Ad Tech Diagnostic Analyst Course

In the dynamic world of advertising technology, the ability to swiftly diagnose and resolve complex issues is not just a skill—it’s a necessity. The Ad Tech Diagnostic Analyst Certification is designed for ad tech professionals who are determined to elevate their technical problem-solving prowess to expert levels. This rigorous 8-week course provides in-depth training in diagnosing, troubleshooting, and effectively resolving the multifaceted technical challenges that are commonplace in the ad tech industry.

Key Outcomes:

  • Mastery of Diagnostic Tools: Gain hands-on experience with the latest tools and techniques for identifying and solving ad tech issues, ensuring you can navigate both common and obscure challenges with confidence.
  • Problem-Solving Expertise: Develop a systematic approach to troubleshooting that includes replicating issues, rigorous testing, and evidence-based solutions, turning erratic ad tech challenges into structured tasks.
  • Strategic Insights: Learn to anticipate potential pitfalls in ad tech implementations and configurations, enabling proactive rather than reactive solutions.
  • Professional Development: Completing this course will not only enhance your resume but also establish you as a go-to expert in ad tech troubleshooting within your organization and the broader industry.
  • Networking and Peer Learning: Engage with a cohort of peers, fostering a professional network of talented specialists who share a commitment to excellence in ad tech.
  • Continued Support and Learning: Benefit from 12 months of post-course support, including monthly expert calls and access to an exclusive alumni forum, ensuring ongoing professional growth and troubleshooting support.

This course is tailored for those who thrive on dissecting complex technical scenarios and are eager to advance their careers by becoming indispensable assets in the fast-evolving ad tech landscape. Enroll in the Advanced AdTech Troubleshooting Certification and transform your approach to ad tech problem-solving, from ticket to resolution.

Learning Structure

Integrating well-researched learning principles and drawing from first-hand experience in the ad tech industry, the Ad Tech Diagnostic Analyst Certification is structured around the concept of cohort-based learning. This approach, along with the incorporation of various educational methodologies, ensures a comprehensive and effective learning experience for participants. Here’s a detailed breakdown of these concepts and principles:

Cohort-Based Learning

Definition and Benefits:

  • A cohort in educational terms is a group of students who work through a curriculum together to achieve the same academic goal. In this course, each cohort is limited in size to ensure personalized attention and to foster a strong sense of community.
  • Community Building: Cohort-based settings naturally lead to the development of a supportive community. Students form professional relationships, collaborate on assignments, share insights, and motivate each other, enhancing the learning experience.
  • Peer Learning: By interacting with peers facing similar challenges, cohort members can learn from each other’s experiences and perspectives. This peer-to-peer exchange is invaluable for troubleshooting complex ad tech problems.

Integrated Learning Principles

Active Learning:

  • This course emphasizes ‘learning by doing’ through practical assignments and real-world problem solving, which research shows improve knowledge retention and understanding.

Problem-Based Learning (PBL):

  • PBL is an educational approach that uses complex and challenging problems that mirror real-world issues. This course employs PBL by presenting real ad tech problems, encouraging students to develop practical solutions based on theory and practice.

Scaffolding:

  • Instructional scaffolding is used to support learning as students progress through increasingly challenging levels of content. The course starts with foundational troubleshooting concepts and progressively introduces more complex tools and techniques.

Reflective Practice:

  • Students are encouraged to reflect on what they learn and how they apply it. This critical reflection is facilitated through discussion sessions and assignments that require learners to think about and articulate their problem-solving processes and decisions.

Research and First-Hand Experience

Foundation in Evidence:

  • The curriculum is built on a foundation of both academic research in educational methodologies and extensive first-hand experience from professionals in the ad tech field. The application of these pedagogies is designed to cater specifically to the unique challenges of ad tech troubleshooting.

Industry Relevance:

  • The course design and content are informed by years of direct experience and feedback from industry professionals. This ensures that the learning is not only theoretically sound but also practically applicable, preparing students to meet the demands of the market.

Continuous Feedback and Adaptation:

  • The course structure allows for ongoing feedback from participants, which is used to refine and adapt the curriculum. This agile approach helps keep the course relevant to current industry challenges and technological advancements.

By utilizing these educational principles and structuring the course around a cohort model, the Advanced AdTech Troubleshooting Certification offers a dynamic and interactive learning environment. This approach not only enhances the learning experience but also ensures that students are well-prepared to tackle real-world challenges in their professional roles.

Curriculum

Week 1 – Introduction to Ad Tech Problem Solving

Explore the foundational concepts of ad tech problem solving, understand different types of issues, and learn the structured approach to diagnosing them.

