Get a basic introduction to the careers, tools, and techniques of modern data science.
- [Barton] We're in the midst of a new global revolution, one that is driven by and focused on the data that surrounds us and infuses everything we do from making toast, to driving cars across the country, to inventing new paradigms for social interaction. And the key to all of this is data science. I'm Barton Poulson, and in this course, we'll explore some of the ways that data science allows us to ask and answer new questions that we previously didn't even dream of. To do that, we'll see how data science connects to other data-rich fields like artificial intelligence, machine learning, and prescriptive analytics. We'll map out the fundamental practices for gathering and analyzing data, formulating rules for classification and decision making, and implementing those insights. We'll touch on some of the tools that you can use in data science, but we'll focus primarily on the meaning and the promise of data in our lives. Because this discussion focuses on ideas as opposed to specific techniques, if you want to know how you can thrive in the new world of data regardless of your technical background, you can get a better understanding of how to draw on data to do the things that are important to you and to do them more effectively and more efficiently. And so, let's get started with Data Science Foundations: Fundamentals.
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
Views
Related Courses
-
Introduction
-
1. What Is Data Science?
-
The data science pathway4m 51s
-
2. The Place of Data Science in the Data Universe
-
Artificial intelligence8m 22s
-
Machine learning8m 6s
-
Big data5m 36s
-
Predictive analytics4m 57s
-
Prescriptive analytics7m 42s
-
Business intelligence4m 40s
-
-
3. Ethics and Agency
-
4. Sources of Data
-
Data preparation5m 26s
-
In-house data2m 6s
-
Open data4m 49s
-
APIs2m 40s
-
Scraping data4m 44s
-
Creating data5m 37s
-
Self-generated data3m 30s
-
-
5. Sources of Rules
-
6. Tools for Data Science
-
Languages for data science3m 55s
-
7. Mathematics for Data Science
-
Algebra7m 25s
-
Calculus5m 3s
-
Bayes' theorem4m 25s
-
-
8. Analyses for Data Science
-
Descriptive analyses6m 38s
-
Predictive models7m 32s
-
Trend analysis6m 22s
-
Clustering5m 45s
-
Classifying5m 34s
-
Dimensionality reduction5m 42s
-
Validating models4m 55s
-
Aggregating models4m 8s
-
-
9. Acting on Data Science
-
Interpretability3m 17s
-
Actionable insights2m 53s
-
-
Conclusion
-
Next steps2m 47s
-
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.
CancelTake notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.
Share this video
Embed this video
Video: The fundamentals of data science