**Naming conventions are often quite different in statistics and data science**, which causes quite a bit of confusion. Part of the problem with naming conventions is that "...*data science* *is the child of statistics and computer science*” (Blei & Symth, 2017) . In essence, data science then is the child of two parents who speak different languages. In one sense, this makes the job of the data scientist not only to apply the knowledge from both "parents", but to also act as a translator between the two, which is where a dictionary comes in handy.

Of, course, **many terms are universal.** For example, regression analysis and *Bayesian Inference* mean the same thing no matter what field you're working in. **Others are completely different**, like the X_{i}'s in data: if you're a statistician, those are covariates. A data scientist would call those *features*, a name borrowed from computer science.

So, let's **clear up a few of the more common mismatches.** The following picture is adapted from Larry Wasserman's *All of Statistics*. His dictionary was an eye opener for me when I first started studying data science; Hopefully it will clear up some of those fuzzy definitions for you as well.

Blei, D. and Symth, P. “Science and Data Science,” *Proceedings of the National Academies of Sciences*, vol. 114, no. 33, June 2017, pp. 8689-8692.

Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer.

© 2020 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central