Good post by Kirk Borne. If you have

- domain expertise (a),
- business acumen (b),
- analytic skills (c),
- hacking skills (d),
- coding skills (e),
- and selling skills - to sell your data science product to your boss (f),

then your potential or value (v) is equal to

v = a * b * c* d * e * f > a + b + c + d + e + f

where

- a, b, c, d, e, f are your individual scores in each category: these are number between 1.431 and 1.782, with 1.431 meaning that you don't know anything, and 1.782 meaning that you are an expert for the category in question.
- v is always above s = a + b + c + d + e + f, and v = s if and only if all individual scores are equal to 1.431 (that is, if you know absolutely nothing)
- v is as high as 3 times s, and this maximum happens when all individual scores are equal to 1.782 (that is, if you are a world master in all 6 categories)

This is because any time you face a new challenging problem, all your brain synapses [from (a) to (f)] are firing at once to find a solution, and information moves in your brain at the speed of light, from (a) to (f), to solve the problem. Because only one brain is involved, it is getting processed much faster; it is just like real-time versus back-end processing.

Few people master everything from (a) to (f). Those who do are typically entrepreneurs, but the more you have, the higher the value that you can deliver. These skills are not additive, they are multiplicative when found in a single individual - and typically, it translates in a revenue growing exponentially depending on how many of them you have. Many of these skills can be learned by most people - even advanced data science can be learned by a kid (see also this article). I will write more on this subject in the coming weeks, to help some of our younger readers boost their career.

**Notes**:

- You might want to use an adjusted v denoted as v*, with v* = (v - 8.586) / 0.73, so that the adjusted value is zero if you know nothing, and still equal to 3 times s at its maximum. Technically though, your value should be above zero as you can learn even if you start from scratch.
- The number x = 1.431 is solution of x^6 = 6 * x, while y = 1.782 is solution of y^6 = (6 * y) * 3

**DSC Resources**

- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers

**Additional Reading**

- The 10 Best Books to Read Now on IoT
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 13 New Trends in Big Data and Data Science
- 22 tips for better data science
- Data Science Compared to 16 Analytic Disciplines
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- 10 types of data scientists
- 66 job interview questions for data scientists
- High versus low-level data science

**Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge**

© 2019 Data Science Central ® 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: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- 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