English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 4h 46m | 1.38 GB
Dr. Dursun Delen is an internationally renowned expert in business analytics, data science, and machine learning. 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 10 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), 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, National Science Foundation, National Institute for Standards and Technology, and the Department of Energy. Today, in addition to his academic endeavors, Dr. Delen provides professional education and consulting services to businesses in assessing their analytics, data science, and information system needs and helping them develop state-of-the-art computerized decision support systems.
Table of Contents
Introduction
1 Data Science Made Easy Introduction
2 Data Science Made Easy Introduction
Lesson 1 Data Science Overview
3 Topics
4 Definition, Terminology, and a Simple Taxonomy
5 Data Science Process
6 Data Science Methods and Algorithms
7 AIML Evolution
Lesson 2 Data Science Tools
8 Topics
9 Tool Landscape
10 Introduction to KNIME AP
11 Nodes and Extensions
12 KNIME Demo with Iris Dataset Part 1
13 KNIME Demo with Iris Dataset Part 2
Lesson 3 ML Model Development with KNIME
14 Topics
15 Data Ingestion and Preparation Part 1
16 Data Ingestion and Preparation Part 2
17 ML Model Building and Testing
18 Comparative Assessment
Lesson 4 Best Practices in Data Science and AIML
19 Topics
20 Data Balancing for Class Imbalance Problem
21 Cross Validation for Bias-Variance Tradeoff
22 Model Ensembles (with Bagging & Boosting)
23 Model Explainability (XAI)
Lesson 5 Text Analytics
24 Topics
25 Overview of Text Mining and Natural Language Processing (NLP)
26 Text Mining Process
27 TM Applications Sentiment Analysis
28 TM Applications Topic Modeling
Summary
29 Data Science Made Easy Summary
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