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LinkedIn Learning

Data Science Foundations: Fundamentals

via LinkedIn Learning

Overview

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Get an accessible, nontechnical overview of data science, covering the vocabulary, skills, jobs, tools, and techniques of the field.

Syllabus

Introduction
  • Getting started
1. What Is Data Science?
  • Supply and demand for data science
  • The data science Venn diagram
  • The data science pathway
  • The CRISP-DM model in data science
  • Roles and teams in data science
  • The role of questions 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
  • Bias
  • Security
  • Legal
  • Explainable AI
  • Agency of algorithms and decision-makers
4. Sources of Data
  • Data preparation
  • Labeling data
  • In-house data
  • Open data
  • APIs
  • Scraping data
  • Creating data
  • Passive collection of training data
  • Self-generated data
  • Data vendors
  • Data ethics
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
  • AutoML
  • Machine learning as a service
7. Mathematics for Data Science
  • Sampling and probability
  • Algebra
  • Calculus
  • Optimization and the combinatorial explosion
  • Bayes' theorem
8. Unsupervised Learning
  • Supervised vs. unsupervised learning
  • Descriptive analyses
  • Clustering
  • Dimensionality reduction
  • Anomaly detection
9. Supervised Learning
  • Supervised learning with predictive models
  • Time-series data
  • Classifying
  • Feature selection and creation
  • Aggregating models
  • Validating models
10: Generative Methods in Data Science
  • Generative adversarial networks (GANs)
  • Reinforcement learning
11. Acting on Data Science
  • The importance of interpretability
  • Interpretable methods
  • Actionable insights
Conclusion
  • Next steps and additional resources

Taught by

Barton Poulson

Reviews

4.7 rating at LinkedIn Learning based on 345 ratings

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