
Covers cutting-edge topics like AI, machine learning, and big data.
"The deep dive into machine learning and AI for marketing was exactly what I needed to stay competitive in the industry."
"I appreciated the focus on big data and neural networks; it broadened my understanding significantly."
"The course did a great job of integrating AI/ML concepts into practical digital marketing scenarios, which felt very relevant."
Provides actionable strategies for real-world marketing scenarios.
"I found this course incredibly helpful; it gave me practical tools and strategies I could immediately apply to my marketing campaigns."
"The scenarios and activities were excellent, really helping me understand how to make data-driven decisions in my role."
"This course delivers on its promise of real-world applications. The modules on web analytics were particularly insightful."
Moves quickly and may benefit those with some prior experience.
"The course moved quite fast, and I felt it would be more challenging if you're a complete beginner in marketing analytics."
"While valuable, I think some prior exposure to digital marketing or data concepts would make it easier to follow the content."
"I had to spend extra time on some topics as the pace was brisk, but the material is solid if you push through it."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Digital Marketing 2 with these activities:
Join a Study Group for Digital Marketing Analytics Concepts
Engaging in peer discussions will foster a better understanding of course concepts and provide diverse perspectives.
Show steps
  • Connect with classmates or join online forums to form a study group.
  • Establish regular meeting times to discuss assigned readings, assignments, and concepts.
  • Take turns leading discussions and presenting different perspectives.
  • Support each other with questions, explanations, and encouragement.
Solve Practice Questions on SEO Fundamentals
Practicing SEO fundamentals through drills will help you solidify your understanding and prepare you for real-world scenarios.
Show steps
  • Review the provided materials on SEO basics.
  • Attempt the practice questions covering topics like keyword research, on-page optimization, and link building.
  • Check your answers and review the explanations to identify areas for improvement.
Compile a Resource Guide on Digital Marketing Analytics Tools
Creating a resource guide will help you stay updated on the latest tools and technologies used in digital marketing analytics.
Show steps
  • Research and identify various digital marketing analytics tools available.
  • Categorize the tools based on their functionalities, such as web analytics, social media analytics, or email marketing analytics.
  • Compile a list of the tools, including their key features, pricing, and links to their websites.
  • Share the resource guide with classmates or industry professionals.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Conduct A/B Testing on Website Elements
Hands-on A/B testing will provide you with practical experience in optimizing website elements for better performance.
Browse courses on A/B Testing
Show steps
  • Identify a website element to test, such as a headline, call-to-action, or layout.
  • Create variations of the element with different design or content.
  • Use a tool or platform to set up the A/B test and collect data.
  • Analyze the results to determine which variation performs better.
  • Implement the winning variation on your website.
Complete Online Tutorials on Machine Learning Algorithms
Following online tutorials on machine learning algorithms will enhance your understanding of their applications and practical implementation.
Show steps
  • Identify reputable online platforms or courses offering tutorials on machine learning algorithms.
  • Choose tutorials that cover algorithms relevant to digital marketing, such as supervised and unsupervised learning.
  • Follow the tutorials, experiment with code examples, and take notes on key concepts.
  • Apply the learned techniques to practical scenarios or projects.
Attend a Workshop on Data Visualization for Digital Marketing
Attending a workshop on data visualization will enhance your skills in presenting data effectively, which is crucial for digital marketing analytics.
Browse courses on Data Visualization
Show steps
  • Research and identify relevant workshops offered by industry professionals or organizations.
  • Register and attend the workshop.
  • Actively participate in the sessions, take notes, and ask questions.
  • Apply the learned techniques to your own digital marketing projects.
Read 'Digital Marketing Analytics' by Chuck Hemann
This book provides a comprehensive overview of digital marketing analytics techniques and their applications, which will complement the course content.
Show steps
  • Read the assigned chapters on topics like web analytics, paid search, and social media analytics.
  • Take notes and highlight key concepts.
  • Complete the chapter exercises to apply your understanding.

Career center

Learners who complete Digital Marketing 2 will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Digital Marketing 2.
Provides an exhaustive, highly structured framework for planning, executing, and evaluating cross-channel digital marketing initiatives. Simon Kingsnorth systematically dissects search optimization, programmatic media, content distribution, and automation workflows. It serves as an indispensable reference for practitioners seeking to build cohesive, revenue-aligned digital engines.
