- [Voiceover] Another great way of getting data…in data science is by scraping data.…Now this is important because…not all data comes in nice JSON formats.…Not all data is built for access…and so sometimes you have to get creative…in how you access the data.…For instance, I can include HTML text on a web page…or tables on Wikipedia…or PDF documents or media like images…and video and audio and so on.…Also, there's Word documents…and there's PowerPoint presentations…and any number of sources that have data…that do not come in a nice package for importing…in to Python or R, whatever you're using…to analyze data.…
Now, before I say anything else,…let me say, be aware of issues of…copyright and confidentiality.…This data was not created for you to scrape…and sometimes there are reasons why it's been restricted.…You need to be aware of what the restrictions are…and to respect those and operate professionally.…If you want to scrape data,…you have a couple of choices.…One is use readymade tools.…So for instance, import.io.…
- 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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