Data Science Foundations: Fundamentals

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Barton Poulson

5:17:19

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01. Introduction

001. Getting started.mp4

01:33

02. What Is Data Science

002. Supply and demand for data science.mp4

04:28

003. The data science Venn diagram.mp4

04:29

004. The data science pathway.mp4

04:51

005. The CRISP-DM model in data science.mp4

04:01

006. Roles and teams in data science.mp4

04:25

007. The role of questions in data science.mp4

04:53

03. The Place of Data Science in the Data Universe

008. Artificial intelligence.mp4

08:27

009. Machine learning.mp4

08:06

010. Deep learning neural networks.mp4

08:22

011. Big data.mp4

05:36

012. Predictive analytics.mp4

04:57

013. Prescriptive analytics.mp4

07:41

014. Business intelligence.mp4

04:40

04. Ethics and Agency

015. Bias.mp4

06:35

016. Security.mp4

05:32

017. Legal.mp4

06:42

018. Explainable AI.mp4

09:56

019. Agency of algorithms and decision-makers.mp4

04:52

05. Sources of Data

020. Data preparation.mp4

05:26

021. Labeling data.mp4

08:49

022. In-house data.mp4

05:38

023. Open data.mp4

04:15

024. APIs.mp4

02:40

025. Scraping data.mp4

04:44

026. Creating data.mp4

05:37

027. Passive collection of training data.mp4

03:57

028. Self-generated data.mp4

03:30

029. Data vendors.mp4

05:30

030. Data ethics.mp4

05:14

06. Sources of Rules

031. The enumeration of explicit rules.mp4

04:03

032. The derivation of rules from data analysis.mp4

04:26

033. The generation of implicit rules.mp4

03:32

07. Tools for Data Science

034. Applications for data analysis.mp4

04:52

035. Languages for data science.mp4

03:55

036. AutoML.mp4

04:16

037. Machine learning as a service.mp4

03:21

08. Mathematics for Data Science

038. Sampling and probability.mp4

05:27

039. Algebra.mp4

07:25

040. Calculus.mp4

05:03

041. Optimization and the combinatorial explosion.mp4

06:10

042. Bayes theorem.mp4

04:25

09. Unsupervised Learning

043. Supervised vs. unsupervised learning.mp4

03:38

044. Descriptive analyses.mp4

06:38

045. Clustering.mp4

05:45

046. Dimensionality reduction.mp4

05:38

047. Anomaly detection.mp4

05:00

10. Supervised Learning

048. Supervised learning with predictive models.mp4

07:32

049. Time-series data.mp4

09:14

050. Classifying.mp4

05:34

051. Feature selection and creation.mp4

05:49

052. Aggregating models.mp4

08:32

053. Validating models.mp4

05:46

11. 10 Generative Methods in Data Science

054. Generative adversarial networks (GANs).mp4

05:50

055. Reinforcement learning.mp4

06:02

12. Acting on Data Science

056. The importance of interpretability.mp4

03:17

057. Interpretable methods.mp4

05:03

058. Actionable insights.mp4

02:53

13. Conclusion

059. Next steps and additional resources.mp4

02:47

Description

Data science is driving a world-wide revolution that touches everything from business automation to social interaction. It’s also one of the fastest growing, most rewarding careers, employing analysts and engineers around the globe. This course provides an accessible, nontechnical overview of the field, covering the vocabulary, skills, jobs, tools, and techniques of data science. Instructor Barton Poulson defines the relationships to other data-saturated fields such as machine learning and artificial intelligence. He reviews the primary practices: gathering and analyzing data, formulating rules for classification and decision-making, and drawing actionable insights. He also discusses ethics and accountability and provides direction to learn more. By the end, you’ll see how data science can help you make better decisions, gain deeper insights, and make your work more effective and efficient.

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Data Science

Barton Poulson

Instructor's Courses

Founder of datalab.cc, author for LinkedIn Learning, associate professor of psychology at Utah Valley University. I teach people how to use data to find practical solutions to real-life problems. #DataIsForDoing

Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.