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LinkedIn Learning

Data Science Foundations: Fundamentals (2019)

via LinkedIn Learning

Overview

Get a basic introduction to the careers, tools, and techniques of modern data science.

Syllabus

Introduction
  • The fundamentals of data science
1. What Is Data Science?
  • Supply and demand for data science
  • The data science Venn diagram
  • The data science pathway
  • Roles and teams in data science
2. The Place of Data Science in the Data Universe
  • Artificial intelligence
  • Machine learning
  • Deep learning neural networks
  • Big data
  • Predictive analytics
  • Prescriptive analytics
  • Business intelligence
3. Ethics and Agency
  • Legal, ethical, and social issues of data science
  • Agency of algorithms and decision-makers
4. Sources of Data
  • Data preparation
  • In-house data
  • Open data
  • APIs
  • Scraping data
  • Creating data
  • Passive collection of training data
  • Self-generated data
5. Sources of Rules
  • The enumeration of explicit rules
  • The derivation of rules from data analysis
  • The generation of implicit rules
6. Tools for Data Science
  • Applications for data analysis
  • Languages for data science
  • Machine learning as a service
7. Mathematics for Data Science
  • Algebra
  • Calculus
  • Optimization and the combinatorial explosion
  • Bayes' theorem
8. Analyses for Data Science
  • Descriptive analyses
  • Predictive models
  • Trend analysis
  • Clustering
  • Classifying
  • Anomaly detection
  • Dimensionality reduction
  • Feature selection and creation
  • Validating models
  • Aggregating models
9. Acting on Data Science
  • Interpretability
  • Actionable insights
Conclusion
  • Next steps

Taught by

Barton Poulson

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