- 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
- [Voiceover] Hi, I'm Barton Poulson, and welcome to Introduction to Data Science. In this course, we'll get a comprehensive overview of data science, a growing field that combines coding, math, statistics, and often business acumen. I'll start by showing you the entire process for data science projects and the different roles and skills that are needed. Then I'll show you the basics of obtaining data through a variety of sources, including web APIs and page scraping. We'll see how to use tools like R, Python, the command line, and even spreadsheets to explore and manipulate data.
We'll also take a look at powerful techniques for analyzing data, such as support vector machines and random forests. We'll be covering all these features plus plenty of other techniques for planning, performing, and presenting your projects to help you get started in data science and making the most of the data that's all around you. And so let's get started with Introduction to Data Science.
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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