The goal of the analysis is to gain a compact insight into the patterns and dependencies in the data and possibly use this as a basis for forecasts.

(a)  

are the basis of all modeling/learning. Fundamental concepts are observational units and variables. The values or realizations of the variables are measured (collected) for each unit, resulting in the data or observations.

(a)  

Often used as an alternative term for (a)   for decision making, especially in a business intelligence context. Emphasizes combination of statistics with computer programming as well as focus on continuous reporting and predictions.

deals with raw business data, typically structured data, and provides insight and information for business decision making.

(a)  

utilizes more advanced statistical methods and analytics modeling than BI and often deals with much more complex and unstructured data types.

(a)  

is the science and art of collectng, analyzing , and drawing conclusions from data.

(a)  

is an object described in a set of data. (a)   can be people, animals or things.

an attribute that can take different values for different individuals

(a)  

assigns labels that place each individual into a particular group.

(a)  

takes number values that are quantities-counts or measurements

(a)  

generally takes value that vary. We are interested in the pattern that variation.

(a)  

tells us what values a variable takes and how often it takes those values.

(a)  

Categorial variable"__________"Bar graph of"________"

(a)  

Quantitative variable"__________"Dot plot of"________"

(a)  

is the process where data is collected and presented in a summarized format for statistical analysis and to effectively achieve business objectives.

(a)  

is the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis

(a)  

is a method to determine the statistical relationship between a dependent variable and one or more independent variables.

(a)  

a statistical method of estimating values from a set of observations by minimizing the sum of the squares of the differences between the observations and the values to be found.

(a)  

are built to predict what users might like, especially when there are lots of choices available

(a)  

It primarily makes recommendations based on inputs or actions from other people (rather than only the user for whom a recommendation is being made).

(a)  

This strategy involves creating user groups by comparing users’ activities and providing recommendations that are popular among other members of the group. It is useful on sites with a strong but versatile audience to quickly provide recommendations for a user on which little information is available.

(a)  

This is a specific type of the one mentioned above, otherwise known as “Users who looked at X also looked at Y.” Implementing this type of recommendation system is a matter of looking at purchasing sequences or purchasing groups, and showing similar content. This strategy is useful for capturing recommendations related to naturally complementary content as well as at a certain point in the life of the user.

(a)  

systems make recommendations based on the user’s purchase or consumption history and generally become more accurate the more actions (inputs) the user takes.

(a)  

As the most basic type of content-based recommendation system, this strategy involves recommending content that is close based on its metadata. This approach makes sense for catalogs with a lot of rich metadata and where traffic is low compared to the number of products in the catalog.

(a)  

Going one step further than the content similarity approach, the crux of this strategy is inferring individuals’ inherent interests by assuming that previous choices are indicative of certain tastes or hobbies. Where the previous strategy is based on explicit, manually filled catalog metadata, this strategy hinges on discovering implicit relationships. This is done by using the history of users’ larger interactions (e.g., movie watched, item purchased, etc.) to learn these tastes.

(a)  

This is a variant of the Latent Factor Modeling strategy, whereby instead of considering users’ larger actions, one would infer interests by analyzing unstructured text to detect particular topics of interest. It is particularly interesting for use cases with rich but unstructured textual information (such as news articles).

(a)  

This involves highlighting product recommendations based on the product’s intrinsic features that may make it interesting to a wide audience: price, feature, popularity, etc. This strategy can also take into account the freshness or age of the content and thus enable using the most trendy content for recommendations. This is often used in cases where new content is the majority.

(a)  

Extremely simple and critical but often overlooked, the first step in building a recommendation system is defining the goals and parameters of the project. This will most definitely involve discussions between and input from both the data team as well as business teams (which might be product managers, operations teams, even partnership or advertising teams, depending on your product).

(a)  

The best recommendation systems use terabyte(s) of data. So when it comes to rounding up data to use for your recommendation systems, in general, the more the better. This can be difficult if users are unknown when you’re trying to make a recommendation for them — i.e., they’re not logged in or, even more challenging, they’re brand new. If you have a business where most users are unknown, you may need to rely on external data sources or general data not explicitly tied to preferences, like demographics, browsing history, etc.

