- [Voiceover] After spreadsheets,…the first tool I wanna talk about in Data Science is R.…That's the statistical programming language,…that goes by the single letter name R.…It can easily be argued that R,…is the language of Data Science.…Take a look again,…at the KDnuggets Poll that we saw previously.…This is a survey of data mining professionals,…and R is the single most commonly used tool,…almost twice as much as everything else.…And it's 50% more used than Python,…which is considered its major competitor…with its specialized statistical packages.…
There's a few reasons for this.…Number one, R is Free and Open Source.…That's an advantage, because some of the…proprietary programs can be extremely expensive.…Second, R is optimized for Vector Operations.…That makes it possible for R to work…through an entire collection of data,…without having to write explicit for loops in it,…saves a lot of time.…Third, R has a great community behind it.…There's an immense amount of support,…and you can almost always find help…
Author
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: R