In this video, learn how data science may or may not help businesses make operational decisions, but that's the sole focus of business intelligence which uses a well-defined set of approaches to find value in data.
It's an article of faith for me that any organization … will do better by using data to help with their strategy, … and with their day-to-day decisions. … But it reminds me of one of my favorite quotes … from over 100 years ago. … William James was one of the founders of American psychology … and philosophy, and he's best known for functionalism … in psychology and pragmatism in philosophy, … and he had this to say: he said, … "My thinking is first and last and always … for the sake of my doing." … That was summarized by another … prominent American psychologist, Susan Fiske, as, … "Thinking is for doing." The point is, … when we think, the way that our brain works, … it's not just there because it's there, … it's there to serve a particular purpose. … And I think the same thing is true about data … and data science in general. In fact, I like to say … data is for doing. The whole point of gathering data, … the whole point of doing the analysis, … is to get some insight that's going to allow us to do …
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
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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: Business intelligence