(a)  

is anything that requires user effort, like leaving a review/rating or initiating a complaint or product return (often from customer relationship management, CRM, data).

(a)  

is information that can be gathered about a user’s preferences without them actually specifying those preferences. For example, past purchase history, time spent looking at certain offers, products, or content, data from social networks, etc

(a)  

One thing to consider when exploring and cleaning your data for a recommendation system, in particular, is changing user tastes. • Consider only looking at features that are more likely to represent the user’s current tastes and removing older data that might no longer be relevant or adding a weight factor to give more importance to recent actions compared to older ones. Datasets for recommendation systems can be challenging to work with because they are commonly high dimensional, but at the same time, it’s also common that many of the features don’t have any values, which can make clustering and outlier detection difficult.

(a)  

Given the work done in the previous steps, you could have already built a recommendation system, simply by ranking those scores by users and you’ll have products to recommend. This strategy doesn’t use machine learning or a predictive element, but that’s totally fine. For some use cases, this is sufficient.

(a)  

is a collaborative filter based on measures of association between items or users.

(a)  

recommends typical products popular across your customer base to new website visitors.

(a)  

is a way to create latent factors representing groups of items or families of items.

(a)  

automatically recommend related items as you browse or place an item in the cart.

(a)  

groups of similar users or items together to streamline analysis of massive data matrices.

(a)  

estimates preferences for new items based on average difference in preference value (ratings) between a new item and the other items a user prefers.

(a)  

is the process of examining raw datasets to find trends, draw conclusions and identify the potential for improvement.

(a)  

uses current and historical data to gain insights, macro and micro, and support decision-making at both the patient and business level.

(a)  

refers to analysis of the data using quantitative and qualitative techniques to be able to explore for trends and patterns in the data — to “acquire, manage, analyze, interpret and transform data into accurate, consistent and timely information.”

(a)  

(AHIMA) stands for?

(a)  

refers to “a collaborative activity that involves people, processes and technologies” to use the information derived from data analytics to “improve the delivery of health care services and improve patient outcomes.”

(a)  

apply their knowledge of information systems, databases and information technology to help design effective technology systems that gather, store, interpret and manage the data that is generated in the process of providing health care to patients.

(a)  

find ways to capture and use the data that is acquired by health information technology systems, within the health system or from external sources, and then display it in meaningful ways through graphs, charts, etc. to help demonstrate how an organization can improve clinical care and decision-making.

(a)  

is any data relating to the health of an individual patient or collective population. This information is gathered from a series of health information systems (HIS) and other technological tools utilized by health care professionals, insurance companies and government organizations.

(a)  

There are a variety of tools and systems used to collect, store, share and analyze health data gathered through various means. These tools include:

(a)  

How Health Care Analytics Improves Patient Care

(a)  

uses historical data to draw comparisons or discover patterns. This type of analysis is best for answering questions about what has already occurred. We can gain insight into the past with (a)   .

(a)   allows us to understand why it happened and answer questions such as:

• Why did these patients go to the hospital last week?

• Why did patients leave home therapy?

• Why do patients not meet BMM targets?

These types of questions allow us to dig into the data a bit more, usually to understand or explain the answers found in descriptive analytics.

uses current and historical data to make predictions about the future. The models created with this type of analytics are best for answering questions about what could happen next. We can gain insight into the future with (a)   .

will also make predictions about future outcomes. Machine learning is a big factor with this type of analytics. The information provided can help determine the best course of action. We can gain insight on what course of action should be taken to reach the most ideal outcome with (a)   .

A LinkedIn search for jobs in health care analytics reveals thousands of results, including such job titles as:

(a)  

This includes a variety of employers ranging from health care organizations and providers to technology companies, universities and government agencies.

(a)  

Health Data Analyst Responsibilities One employment website that is seeking health data analysts lists some of the key job responsibilities as follows:

(a)