AI Workflow: Business Priorities and Data Ingestion

Go to Course: https://www.coursera.org/learn/ibm-ai-workflow-business-priorities-data-ingestion

Introduction

### Course Review: AI Workflow: Business Priorities and Data Ingestion **Overview** The course titled "AI Workflow: Business Priorities and Data Ingestion" is the inaugural module of the IBM AI Enterprise Workflow Certification specialization on Coursera. This course serves as the foundation for a six-part series, designed specifically for practicing data scientists who are already equipped with essential knowledge in the field. The structure emphasizes the importance of progressing through the modules sequentially, as each course is interconnected, building upon the knowledge and skills developed in preceding sessions. --- **Course Structure and Syllabus Breakdown** The course is divided into several key modules, each targeting critical aspects of the AI workflow: 1. **IBM AI Enterprise Workflow Introduction**: This initial module sets the stage for the entire specialization. Here, learners are introduced to the specialization requirements, while also assessing their understanding of prerequisite knowledge. The focus on design thinking emphasizes a cross-disciplinary approach—a valuable perspective that extends beyond the confines of data science alone. By using design thinking as a framework, the course encourages a systematic and iterative approach to problem-solving. 2. **Data Collection**: In this module, you'll delve into the art of identifying and articulating business opportunities. A scientific approach to understanding business use cases is stressed, likening data scientists to investigators who must thoughtfully consider the ramifications of their work. This stage underscores the necessity of taking a step back and critically analyzing key processes, ensuring that data collection is cohesive and aligned with business needs. 3. **Data Ingestion**: A significant portion of the data scientist's workload involves data cleaning, parsing, assembling, and validating data—tasks that can consume up to 60% or more of their time. This module tackles the challenges of data ingestion and provides insights through a real-world case study. The emphasis on practical application helps to frame theoretical concepts in a context that resonates with everyday experiences in data science. --- **Why You Should Enroll** 1. **Foundational Knowledge**: This course provides a robust foundation for the entire specialization. With its focus on fundamental concepts such as design thinking and the scientific method, learners will grasp the core structures needed for future modules. 2. **Practical Application**: The inclusion of case studies and real-world scenarios enriches the learning experience by connecting abstract theories to tangible applications. This relevance encourages students to engage deeply with the material. 3. **Cross-Disciplinary Insights**: The emphasis on applying design thinking beyond data science allows students to consider broader business implications and collaborate effectively across various teams. 4. **Tailored for Professionals**: As this course is designed for practicing data scientists, the content is relevant and directly applicable to current industry challenges, making it a worthwhile investment of time and effort. --- **Recommendations** If you are a data scientist seeking to deepen your expertise and enhance your workflow practices, I wholeheartedly recommend enrolling in "AI Workflow: Business Priorities and Data Ingestion." This course provides essential insights that will prove invaluable as you progress through subsequent modules of the specialization. By establishing a clear understanding of business priorities and the intricacies of data ingestion, you will be well-prepared to tackle the more advanced concepts that follow. In conclusion, this course is not just an introduction but a necessary stepping stone in an exciting journey through the realm of AI workflow. Embrace the opportunity to learn, and you'll find that the skills you acquire here will serve you well in your professional endeavors. Happy learning!

Syllabus

IBM AI Enterprise Workflow Introduction

The goal of this first module is to introduce you to the overall specialization requirements, evaluate your understanding of some key prerequisite knowledge, and familiarize you with several process models commonly used today. In this course we will use the process of design thinking, but it is the consistent application of a process in practice that is important, not the exact process itself. There are a number of reasons for choosing the design thinking process, but the most important is that it is being applied in a cross-disciplinary way—that is outside of data science.

Throughout this module you will learn or reinforce what you already know about identifying and articulating business opportunities. In this module you will learn the importance of applying a scientific thought process to the task of understanding the business use case. This process has many similarities to that of being an investigator. You will also generate a healthy respect for the need to pause, step back and think scientifically about the main processes in this stage.

Cleaning, parsing, assembling and gut-checking data is among the most time-consuming tasks that a data scientist has to perform. The time spent on data cleaning can start at 60% and increase depending on data quality and the project requirements. This module looks at the process of ingesting data and presents a case study working a real world scenario.