AI Workflow: Business Priorities and Data Ingestion from Coursera

This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.

This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks.

By the end of this course you should be able to:

1. Know the advantages of carrying out data science using a structured process

2. Describe how the stages of design thinking correspond to the AI enterprise workflow

3. Discuss several strategies used to prioritize business opportunities

4. Explain where data science and data engineering have the most overlap in the AI workflow

5. Explain the purpose of testing in data ingestion

6. Describe the use case for sparse matrices as a target destination for data ingestion

7. Know the initial steps that can be taken towards automation of data ingestion pipelines

Who should take this course?

This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.

What skills should you have?

It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

What's inside

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.

Data Collection

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.

Data Ingestion

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores how to carry out data science using a structured process, which is standard in industry

Teaches how to apply a scientific thought process to understanding business use cases, which helps learners develop critical thinking skills

Provides a case study to help learners practice the process of ingesting data, which helps learners develop practical skills

Taught by Mark J Grover and Ray Lopez, who are recognized for their work in the field of data science

Examines the overlap between data science and data engineering, which is highly relevant to professionals working in these fields

Requires students to have extensive background knowledge in linear algebra, probability, statistics, machine learning, Python, and IBM Watson Studio, which may pose a barrier for some learners

Reviews summary

In-demand data science skills

Learners say that this course provides an engaging and thorough introduction to data science and machine learning. The course is also praised for its practical focus, as it covers real-world applications of data science such as data ingestion, data analysis, and model building. Overall, this course is a great choice for students and learners who want to learn more about data science and gain practical experience with the tools and techniques used in the field.

Links to external resources for further learning.

"If one watches the video, the transcript gives view point and hyperlinks really makes you ready for the quiz which is the following section."

Content is organized and easy to follow.

"I am totally amazed with the course content. I have never such a well structured course like this."

"Every Video is short and crisp, and each video is followed by transcript with hyperlinks. If one watches the video, the transcript gives view point and hyperlinks really makes you ready for the quiz which is the following section."

Engaging and practical assignments.

"I love the practical business focus of your IBM! Keep doing great stuff"

"The Data Ingestion notebook was such a great experience."

"The notebooks which were shared both local version and Watson version. This in turn giving you liberty to use Cloud platform to get hands on."

Occasional technical problems in assignments.

"The course goes over practical considerations relevant to applying data science in the real world, but the final case study focuses more on data ingestion. It would have been nice if there was some component dedicated to practicing the 'empathize' stage and gaining business problem awareness."

"The instructor has completely failed to create a course that works and does not adequately answer questions. There were so many errors in the week 2 data ingest notebook that even after I fixed things, there remained errors at the end that made it impossible to use and the instructor was never able to fix it."

"I felt the mentioned time of completion of each unit is not accurate, especially the reading part. I understand that it a subjective matter. But, some reading parts have links to external sources as well, and considering those the time mentioned is not accurate."

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 AI Workflow: Business Priorities and Data Ingestion with these activities:

Review Python and Jupyter Notebooks

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Reinforces understanding of key tools and environment used in the AI workflow.

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Read through online tutorials and documentation on Python and Jupyter Notebooks.
Complete practice exercises or coding challenges related to Python.
Set up a Jupyter Notebook environment on your computer and experiment with basic commands.

Solve Probability and Statistics Practice Problems

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Strengthens foundational knowledge and problem-solving skills essential for data science and the AI workflow.

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Obtain practice problems from textbooks, online resources, or previous coursework.
Solve problems independently, focusing on understanding concepts and applying formulas.
Review solutions and identify areas for improvement.

Compile a Glossary of Key Terms and Concepts

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Enhances understanding and retention of key technical terms and concepts throughout the course.

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Create a document or spreadsheet to record key terms and their definitions.
Regularly add to the glossary while studying course materials, participating in discussions, or reading external resources.
Review the glossary periodically to reinforce understanding.

