From dc442f82b422ecd1910473c4864f8c708f094b52 Mon Sep 17 00:00:00 2001 From: Viggy Kumaresan Date: Mon, 15 Oct 2018 17:39:00 -0400 Subject: [PATCH] Fixed formatting of the sections. (#18919) Adjusted spacing and formatting to make the text clearer. --- .../data-science-tools/detail/index.md | 38 +++++++++---------- 1 file changed, 18 insertions(+), 20 deletions(-) diff --git a/client/src/pages/guide/english/data-science-tools/detail/index.md b/client/src/pages/guide/english/data-science-tools/detail/index.md index 4d9cbcc5ec..c9a8c2a510 100644 --- a/client/src/pages/guide/english/data-science-tools/detail/index.md +++ b/client/src/pages/guide/english/data-science-tools/detail/index.md @@ -3,29 +3,27 @@ title: detail --- ## What is Data Science -### 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. +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. ## 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? - * Mathematics - Calculus, Linear Algebra - * Statistics - Hypothesis, Testing, Regression - * Programming - SQL, R/Python - * Machine Learning - Supervised and Unsupervised Learning, Model Fitting - * Business/Product Intuition - Interpret and communicate results to non-technical audience + +Mathematics - Calculus, Linear Algebra +Statistics - Hypothesis, Testing, Regression +Programming - SQL, R/Python +Machine Learning - Supervised and Unsupervised Learning, Model Fitting +Business/Product Intuition - Interpret and communicate results to non-technical audience ## Life Cycle - 1 - Identify or Formulate Problem - 2 - Data Preparation - 3 - Data Exploration - 4 - Transform and Select - 5 - Build Model - 6 - Validate Model - 7 - Deploy Model - 8 - Evalute or Monitor Results \ No newline at end of file + +1 - Identify or Formulate Problem +2 - Data Preparation +3 - Data Exploration +4 - Transform and Select +5 - Build Model +6 - Validate Model +7 - Deploy Model +8 - Evalute or Monitor Results