Join Barton Poulson for an in-depth discussion in this video Calculus, part of Data Science Foundations: Fundamentals.
- [Instructor] You may have the best product or service … in the world, but if you want to get paid, … you've got to make the sale … and you got to do it in a way that's profitable for you. … Surprisingly, calculus may be one of the things … to help you do just that. … The idea here is that calculus is involved … any time you're trying to do … a maximization and a minimization, … when you're trying to find the balance … between these disparate demands. … Let me give you an example of this might work. … Let's say that you sell a corporate coaching package online … and that you currently sell it for $500 … and that you have 300 sales per week. … That's $150,000 revenue per week. … But let's say that, based on your experience … with adjusting prices, you've determined … that for every $10 off of the price, … you can add 15 sales per week. … And let's also assume, just for purposes of this analysis, … that there's no increase in overhead. … So, the idea here is you can change the sales … by adjusting the price, but where are you going to have …
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: Calculus