- [Voiceover] The last thing we wanna discuss…before we wrap up our course…on an introduction to data science…is reproducible research.…I like to think of this…as leaving a digital trail of your work.…There's a few reasons…you would wanna be able to do this.…First is, it allows you to check your work…and verify your conclusions.…Second, for your client,…for future researchers, and yourself,…and anyone who's gonna come back to the project,…it allows them to see how it happened.…Third, it may be required by certain policies.…
And fourth, by documenting your process,…it ensures a form of intellectual honesty.…Now there's a few things…that you actually need to include…in reproducible research.…Number one is your sources.…You want to include the raw data…before it's processed at all.…You want to include a list of the goals…and the rationale for the project,…and the resources,…and that can include the software, the hardware,…even the people who worked on it,…the funding sources, whatever is in the background.…Next, you want to talk about process.…
- 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
1. What Is Data Science?
2. Fields of Study
Ethical issues2m 39s
4. Data Sources
5. Data Exploration
8. Applied Statistics
9. Machine Learning
Next steps2m 17s
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