Data science needs data, and the easiest data is the data you already have. In this video, learn how to choose relevant, in-house resources and find maximum value with minimum additional investment.
- [Narrator] Data science projects can feel … like this epic expedition or this massive group project. … But sometimes, you can get started right here, right now. … That is, your organization may already have … the data that you need. … And there a few major advantages … to using this kind of in-house data. … The first is it's fast. … It's the fastest way to start. … It's right there and it's ready to go. … An interesting one is that certain restrictions … on data may not apply for use that is entirely … within the boundaries of your organization. … So if you have data that includes individual identifiers, … you may be able to use that … for your organization's own research. … Next, you may actually be able to talk … with the people who gathered the data in the first place. … You can have questions for them. … They can tell you how they sampled it, … what the things mean, … why they did it in this particular way, … all of that can save you an enormous amount … of time and headaches, …
Author
Released
8/8/2019- 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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1. What Is Data Science?
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The data science pathway4m 51s
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2. The Place of Data Science in the Data Universe
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Artificial intelligence8m 22s
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Machine learning8m 6s
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Big data5m 36s
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Predictive analytics4m 57s
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Prescriptive analytics7m 42s
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Business intelligence4m 40s
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3. Ethics and Agency
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4. Sources of Data
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Data preparation5m 26s
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In-house data2m 6s
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Open data4m 49s
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APIs2m 40s
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Scraping data4m 44s
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Creating data5m 37s
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Self-generated data3m 30s
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5. Sources of Rules
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6. Tools for Data Science
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Languages for data science3m 55s
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7. Mathematics for Data Science
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Algebra7m 25s
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Calculus5m 3s
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Bayes' theorem4m 25s
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8. Analyses for Data Science
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Descriptive analyses6m 38s
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Predictive models7m 32s
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Trend analysis6m 22s
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Clustering5m 45s
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Classifying5m 34s
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Dimensionality reduction5m 42s
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Validating models4m 55s
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Aggregating models4m 8s
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9. Acting on Data Science
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Interpretability3m 17s
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Actionable insights2m 53s
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Conclusion
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Next steps2m 47s
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Video: In-house data