  • Live Training: Introduction to the scope and mindset of an ad tech problem solver, overview of the ad tech ecosystem, importance of systematic approaches, and categorization of common issues.
  • Offline Content: Additional reading on testing methodologies, glossary of key terms including definitions of ‘the lab’ and ‘the boxes’—whitebox, blackbox, and greybox.
  • Assignment: Diagnose a simulated fill rate issue using provided CSV data to prepare for understanding deeper non-monetized opportunities.

Week 2 – Non-Monetized Opportunities

Dive into diagnosing and understanding non-monetized opportunities in ad tech, focusing on common pitfalls and technical nuances.

  • Live Training: Detailed walkthrough of non-monetized opportunities such as unrendered, unsold, and selection issues in Google Ad Manager (GAM).
  • Offline Content: In-depth exploration of causes and solutions for each type of non-monetized opportunity.
  • Assignment: Analyze data from a real ad operation to identify and categorize non-monetized opportunities, preparing for understanding actionable data extraction in Week 3.

Week 3 – Getting Actionable Data and Discrepancies

Learn how to extract actionable data from GAM and understand how to handle discrepancies in data reporting.

  • Live Training: Techniques for pulling and utilizing data from GAM effectively; analyzing and resolving discrepancies.
  • Offline Content: Detailed explanations of common causes for data discrepancies and strategies for managing them.
  • Assignment: Create a comprehensive report from GAM data and analyze discrepancies, preparing for blackbox testing scenarios.

Week 4 – Blackbox Testing

Master the skills required to approach blackbox testing—where system logic is inaccessible but still testable.

  • Live Training: Strategies and techniques for approaching and diagnosing issues in blackbox systems.
  • Offline Content: Systematic testing methodologies and documentation practices for blackbox environments.
  • Assignment: Develop a strategy and execute a blackbox test on a simulated system, setting the stage for greybox diagnostics.

Week 5 – Diving into Unfamiliar Code (Greybox)

Tackle the challenges of diagnosing issues in greybox environments, where partial code access provides unique diagnostic opportunities.

  • Live Training: Introduction to tools and strategies for navigating and testing greybox systems, such as inspecting traffic, adding breakpoints, and overriding local settings.
  • Offline Content: Case studies on effective greybox diagnostics and tutorials on tools used for examining browser-run scripts.
  • Assignment: Propose and outline a method for implementing and testing a greybox strategy on a given use case, preparing for advanced diagnostic techniques.

Week 6 – Advanced Diagnostic Techniques

Enhance your skills beyond basic troubleshooting with advanced diagnostic techniques that cover a broad range of technologies and systems.

  • Live Training: Exploration of advanced techniques including synthetic monitoring, deep log analysis, and diagnostic experimentation.
  • Offline Content: Detailed guides on implementing these techniques in real-world scenarios.
  • Assignment: Apply an advanced diagnostic technique to solve a complex problem provided in class materials, leading into programmatic problem solving.

Week 7 – Programmatic Problem Solving

Focus on the specific challenges and solutions related to programmatic advertising, from compliance to optimization.

  • Live Training: Deep dive into programmatic levers that influence ad performance and compliance, including consent mechanisms, ads.txt issues, and viewability.
  • Offline Content: Comprehensive reviews of each lever with real-world application examples.
  • Assignment: TBD

Week 8 – TBD (Student-Directed Learning)

The course culminates with a session directed by the cohort’s interests, focusing on deepening understanding of previously covered topics or exploring new areas.

  • Live Training: Content based on the collective input and interest of the cohort, potentially revisiting any of the earlier sessions or exploring a new topic requested by the students.
  • Offline Content: Customized content to support the live session, curated based on the chosen topic.
  • Assignment: None this week; instead, students will engage in a comprehensive review and application of learned concepts in a group project or discussion.

Post-Course Engagement

  • Ongoing Support: Monthly calls and active forum participation for 12 months.
  • Networking: Leverage the alumni network for peer mentorship, ongoing learning, and professional opportunities.

Requirements

The list here are recommendations designed to help ensure that all participants can get the most out of the course.

  • 2+ years experience with Google Ad Manager
  • Prebid knowledge
  • HTML, CSS, and JavaScript basics
  • Knowledge of browser developer tools
  • 1+ years of experience working for an Ad Tech Company

Time Commitment

3-5 hours per week for 8 weeks

Enroll

The first cohort is planned to start October 2024. Fill out the form here to apply.

Apply for the October 2024 Cohort

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