Data Scinece As A Field Of Knowledge

Question 1

Machine learning

Answer 1

A subset of artificial intelligence that uses statistical techniques to enable computer systems to learn from experience without being explicitly programmed

Question 2

Supervised learning

Answer 2

A model with a known target variable, that “trains” the data to learn how to predict the target variable.

Question 3

Unsupervised model

Answer 3

A model with no known target variable; this type of model is free to make connections between data points without having to consider an outside target variable.

Question 4

Exploratory data analysis (EDA)

Answer 4

The process of exploring a data set to discover insights, I identify patterns, establish relationships and trends, and test assptions.

Question 5

Bias

Answer 5

Tendency of a sampling method to overestimate or underestimate the value if an underlying population parameter.

Question 6

Target variable

Answer 6

Typically, a component of a business problem or objective.

Question 7

Fixed data set

Answer 7

Does not recieve new data regularly.

Question 8

Interpretability

Answer 8

How easily a human can understand or predict a decision or result.

Question 9

Iteration

Answer 9

Function that repeats in a specified order, often until a specific result occurs.

Question 10

iterative

Answer 10

refers to a process where the design of a product or application is improved by repeated review and testing

Question 11

Anomalies

Answer 11

data points that stand out from other data points in the data set and don’t confirm the normal behavior in the data. (They deviate from the data set’s normal behavioral patterns.)

Question 12

Outlier

Answer 12

a single data point that goes far outside the average value of a group of statistics. It is markedly different from the norm in some respect.

Question 13

Data Visualization

Answer 13

is the representation of data through the use of common graphics, such as charts, plots, infographics, and even animations. to communicate complex data relationships and data-driven insights in a way that is easy to understand.

Question 14

Features

Answer 14

an individual measurable property within a recorded dataset.

They are often called “variables” or “attributes.” Relevant features have a correlation or bearing (called feature importance) on a model’s use case.

Question 15

Relevant Features

Answer 15

have a correlation or bearing (called ____ importance) on a model’s use case.

Question 16

Spurious correlation

Answer 16

when two variables appear to be correlated but are not.

Question 17

Overfitting

Answer 17

occurs when the model cannot generalize and fits too closely to the training dataset instead

Question 18

Lean startup

Answer 18

a method for establishing a new company or product that focuses on customer feedback and flexible development over traditional business planning.

Question 19

minimum viable product

Answer 19

is a development technique in which a new product or website is developed with sufficient features to satisfy early adopters. The final, complete set of features is only designed and developed after considering feedback from the product’s initial users.

Question 20

model validation

Answer 20

is the process that is carried out after Model Training where the trained model is evaluated with a testing data set.

Question 21

Principles behind the Agile Manifesto

Answer 21

1. Highest priority is to satisfy the customer through early and continuous delivery of valuable software.
2. Welcome changing requirements, even late in development.
3. Deliver working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale.
4. Business people and developers must work together daily throughout the project.
5. Build projects around motivated individuals. Give them the environment and support they need and trust them to get the job done.
6. The most efficient and effective method of conveying information and within a development team is face-to-face conversation.
7. Working software is the primary measure of progress.
8. These processes promote sustainable development. The sponsors, developers, and users should be able to maintain a constant pace indefinitely.
9. Continuous attention to technical excellence and good design enhances agility.
10. Simplicity–the art of maximizing the amount of work note done–is essential.
11. The best architectures, requirements, and designs emerge from self-organizing teams.
12. At regular intervals, the team reflects on how to become more effective, then tunes, and adjusts its behavior accordingly.
    https://agilemanifesto.org/principles.html