The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.
The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.
A monumental foundational text establishing the mathematical, algorithmic, and statistical principles of modern deep neural networks, from linear algebra and optimization to convolutional and sequence modeling architectures.
A timeless mathematical masterpiece that provides an uncompromising Bayesian and probabilistic foundation for linear classifiers, graphical models, kernel methods, and sampling algorithms.
The third edition of the essential guide to data wrangling with pandas, NumPy, and Jupyter in Python. Updated for newer versions of the libraries, this book remains a core resource for anyone performing data analysis in Python. It widely used textbook and reference for its clear explanations and practical examples.
Articulates the seminal inbound philosophy that transformed digital customer acquisition from intrusive outbound interruption to magnetic content-led discovery. Brian Halligan and Dharmesh Shah unpack search visibility, blogging cadence, social reach, and lead nurturing. It offers founders and marketers a proven blueprint for sustainable organic growth.
Transforms content marketing into a radical customer-first framework centered on answering buyers questions with unvarnished transparency. Marcus Sheridan details how addressing cost, comparisons, and problems directly on your website dismantles friction, accelerates the sales pipeline, and builds enduring commercial trust.
Offers a complete overview of online testing, covering theoretical foundations and best practices for a comprehensive alignment of all aspects of the design, implementation, and evaluation of computerized testing.
Provides a comprehensive guide to Hadoop, the open-source framework for Big Data processing. It covers the core concepts and components of Hadoop, as well as advanced topics such as data warehousing and machine learning.
The definitive mathematical and theoretical canon of statistical learning, establishing the rigorous analytical foundations of regularized regression, tree ensembles, and high-dimensional inference.
Established the conceptual foundation of modern digital marketing by demonstrating that customer attention must be earned through explicit consent rather than bought through unwanted broadcast. Seth Godin introduces value-driven communication sequences that remain the bedrock of modern email marketing, inbound workflows, and retention campaigns.
A distilled practitioner field guide outlining best practices for the entire machine learning lifecycle, from problem formulation and feature engineering to model deployment and monitoring.
Delivers an end-to-end systems architecture guide for building reliable production ML systems, addressing data engineering trade-offs, real-time feature computation, and model drift mitigation.
Clarifies brand messaging by applying classical narrative architecture to digital customer journeys, positioning the user as hero and the brand as empathetic guide. Donald Miller supplies actionable templates to overhaul website copy, landing pages, and email funnels, eliminating confusing corporate jargon to substantially boost digital conversion rates.
Guides engineers step-by-step through implementing, training, and fine-tuning complete generative transformer architectures in pure PyTorch, providing crystal-clear low-level insight into modern foundation models.
Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment workflows.
Bridges linear algebra, multivariable calculus, vector geometry, and probability directly to fundamental machine learning algorithms like PCA, Gaussian mixture models, and support vector machines.
Elevates content creation across blogs, social posts, landing pages, and newsletters into an authoritative craft accessible to every marketing professional. Ann Handley delivers tactical rules on narrative empathy, grammatical cadence, and rigorous editing, empowering digital marketers to publish compelling copy that captivates target audiences.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
Comprehensive guide to paid search marketing, covering everything from keyword research to ad copywriting to campaign optimization. It's a great resource for anyone who wants to learn more about paid search or improve their existing campaigns.
Is an advanced guide to web analytics using Google Analytics 4, the latest version of Google's analytics platform. It covers advanced topics such as event tracking, custom dimensions and metrics, and data analysis techniques.
Introduces the Bullseye framework to help teams systematically discover, test, and scale the most productive customer acquisition channels among nineteen distinct options. Gabriel Weinberg and Justin Mares emphasize rapid experimentation, channel metrics, and iterative validation, preventing growth teams from prematurely locking into ineffective channels.
Instructs data scientists on applied model evaluation, cross-validation, hyperparameter tuning, and pipeline construction through Scikit-Learn maintainers Andreas Müller and Sarah Guido.
Demystifies practical transformer model implementation through the Hugging Face ecosystem, demonstrating how to fine-tune, optimize, and deploy attention architectures across diverse natural language processing and understanding tasks.
Provides an in-depth introduction to machine learning, covering the fundamental concepts and algorithms used in Big Data analysis. It is written by Andrew Ng, a leading expert in machine learning, and is highly recommended for those who want to gain a deeper understanding of Big Data.
