Tableau Public Data Visualization Basics - From Understanding Charts to Crafting Presentation Stories

Tableau Map, it has it all, and it overflows.

This course is a hands-on exercise in creating a dashboard in Tableau to monitor customer acquisition and retention, and then deriving insights from the completed dashboard.

"Have you given up on data analysis after only studying the tools?" PMs at large corporations only need to look at the data already implemented by the analysis team. However, PMs at startups or side projects are different. They must build the 'foundation' themselves—from designing logs and labeling them to collecting the data. "Everyone says data is important, but what exactly should I implement and where in my service?" Stop getting lost in abstract concepts. Checking login history is the true beginning of Data-Driven Decision Making (DDDM).

Is it enough to just search for past exam questions and pass the Google Analytics Individual Qualification (GAIQ)? A certification holds little meaning if you obtain it without knowing how GA4 is actually used in practice or how to query and utilize data. This course focuses on practical application, allowing you to directly examine reports and GA demo accounts, and even set up a demo shopping mall yourself. You will build the skills necessary for the field while solving the latest 2026 GAIQ exam questions.

I have packed this with everything I want to tell junior PMs/POs who are struggling due to a lack of good mentors. I will generously share various techniques and mental models used by high-performing PMs/POs.

We explain 32 data visualization techniques very quickly and easily from a non-expert's perspective!

In this course, you can learn a wide range of topics, from basic Excel functions to advanced formulas, data visualization, and dashboard creation, with a focus on hands-on practice. By providing templates and solution files, self-study is possible, and you can maximize work efficiency by mastering data analysis skills that can be immediately applied in real-world scenarios.

Here’s an example of the synergy that can be created when combining Tableau Desktop and Server/Online. Let’s build a pipeline together to see how data can lead to concrete actions!

Data Visualization Storytelling That's Perfectly Achievable with Excel Alone

We will solve 88 practice questions for the Tableau Desktop Foundations (formerly Tableau Desktop Specialist) certification. We will practice directly using the Tableau program.

Learn how to use AI tools to strategically analyze raw data and visualize it persuasively—without complex or tedious statistical or coding knowledge. Starting with preprocessing messy raw data, you’ll complete complex tasks such as exploratory data analysis (EDA) and correlation analysis through just a few AI conversations, dramatically reducing the time required for practical analysis. Through concise, hands-on theory and scenario-based exercises, develop your own end-to-end professional skills for transforming numbers into powerful tools of business persuasion.

This is a special lecture that can lay the theoretical foundation to simultaneously prepare for the Microsoft AZ-900 certification, and as the latest content reflecting the scope of questions as of May 2025, by providing content related to core data concepts, Azure's relational data, Azure's non-relational data, and Azure's analytical workloads in a form combining theory and practice, it can be utilized as a meaningful educational opportunity to not only obtain the certification but also take the first step towards becoming a data professional.

Learn the problem definition and hypothesis formulation process, core competencies for data analysis!

Repetitive pipeline management in Data Warehouses (DW), now free yourself from that painful cycle with dbt! Building on the efficiency provided by dbt, become a data analytics engineer who creates higher value through business-centric data modeling, effective data lifecycle management, and more.

