s307 chapter 1

scrum

a process that uses small teams that meet in chunks to complete pieces of the projects, called "sprints" to complete work efficiently

Phases of Database Design

-Requirements Analysis: learning a companies business practices -Conceptual Design: a 'rough draft'/first attempt at structure -Logical Design: logical schema, capture every piece of data they need and represent it -Physical Design: coding process,actually making something -Implementation: making database -Maintenance: the database will evolve, this part helps the database adjust during that

Benefits of Database Approach

-less inconsistencies -less redundancy -data can be shared easily -security is enforced -more standardized

lean approach

-techniques aimed at eliminating waste -can be applied to all types of businesses -Built on ELIMINATING WASTE, JUST IN TIME INVENTORY CONTROL, AND ADOPTING THE 5S PRACTICES

3 Levels of AI:

1. Narrow: machines can perform some tasks better than humans 2. General: machines can perform several tasks at the same level as the average human 3. Technological Singularity: machines ca perform nearly every task superior to humans

3 Things we do with big data:

1. Nothing: very expensive 2. Study and Analyze it: the wealthiest companies in the world 3. Sell and Share it: (google, amazon)

foreign key

A primary key of one table that appears as an attribute in another table and acts to provide a logical relationship between the two tables

Descriptive Analytics

the ability to use data to gain insights: reporting, data visualization, dashboards, scorecards

Flat File System

Problems: order of application (difficult to extract values), no simultaneous access

MapReduce

Software framework that enables developers to write programs that can process massive amounts of unstructured data in parallel across a distributed group of processors. (thanks Google)

minimum viable product (MVP)

The most pared down version of a product that can still be released. The feedback from the users guide the future development of the product

5 V's of Big Data

Volume (large quantities) Variety Velocity (fast rate) Veracity (how to track accuracy and relevance) Value

Big Data

a collection of data from traditional and digital sources inside and outside your company that represent a source for ongoing discovery and analysis `

enterprise data model

a conceptual design of the database, you go back and get feedback on this and then tweak it , **you don't care about every piece of data at this point

Supervised Learning

all data is labeled, algorithms learn to predict the output from the input data

Unsupervised Learning

all data is unlabeled and the algorithms learn structure from the input data

artificial intelligence

any object that perceives its environment and takes actions to maximize its possibilities of success

Hadoop

big data companies like google needed a way to make sense of the large amounts of data their companies were processing, Hadoop allows big problems to be broken down into smaller pieces so that analysis can be done quickly and cost-effectively

Metadata

data that describes other data

a true AI system...

is one that can learn on its own!

redundant data

not necessary duplicated, but the same data stored in different locations

KNN Algorithm

stores all available cases and classifies new cases based on similarity measure

duplicate data

the exact row of data twice

Why don't we use file based data systems?

there is limited data sharing, program data dependence, and excessive development times/program maintenance

Prescriptive Analytics

use results of predictive analytics along with real-time data to make recommendations (machine learning)

Machine Learning

using mathematical models and algorithms to solve problems without explicit instruction

Predictive Analytics

using statistics and past events to predict what might happen in the future

Structured Data

very organized, often relational databases, easy to search and index ex: numbers, dates, text

Unstructured Data

very unorganized, text-heavy****can't be modeled ex: pictures, video, audio, webpages, emails, social media posts

functional decomposition

you meet with the client, they describe their processes and you take detailed notes to learn them