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Fixed formatting of the sections. (#18919)

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Adjusted spacing and formatting to make the text clearer.
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Viggy Kumaresan
2018-10-15 17:39:00 -04:00
committed by Quincy Larson
parent f67c81f01d
commit dc442f82b4
1 changed files with 18 additions and 20 deletions
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client/src/pages/guide/english/data-science-tools/detail/index.md
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@@ -3,29 +3,27 @@ title: detail
--- ---
## What is Data Science ## What is Data Science
### Data Science is a multi-disciplinary field that combines skills in Data Science is a multi-disciplinary field that combines skills in software engineering and statistics with domain experience to support the end-to-end analysis of large and diverse data sets, ultimately uncovering value for an organization and then communicating that value to stakeholders as actionable results.
software engineering and statistics with domain experience to
support the end-to-end analysis of large and diverse data sets,
ultimately uncovering value for an organization and then
communicating that value to stakeholders as actionable results.
## Data Scientist ## Data Scientist
Person who is better at statistics than any software engineer and
better at software engineering than any statistician. Person who is better at statistics than any software engineer and better at software engineering than any statistician.
## What Skills Do You Need? ## What Skills Do You Need?
* Mathematics - Calculus, Linear Algebra
* Statistics - Hypothesis, Testing, Regression Mathematics - Calculus, Linear Algebra
* Programming - SQL, R/Python Statistics - Hypothesis, Testing, Regression
* Machine Learning - Supervised and Unsupervised Learning, Model Fitting Programming - SQL, R/Python
* Business/Product Intuition - Interpret and communicate results to non-technical audience Machine Learning - Supervised and Unsupervised Learning, Model Fitting
Business/Product Intuition - Interpret and communicate results to non-technical audience
## Life Cycle ## Life Cycle
1 - Identify or Formulate Problem
2 - Data Preparation 1 - Identify or Formulate Problem
3 - Data Exploration 2 - Data Preparation
4 - Transform and Select 3 - Data Exploration
5 - Build Model 4 - Transform and Select
6 - Validate Model 5 - Build Model
7 - Deploy Model 6 - Validate Model
8 - Evalute or Monitor Results 7 - Deploy Model
8 - Evalute or Monitor Results