Introduction to data science

Question 1

Webscraping tools: Readymade

Answer 1

import.io
ScraperWiki
Tabula
Google Sheets _=IMPORTHTML_
excel

Question 2

Web scraping tools: custom

Answer 2

R
Python
Bash
Java
PHP

Question 3

Steps to explore data

Answer 3

Review data
Check assumptions
Check Anomalies
Data Suggestions

Question 4

Exploratory Graphics

Answer 4

Coding: R, Python, JavaScript
Applications: Tableau, Qlik

Question 5

Exploratory Graphics

Answer 5

Bar Charts: for categories and can be grouped
Box Plots: for quantitative variables, in quartiles, show outliers
Histograms: show shape of distribution
scatter plot matrices

Question 6

Questions to ask in exploratory process

Answer 6

do you have what you need?
are there clumps or Gaps
are there exceptional cases
are there errors in the data

Question 7

Exploratory Statistics

Answer 7

Robust Statistics: stable, less effected by outliers, skewness, kurtosis
Resampling: empirical estimate of sampling variability, jackknife, bootstrap, permutation, cross validation

Question 8

Advantages of Excel

Answer 8

good for browsing, sorting rearranging, getting a visual picture, finding and replacing
more uses: formatting, transposing, making pivot tables

Question 9

SQL

Answer 9

First, SQL is the language used for getting data from relational databases. Second, a few commands go a very long way. And third, the data is usually pulled out of a database and then sent to other programs like R or Python for analysis.

Question 10

HTML

Answer 10

HTML, or HyperText Markup Language is the language of web pages; it’s the thing that says what a text is and what the headings are, and where to put links. And the information on a web page is styled with CSS which is for Cascading Style Sheets.

Question 11

XML

Answer 11

XML or Extensible Markup Language. This is a data encoding that is simultaneously human-readable and machine-readable, which is not always the case.