['Thought Architect' Roadmap for Translating Technical Truth into Business Value] 1. Introduction: Analysis of Constraints in Transferring Technical Truth to Business Value A significant bottleneck frequently observed across corporate sectors and engineering sites (R&D) is the phenomenon where innovative achievements, backed by massive resources and research capabilities, are discarded because they fail to overcome the 'communication threshold.' This is judged to stem from information asymmetry: a situation where management does not sufficiently perceive the depth of deep-tech, and technical personnel fail to appropriately utilize business language. This masterclass redefines the 'text-based communication competency' of engineers and scientists not as a mere administrative act of recording, but as part of a precise 'Engineering Design' process. Through this, we aim to present a mechanism by which an engineer's technical insights are transformed beyond simple records into core assets that drive the organization's strategic decision-making. 2. [Diagnosis] Analysis of Three Major Cognitive Defects Hindering Technical Communication ① Disconnection of Information Transfer due to the Curse of Knowledge This phenomenon occurs when experts become immersed in their own advanced level of knowledge and overlook the cognitive background of the information recipient. Consequently, reports centered on obscure formulas and technical acronyms are repeated, which is highly likely to result in structural losses such as loss of contact with decision-makers, delays in project approval, and failure in resource allocation. ② Value Loss due to Lack of Logical Architecture and Data Fragmentation By regarding document preparation as a simple post-hoc administrative procedure, errors of listing information without systematic design are frequently observed. These structural defects significantly damage the value of R&D achievements during the delivery process, tending to lead not only to the undervaluation of an individual engineer's capabilities but also to the loss of tangible and intangible assets for the entire organization. ③ Technical Reliability Crisis due to Deepening AI Dependency If generative AI, introduced to improve productivity, causes hallucinations without understanding the technical context, 'technical reliability'—the core value of engineering—can be seriously undermined. Utilizing AI without an accompanying logical verification system is highly likely to act as a potential system risk rather than a work efficiency tool. 3. [Prescription] Strategic Communication System for the Thought Architect ① Engineering Design of Thought for Unstructured Information A process of structuring abstract experimental data and hypotheses based on engineering system design principles is required. Bottom Line Up Front (BLUF): By placing core values and recommendations at the forefront of the document, we aim for an information structure optimized so that management can perform rapid and accurate decision-making (capital investment, mass production approval, etc.). Mutually Exclusive, Collectively Exhaustive (MECE): By adhering to principles that prevent logical overlap and omission, we ensure the logical completeness of the report by designing a seamless flow of thought. ② Global Standard Compliance and Quality Assurance Objectivity and integrity of data must be secured by internalizing standard protocols recognized by the global scientific community and global corporations. Standard Technical Reporting Structure (IMRaD): Maintain the consistency and reliability of the engineering narrative through a rigorous formal structure leading from Introduction, Methods, Results, to Discussion. 5 Principles of Sentence Quality (5C): By applying Correctness, Clarity, Conciseness, Consistency, and Completeness as quality control filters, we establish a Plain Language system that even non-experts can understand immediately. ③ S.E.E.D Prompt Architecture for Securing AI Command To operate AI not as a simple ghostwriting tool but as an assistant performing logical operations, a dedicated interface design must be involved. S.E.E.D Framework: Precisely control the quality of AI outputs by systematically structuring and inputting Situation, Expectation, Engineering Structure, and Data. Human-in-the-Loop: Establish a protocol where humans perform verification as the final approver to block the possibility of AI errors, thereby firmly maintaining technical authority. 4. [Execution] Strategies for Securing Cognitive Sovereignty and Workflow Optimization Communication competency acts as a core mechanism for preserving an engineer's cognitive functions beyond mere technical means. Responsibility-based Sandwich Workflow: Humans must perform context design (Top Bun) and final verification (Bottom Bun), delegating only the intermediate data processing (Meat) stage to AI to promote the activation of the human-unique Executive Control Network (ECN). RQTDW Learning Protocol for Knowledge Internalization: Apply the five stages of Read, Question, Think (facing contradictions), Discuss (virtual discussion), and Write to practical work to avoid simple acceptance of information and actively internalize knowledge. SIFT Model-based Precision Fact-Checking: Filter AI hallucinations and maintain technical precision through the process of Source (checking the origin), Investigate (understanding context), Find (cross-checking), and Trace. 5. Conclusion: Transition from Technical Expert to Engineering Leader The era of practitioners who simply perform given technical tasks is coming to an end. The ability to translate and communicate complex and difficult technical truths into a 'language of value' acceptable to organizations and society can be defined as a core survival strategy and a noble calling for modern engineers. Text communication is the Final Interface that projects an engineer's intellectual capacity to the outside world, beyond a simple act of recording. Through this masterclass, we expect you to prove your technical value and emerge as a powerful 'Engineering Leader' who drives organizational change. This system, which integrates 40 years of engineering insight, will become a strategic asset that completes your status as a professional.

Analyzing customers, markets, and revenue patterns—you no longer need to learn it yourself. Just ask AI, and expert-level analysis and strategies are completed in an instant!

Learn various features of Looker Studio (formerly Google Data Studio) using GA4 demo data. This includes explanations and hands-on practice for Looker Studio's visualization elements. In particular, marketers or planners who are not familiar with SQL can learn easy ways to connect and use BigQuery data with Looker Studio. You can learn methods to create dashboards using Looker Studio.

Data Analysis that Determines Online MD Performance! Learn essential data analysis and strategy formulation techniques necessary for MDs to maximize sales and profit in the e-commerce market.

Do you want to get a job as a data analyst? Based on my experience, let's take a look at the tasks for each data role in real companies.