Introductory terms

Question 1

descriptive statistics

Answer 1

describe a dataset using mathematically calculated values such as mean and std deviation

Question 2

inferential statistics

Answer 2

statistical calculations that enable us to draw conclusions about the larger population

Question 3

Normal distribution

Answer 3

the mean sets the middle of the distribution and the standard deviation sets the width.

Question 4

Probability

Answer 4

the mathematical study of what could potentially happen; in data science probability calculations are used to simulate scenarios and build models, which help us understand data that has yet to exist.

Question 5

Programming

Answer 5

the act of giving the computer instructions to perform a task

Question 6

Clustering

Answer 6

a subsection of data science that allows us to classify data. Programming makes clustering data time-efficient

Question 7

Domain expertise

Answer 7

the particular set of knowledge that someone cultivates in order to understand their data. My domain expertise is the food system, agriculture, sustainability.

Question 8

Data science process (8 steps)

Answer 8

Ask a question; determine necessary data; get the data; clean and organize the data; explore the data; model the data + analysis; communicate findings; reproducibility and automation.

Question 9

a/b testing

Answer 9

a process of showing two variants of the same web page to different segments of website visitors at the same time and comparing which variant drives more conversions.

Question 10

Margin of error

Answer 10

amount results of survey differ from real population value. The larger the error, the less confidence we have in results.

Question 11

Confidence level

Answer 11

the probability that we were to run another survey with the same metrics that would get the same results. (90%, 95%, 99%).

Question 12

Population size

Answer 12

size of the population we’re collecting data on. A common number in sample size calculations is 100,000.

Question 13

Likely sample portion

Answer 13

the % of people surveyed whose results we anticipate matching the expected outcome. If we don’t have historical data, we normally use 50%.

Question 14

Active data collection

Answer 14

setting up specific conditions in which to get data. On the hunt. I.e.: running different experiments and surveys.

Question 15

Passive data collection

Answer 15

you’re looking for data that already exists. You’re foraging data. I.e.: Locating datasets and web scraping.

Question 16

data wrangling

Answer 16

Raw data can come in a variety of file types and formats. Data wrangling - cleaning and organizing datasets.

Question 17

Pandas (a python library)

Answer 17

a great tool for importing and organizing datasets. It can be used to convert spreadsheets (like CSV) into easily readable tables and charts known as DataFrames. We can also use Pandas to transform datasets by adding columns and rows to an existing table (merging).

Question 18

Statistical calculations

Answer 18

use descriptive statistics to get a sense of what it contains, including average, median, and standard deviation. We can use a python module known as NumPy (numerical python) to calculate descriptive statistics values.

Question 19

Data visualizations

Answer 19

enables us to see patterns, relationships, and outliers, and how they relate to the entire dataset. Especially useful with large datasets. Python data viz libraries like Matplotlib and Seaborn can display distributions and statistical summaries for easy comparison. The JavaScript library D3 enables the creation of interactive data visualizations, which are useful for modeling different scenarios.

Question 20

models

Answer 20

To analyze data, we create a model. Models are abstractions of reality, informed by real data, that allow us to understand situations and make guesses about how things might change given different variables.

Question 21

2 varieties of variables

Answer 21

quantitative and categorical variables

Question 22

Quantitative variables

Answer 22

Quantitative variables are variables expressed numerically, whether as a count (discrete) or measurement (continuous).

Question 23

Discrete variables

Answer 23

Discrete variables are numeric values that can only be integer values (counts). They represent whole units and are not decimals or fractions.

Question 24

Continuous variables

Answer 24

Continuous variables are numeric values that can be expressed with decimal precision. Examples are length, weight, and age.

Question 25

Categorical variables

Answer 25

Categorical variables group observations into separate categories that can be ordered or unordered. With categorical variables we want to understand how the observation in our dataset can be grouped and separated from one another based on their attributes.

Question 26

Ordinal variables

Answer 26

when the groupings have a specific order or ranking. For example, “strongly disagree” to “strongly agree,” customer satisfaction ratings, age groups, standings in a competition.

Question 27

Nominal variables

Answer 27

when there is not apparent order or ranking to the categories. For example, eye color, pet type, favorite good, hometown city.

Question 28

Binary variables

Answer 28

Binary variables are a special kind of nominal variable that only has two categories. True/false, or 0 and 1 for example.

Question 29

Git

Answer 29

a version control system (VCS), a google doc for coders; a project managed with git is called a repository. Github is a popular hosting service for Git repositories, which allows you to store your local Git repositories in the cloud.

Question 30

CAPTCHA

Answer 30

Completely Automated Public Turing Test to tell Computers and Humans Apart

Question 31

SQL

Answer 31

Structured Query Language - a programming language used to manage data stored in relational databases

Question 32

HTTP

Answer 32

Hypertext Transfer Protocol

Question 33

TCP

Answer 33

Transmission Control Protocol