These are quotes by a well known data scientist, posted on his Facebook account and elsewhere, over the last three years. It includes both quotes related to data science as well as on how to become successful and happy.

*Better: write "No SQL experience" in your resume: that way, you don't lie*

**The quotes**:

- Data science without sustainable, measurable added value, is not data science.
- Data science is the science of producing revenue out of data. If it does not produce added value, it is not data science.
- There are lies, damn lies, and Amazon reviews.
- Information is exponentially growing, but not as fast as data.
- Statisticians claim that their methods apply to big data. Data scientists claim that their methods do not apply to small data.
- The resistance to a new idea increases as the square of its importance.
- Anything published in textbooks or on the web can be automated or outsourced - job security is based on how much you know that no one else know.
- The proportion of spurious or extreme correlations is proportional to the square of the number of variables. Another reason to use modern techniques for big data.
- Unlike statistics, data science use algorithms rather than models, to make predictions, even to compute confidence intervals.
- More data is better than less. Otherwise the ideal data set would always be an empty one.
- Being ignorant of statistics is not compensated by gathering more data.
- Processing big data with small data technology is like building an 80-story skyscraper brick by brick. It will get a lot of time to get to floor #10, and it will collapse by the time you reach floor #15.
- Data science is about automating data science.
- There are two types of companies: those that think analytics is an expenditure, and those that think it is an unfair competitive advantage.
- Data flows in data sewers. Data scientists process it to make it drinkable, that is, consumable, operationalized.
- An estimate that is slightly biased but robust, easy to compute, and easy to interpret, is better than one that is unbiased, difficult to compute, or not robust. That's one of the differences between data science and statistics.
- You need statistics to write a research paper, you need data science to optimize a business process.
- Some are born natural data scientists. Others will never be one, no matter how much training they get.
- Talented data scientists leverage data that everybody see; visionary data scientists leverage data that nobody see.
- Data scientist is an hybrid business/tech role. If you don''t have the business component, you are an handicapped data scientist.
- Sometimes you make a mistake, you enter the wrong subject line for your newsletter, it works very well, and you've just discovered a new strategy.
- The only thing that never changes is the fact that everything changes all the time.
- Talent hits a target no one else can hit; genius hits a target no one else can see.
- My favorite job title is founder or co-founder; you still keep it, even after you left the company.
- I'm not one of the 1%, but I'm not one of the 99% either.
- The most exciting thing is the unknown, crossing all the times the borders that normal people avoid, and sailing permanently in uncharted waters.
- Passion, vision, and mission lead to success and happiness. Don't get a job, find a passion instead, and play smart rather than work hard, to make money.
- Cubicle life is the most risky way to accumulate wealth. You can lose all your revenue at once in unpredictable ways: it is a gamble. There are less stressful, more rewarding, and less risky ways to build a career.
- The lottery is a tax on innumeracy.
- Today I granted tenure to myself.

**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**

- 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