Provides a comprehensive guide to Spark, the popular open-source framework for Big Data processing. It covers the core concepts and components of Spark, as well as advanced topics such as streaming data and machine learning.
Outlines a comprehensive methodology for building a loyal, monetizable digital audience by producing high-value editorial content rather than transient advertisements. Joe Pulizzi shows marketers how to define a unique content niche, establish sustainable publication cadences, and engineer direct attribution models that tie storytelling to business growth.
Investigates the engineering realities of running automated ML pipelines in production, addressing data drift detection, automated retraining loops, telemetry infrastructure, and continuous validation.
Demystifies modern artificial intelligence tools and large language models for marketing practitioners seeking operational efficiency and enhanced campaign performance. Mark Lamplugh presents hands-on strategies for automated content generation, predictive analytics, prompt engineering, and audience clustering to modernize everyday agency and in-house workflows.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
Delivers battle-tested operational patterns for serving foundation models, addressing GPU cluster throughput, semantic caching, automated evaluation benchmarks, and continuous latency minimization.
Bridges direct-response marketing fundamentals with contemporary software automation, CRM integration, and artificial intelligence capabilities. Dan Kennedy and Parthiv Shah provide practical architectures for automated lead segmentation, dynamic follow-up sequences, and customer reactivation campaigns that optimize conversion pipelines.
Provides an agile playbook for modern omnichannel growth, showing performance marketers how to align organic content initiatives with paid acquisition funnels while safeguarding CAC in increasingly saturated digital channels.
Details an applied architectural methodology for running high-performance open-weights models on local workstations, tackling quantization fidelity, memory bandwidth limits, and prompt assembly trade-offs.
Bridges the gap between statistical theory and its practical application in data science using R and Python. It focuses on the statistical concepts most relevant to data scientists and provides code examples. It's a useful reference and learning resource for those applying statistics in their work.
Equips practitioners with the analytical rigor needed to extract actionable business intelligence from complex consumer data streams and digital marketing channels. Kevin Hartman covers data collection architectures, multi-touch attribution modeling, performance dashboards, and storytelling with metrics to ensure digital investments demonstrate measurable return.
Details cloud-native DevOps architectures for orchestrating automated machine learning lifecycles, integrating CI/CD triggers, model registry synchronization, and automated hyperparameter sweep workers.
Delivers a systematic operational checklist for executing technical and on-page search engine audits, guiding digital marketers through Core Web Vitals diagnostics, indexation hygiene, and algorithmic visibility recovery.
Offers a rigorous theoretical foundation for reasoning under uncertainty, combining graph theory and probability distributions to model complex high-dimensional dependencies across autonomous intelligent systems.
Examines the next frontier of digital customer engagement by integrating physical and virtual touchpoints into cohesive, immersive brand experiences. Philip Kotler and co-authors explore spatial computing, augmented reality, conversational artificial intelligence, and omnichannel ecosystems, preparing strategic leaders for the future evolution of interactive marketing.
Offers a new way of thinking about data science and ethics informed by intersectional feminist thought. It challenges existing power structures within data and explores how data can be used to work towards justice. It's highly relevant for contemporary discussions around fairness and bias in data analysis.
Explores how modern technology can be deployed to elevate the human customer experience across dynamic digital and physical touchpoints. Philip Kotler investigates predictive marketing, contextual advertising, and augmented intelligence, offering strategic guidelines for harmonizing technical automation with genuine emotional resonance.
Provides a comprehensive overview of paid search marketing. It covers all aspects of paid search, from campaign setup to optimization.
Is an excellent starting point for anyone new to data analysis or statistics. It demystifies core statistical concepts without relying heavily on mathematical formulas, making it highly accessible for high school and undergraduate students. It provides a strong foundation in the intuition behind statistical analysis and helps readers understand how data can be used and misused. This is valuable background reading that builds prerequisite knowledge.
Provides a guide to creating effective and aesthetically pleasing data visualizations. It delves into the principles behind good visualization design, helping readers make informed choices about how to represent their data. It valuable reference for anyone creating visualizations, from students to professionals.
Provides a comprehensive guide to Big Data analytics with Java, covering topics such as data ingestion, data storage, and data processing. It good option for those who want to gain a practical understanding of how to use Java to analyze Big Data.

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