Data Science Made Easy: Hands-On Analytics with No-Code Software Tool KNIME

4+ Hours of Video Instruction

Your introduction to data science

Overview

In Data Science Made Easy you learn what data science is including its fundamental concepts, methods, algorithms, and applications. You also learn about the best practices in data science and how to execute them using an intuitive, visual, low-code/ no-code software environment called KNIME.

About the Instructor

Dr. Dursun Delen is an internationally renowned expert in business analytics, data science, machine learning, and data mining. He is often invited to national and international conferences to deliver keynote presentations on topics related to data/text mining, business intelligence, decision support systems, business analytics, data science, and knowledge management. Prior to his appointment as a professor at Oklahoma State University in 2001, Dr. Delen worked for industry for more than 15 years, developing and delivering business analytics solutions to companies. His most recent industrial work was at a privately owned applied research and consulting company, Knowledge Based Systems, Inc. (kbsi.com), in College Station, Texas, as a research scientist. During his five years at KBSI, Dr. Delen led a number of projects related to decision support systems, enterprise engineering, information systems development, and advanced business analytics that were funded by private industry and federal agencies, including several branches of the Department of Defense, NASA, the National Science Foundation, the National Institute for Standards and Technology, and the Department of Energy.

Today, in addition to his academic endeavors, Dr. Delen provides professional education, mentoring, and consulting services to businesses in assessing their analytics, data science, and information system needs and helping them in developing state-of-the-art computerized decision support systems. He has published over 180 peer reviewed research articles that have appeared in major journals. He has also authored eleven books and textbooks in the broad areas of business analytics, data science, and business intelligence. Dr. Delen regularly chairs tracks and minitracks at various business analytics and information systems conferences. Currently, he is the editor-in-chief for the Journal of Business Analytics, AI in Business in Frontiers in Artificial Intelligence), senior editor for the Journal of Decision Support Systems, Decision Sciences, and Journal of Business Research, associate editor for Decision Analytics, International Journal of Information and Knowledge Management, and International Journal of RF Technologies, and is on the editorial boards of several other academic journals. He has been the recipient of several research and teaching awards including the prestigious Fulbright scholar, regents’ distinguished teacher and researcher, president’s outstanding researcher, and Big Data mentor awards.
Learn How To

• Explain what data science is
• List the tools available to data scientists
• Use the KNIME tool
• Follow data science best practices
• Utilize the KNIME text analytics extension

Who Should Take This Course

Anyone interest in learning what data science is and how to start practicing it

Course Requirements

There are no specific prerequisites or must-have requirements for this course. It is designed to attract and benefit anyone at any skill and managerial level who is interested in learning data science.

Lesson 1 Data Science Overview

Lesson 1 begins by defining data science, demystifying related terminology and buzzwords, and providing a simple taxonomy. Then it turns to the the most popular data science process, namely, the CRISP process, followed by a brief review of data science methods and algorithms.

Finally, in the last part of the lesson, AI, ML, artificial intelligence, and machine learning evolution are discussed, as well as some of the noteworthy men versus machine initiatives and episodes.

Lesson 2 Data Science Tools

Lesson 2 is all about software tools and platforms for data science. The lesson starts with a comprehensive presentation of the data science enablers, the so-called the tools landscape. Then the lesson introduces one of the most popular open source, free of charge, no-code, low-code analytics platform named KNIME, followed by a brief review of its regular functions, called nodes, and enrichments, called extensions. The last section of the lesson is a demonstration of the KNIME analytics platform using a relatively small data set.

Lesson 3 ML Model Development with KNIME

Lesson 3 dive into machine learning model development in Naive. This lesson starts with inputting a couple of data files about customer churn analysis, followed by merging, exploring, and preprocessing of the combined data set. Then we develop and test several popular machine learning models. After that, we compare these models based on their prediction power using scorer and ROC curves.

Lesson 4 Best Practices in Data Science and AI/ML

Lesson 4 covers the best practices in data science. Data science is as much of an art as it is science. Best practices discovered over time help us develop better models that are more effective and efficient. This lesson starts with coverage of the class imbalance problem and the remedies and fixes that we have to mitigate this problem. Then it turns to looking at cross-validation as a viable method to handle the bias-variance trade-off in predictive modeling. The next video is dedicated to a variety of model ensemble techniques, their descriptions, and their pros and cons. The last part of the lesson covers model explainability, also known as shedding light into the black box. It's also called explainable ML or explainable AI or XAI in short. All of these best practices are also demonstrated in KNIME.

Lesson 5 Text Analytics

Lesson 5 is all about text analytics and text mining, which has been a part of data science and AI machine learning for a very long time. Thanks to large language models like ChatGPT and others, text analytics is gaining overwhelming interest and popularity. This lesson starts by providing the fundamental concepts of text analytics and natural language processing. After that, the text mining process is presented and explained. Then the lesson covers two of the most popular applications of text analytics, namely, sentiment analysis and topic modeling.

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