Guest lecture at Moringa School on Software Engineering concepts for Data Science

1. SoftwareEngineering
for Data Science

2. whatisdatascience?
"A data scientist is a statistician who lives
in San Francisco"

4. understanding data-miningprocess
CRISP-DM
Cross Industry Standard Process for Data Mining
Conceived in 1996
Describe 6 high-level analytics process
Current Version CRISP-DM 2.O

6. agilesoftwaredevelopment
Supports incremental development
Use iterative work cadences, known as sprints
Fits with CRISP-DM methodology
Allows feedback loop in development
“software should not be developed like an automobile on an
assembly line, in which each piece is added in sequential

7. languagesupport
SCRIPTING VS COMPILED LANGUAGES
SCRIPTS
Interpreted not compiled
Loosely typed
Can run with errors
Perfect for prototyping & incremental development
Examples: Python, Javascript, PHP, Ruby, R

8. languagesupport
COMPILED LANGUAGES
Strict syntax
Compiled language
Use an underlying framework
Examples: C,C++, Java

9. pythonandr?
Awesome data structures (data frames,vectors,matrices)
Incremental programming
Statistical packages
Web integration (databases,websites, APIs)
Good for quick and dirty work
Can be modularized
Easy to read syntax

10. BESTPRACTICES
Write rough code (prototyping/proof of concept)
Abstract and separate code into functions
Group functions into library/package

11. LET’SLOOKATSOMECODE