Nine other activities

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Join a study group

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Connect with other students in the course to discuss the concepts, share insights, and work through challenges together.

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Find a study group or create your own.
Meet regularly to discuss the course material.
Work together on assignments and projects.

Follow Tutorials on Design Thinking Process

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Introduces the design thinking process and its application in the AI enterprise workflow.

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Identify and access online tutorials or courses on design thinking.
Follow the tutorials step-by-step, actively engaging with the content.
Complete any exercises or assignments associated with the tutorials.

Complete Jupyter notebook exercises

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Practice the hands-on skills of data ingestion using Jupyter notebooks to solidify your understanding of the concepts covered in the Data Ingestion module.

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Review the Jupyter notebook provided in the course materials.
Follow the instructions in the notebook to complete the exercises.
Run the code in the notebook to verify your results.
Troubleshoot any errors that you encounter.

Explore data ingestion tools

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Expand your knowledge of data ingestion techniques by exploring online tutorials and documentation for popular tools and frameworks.

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Identify relevant data ingestion tools and frameworks.
Review tutorials and documentation to understand their capabilities.
Experiment with the tools and frameworks in a hands-on environment.

Join a Study Group for AI Enterprise Workflow

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Fosters collaboration, knowledge sharing, and discussion of concepts related to the AI enterprise workflow.

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Find or create a study group with peers enrolled in the course.
Set regular meeting times and discuss assigned materials, case studies, or practice problems.
Support each other through Q&A and sharing of resources.

Design a data ingestion pipeline

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Apply the principles of design thinking to create a data ingestion pipeline that meets the specific requirements of the hypothetical streaming media company.

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Identify the data sources and data types that need to be ingested.
Design the architecture of the data ingestion pipeline, including data transformation and cleansing processes.
Develop a testing plan to validate the accuracy and completeness of the ingested data.
Document the design and implementation of the data ingestion pipeline.

Build a Sample Data Ingestion Pipeline

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Provides hands-on experience in building a data ingestion pipeline, bridging the gap between data science and data engineering.

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Identify a small dataset and define the desired transformations.
Use Python and appropriate libraries to write code for data ingestion, cleaning, and transformation.
Test the pipeline for accuracy and efficiency.

Attend a Workshop on Data Science and AI in Enterprise

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Provides exposure to industry experts, case studies, and best practices in data science and AI in an enterprise context.

Browse courses on Data Science

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Identify and register for relevant workshops in the field.
Attend the workshop and actively participate in sessions.
Engage with speakers, ask questions, and network with other attendees.

Contribute to Open-Source Projects Related to Data Science

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Enhances practical skills and promotes collaboration within the data science community while reinforcing concepts learned in the course.

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Identify open-source projects on platforms like GitHub that align with interests and skill level.
Review the project documentation and codebase.
Submit bug reports, feature requests, or code contributions to the project.

Career center

Learners who complete AI Workflow: Business Priorities and Data Ingestion will develop knowledge and skills that may be useful to these careers:

Data Engineer

Automating data ingestion pipelines is an essential skill set for Data Engineers, who design, construct, and manage data pipelines to ensure that data is available for consumption by business-critical applications. This course provides a detailed overview of the data ingestion process, with a focus on building and automating data ingestion pipelines. Learners will gain hands-on experience with Python and Jupyter notebooks to build their own data ingestion pipelines, which will be essential for success as a Data Engineer.

See salaries and explore the career path for Data Engineer

Data Architect

The design and implementation of data ingestion pipelines is a fundamental responsibility of Data Architects, who are responsible for ensuring that data is managed and used effectively within an organization. This course provides a solid conceptual understanding of data ingestion, as well as practical experience with building data ingestion pipelines using Python and Jupyter notebooks. This knowledge is essential for success in Data Architect roles.

