In this video, learn how trends over time, such as stock prices or sales volume, present special challenges for data science analysis but they have some of the greatest potentials for practical impact.
- [Instructor] When you go out hiking in a new place, … it's nice to have a path … to help you get where you're going. … It's also nice to know that the path … might actually take you to some place you want to be, … so how can you see where you're going, … and how long it's going to take you to get there? … Well, as a data scientist, … your job is to figure out the path your data is on, … so you can inform decisions … about whether to stay on the current path, … or whether changes need to be made. … The most basic way to do this is with trend analysis, … and it starts by plotting a line. … Simply make a graph of the changes over time, … and then connect the points … to make a clear line of one kind or another. … Now, when you're doing the analysis, … you have to be worried about something a little different … from other analyses we may have looked at, … and that's something called autocorrelation, … or self-correlation. … The idea here is that every value is influenced … by the previous values, more or less. …
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: Trend analysis