Register your product to gain access to bonus material or receive a coupon.
Data Science Made Easy: Hands-On Analytics with No-Code Software Tool KNIME (Video Course)
- By Dursun Delen
- Published Feb 10, 2025 by Addison-Wesley Professional.
Online Video
- Your Price: $239.99
- List Price: $299.99
- About this video
Video accessible from your Account page after purchase.
Description
- Copyright 2025
- Edition: 1st
- Online Video
- ISBN-10: 0-13-540522-X
- ISBN-13: 978-0-13-540522-2
4+ Hours of Video Instruction
Your introduction to data science
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
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 Descriptions:
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.
About Pearson Video Training:
Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.
Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.
Sample Content
Table of Contents
Lesson 1: Data Science Overview
Lesson 2: Data Science Tools
Lesson 3: ML Model Development with KNIME
Lesson 4: Best Practices in Data Science and AI/ML
Lesson 5: Text Analytics