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Data Analyst

In order to identify patterns, trends, and anomalies in data, Data Analysts must have a deep understanding of data ingestion and preparation techniques. This course provides a comprehensive introduction to data collection and data ingestion, as well as the importance of applying a scientific thought process to the task of understanding the business use case. These skills are critical for success in Data Analyst roles.

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Machine Learning Engineer

To ensure that machine learning models are trained on high-quality data, Machine Learning Engineers must have a solid understanding of data ingestion and preparation techniques. This course provides a comprehensive overview of data collection, data ingestion, and data cleaning, with a focus on the specific requirements of machine learning applications. These skills will help Machine Learning Engineers build and deploy successful machine learning models.

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Cloud Architect

Cloud Architects design, build, and manage cloud-based solutions, which often involve the ingestion and processing of large amounts of data. This course provides a strong foundation in data ingestion and data engineering, with a focus on cloud-based technologies. These skills will help Cloud Architects design and implement scalable, reliable, and secure cloud-based solutions.

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Software Engineer

For Software Engineers working in the field of data science or machine learning, a deep understanding of data ingestion and preparation techniques is essential. This course provides a comprehensive overview of data collection, data ingestion, and data cleaning, with a focus on the specific requirements of software development. These skills will help Software Engineers build and deploy data-driven applications.

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Business Analyst

In order to understand the business requirements and translate them into technical specifications, Business Analysts must have a basic understanding of data ingestion and preparation techniques. This course provides an introduction to data collection, data ingestion, and data cleaning, with a focus on the business context. These skills will help Business Analysts bridge the gap between business and technical teams.

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Product Manager

To understand the needs of users and stakeholders, Product Managers must have a basic understanding of data ingestion and preparation techniques. This course provides an introduction to data collection, data ingestion, and data cleaning, with a focus on the product development process. These skills will help Product Managers build and launch successful products.

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Operations Research Analyst

To optimize business processes and make data-driven decisions, Operations Research Analysts must have a basic understanding of data ingestion and preparation techniques. This course provides an introduction to data collection, data ingestion, and data cleaning, with a focus on the principles of operations research. These skills will help Operations Research Analysts solve complex problems and improve decision-making.

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Financial Analyst

To analyze financial data and make investment recommendations, Financial Analysts must have a basic understanding of data ingestion and preparation techniques. This course provides an introduction to data collection, data ingestion, and data cleaning, with a focus on the financial industry. These skills will help Financial Analysts make informed investment decisions.

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Market Researcher

To understand consumer behavior and market trends, Market Researchers must have a basic understanding of data ingestion and preparation techniques. This course provides an introduction to data collection, data ingestion, and data cleaning, with a focus on market research. These skills will help Market Researchers conduct effective research and make data-driven recommendations.

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UX Researcher

To design and evaluate user experiences, UX Researchers must have a basic understanding of data ingestion and preparation techniques. This course provides an introduction to data collection, data ingestion, and data cleaning, with a focus on user experience research. These skills will help UX Researchers gather and analyze user data to improve the design of products and services.

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Data Scientist

Although this course is intended for practicing data scientists, it may also be beneficial for those aspiring to enter the field, particularly those with a strong background in computer science, mathematics, or statistics. The course provides a comprehensive overview of the AI enterprise workflow, with a focus on the role of data ingestion and data engineering. These skills will help aspiring Data Scientists build a solid foundation for success in the field.

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Software Developer

Software Developers working on data-intensive applications may find this course helpful, as it provides a solid foundation in data ingestion and data engineering. The course covers the principles of data collection, data ingestion, and data cleaning, with a focus on Python and Jupyter notebooks. These skills will help Software Developers build and deploy scalable, reliable, and secure data-intensive applications.

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Database Administrator

Database Administrators responsible for managing data ingestion and data pipelines may find this course helpful, as it provides a comprehensive overview of the AI enterprise workflow, with a focus on data engineering and data management. The course covers the principles of data collection, data ingestion, and data cleaning, with a focus on best practices for database management. These skills will help Database Administrators ensure the availability, integrity, and security of data.

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