- [Voiceover] When you're getting data…for your data science project,…you may have it in a spreadsheet,…you may have it in a relational database,…but it may also be on the web.…For that reason, you need to learn…a little bit about what I call web formats.…The idea here is that data science…thrives on the Internet…in terms of retrieving data…and in terms of sharing data.…There are a few things about this.…The first is HTML, or HyperText Markup Language.…The second is XML, or Extensible Markup Language.…
The third is JSON, or JavaScript Object Notation.…And the fourth is JavaScript,…which is actually a programming language.…We'll take a look at each one in turn.…HTML, or HyperText Markup Language…is the language of web pages;…it's the thing that says what a text is…and what the headings are, and where to put links.…And the information on a web page is styled with CSS…which is for Cascading Style Sheets.…This is the thing that arranges the actual…sort of look and feel of a web page.…
You need to be able to navigate HTML…
Author
Barton Poulson
Released
7/27/2016- Assess the skills required for a career in data science.
- Evaluate different sources of data, including metrics and APIs.
- Explore data through graphs and statistics.
- Discover how data scientists use programming languages such as R, Python, and SQL.
- Assess the role of mathematics, such as algebra, in data science.
- Assess the role of applied statistics, such as confidence intervals, in data science.
- Assess the role of machine learning, such as artificial neural networks, in data science.
- Define the components of effective data visualization.
Skill Level Beginner
Duration
Views
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Introduction
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Welcome58s
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Exercise files34s
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1. What Is Data Science?
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Demand3m 54s
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Venn diagram4m 2s
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Pipeline4m 43s
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Roles3m 14s
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Team2m 14s
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2. Fields of Study
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Big data3m 20s
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Programming2m 26s
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Statistics1m 57s
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3. Ethics
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Ethical issues2m 39s
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4. Data Sources
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Metrics3m 43s
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Existing data4m 36s
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APIs4m 38s
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Scraping2m 16s
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Creating data3m 3s
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5. Data Exploration
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Exploratory graphs4m 32s
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Exploratory statistics4m 26s
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6. Programming
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Spreadsheets3m 49s
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R5m 18s
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Python4m 51s
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SQL3m 44s
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Web formats3m 53s
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7. Mathematics
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Algebra6m 22s
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Systems of equations5m 11s
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Calculus9m 50s
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Big O5m 8s
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Bayes probability8m 15s
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8. Applied Statistics
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Hypothesis6m 23s
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Confidence5m 42s
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Problems5m 30s
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Validating3m 35s
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9. Machine Learning
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Decision trees5m 22s
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Ensembles5m 15s
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k-nearest neighbors (kNN)5m 26s
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Naive Bayes classifiers5m 16s
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Artificial neural networks5m 43s
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10. Communicating
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Interpretability5m 50s
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Actionable insights4m 40s
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Reproducible research3m 27s
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Conclusion
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Next steps2m 17s
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Video: Web formats