## Data Analytics Simplified – A Tutorial – 2

February 5, 2016 by mivuletech

**Data Analytics Simplified – A Tutorial – Part 2**

**By Kato Mivule**

Part 1

**Keywords: **Data analytics, Database querying

Types of Data Analytics

While data analytics might involve querying a database, the difference between data analytics and standard database querying for information can be described as follows [1]:

*Query**:* The search query might not be well formulated in data analytics but is always well formulated for database queries.
*Data*: The data is usually well organized for better analytics results that is, cleaned and preprocessed for analytics; for example removing missing values. However, for database queries, the data is not necessarily cleaned before querying.
*Results*: While basic descriptive statistics could be derived from querying a database, data analytics results are usually the statistical analysis information patterns of the data.

**Data Analytics Algorithms and Models**

*Algorithms*

- Data analytics involves applying algorithms to derive information patterns from data [1].
- An algorithm is a step-by-step process to accomplish a certain task. In data analytics, algorithms are used in effort to fit a model (classification) to the data being analyzed [2].

*Data Models*

- A data model is conceptual design that assumes how the data will be categorized or classified [3].
- In other words, a data model in this case is presupposed copy of what is expected of the data being analyzed [4].

Therefore data analytics involves the following tasks [1]:

- Using various computation algorithms to extract meaningful information patterns in data.
- Creating models for extracting meaningful unknown patterns of information.
- Using data analytics algorithm in attempts to fit a model to the data being examined.
- Using computation algorithms that assess the data and determine the model that best fits those characteristics of the data being observed.

Additionally, data analytics algorithms are made up of three components [1]:

*Models*: The aim of the algorithm is to fit the model to the data being analyzed.
*Conditions*: A set of conditions is used to select and fit a model on the data.
*Data Exploration*: Data analytics algorithms involve exploration of the data being analyzed.

Furthermore, data analytics can be divided into two major categories [1]:

*Predictive analytics* – involves making future predictions using the data being analyzed.
*Descriptive analytics* – involves learning new unknown patterns in the data being analyzed without making any future predictions.

**References**

##### [1] Margaret H. Dunham, “Data Mining: Introductory and Advanced Topics”, Prentice Hall, 2003, Page 3.

##### [3] Paulraj Ponniah, “Data Modeling Fundamentals: A Practical Guide for IT Professionals”, John Wiley & Sons, 2007, ISBN:9780470141014, Page 360.

### Like this:

Like Loading...

*Related*

on February 15, 2016 at 4:39 PMData Analytics Simplified – A Tutorial – 3 | Kato Mivule's Tech[…] « Data Analytics Simplified – A Tutorial – 2 […]

on March 4, 2016 at 7:00 PMData Analytics Simplified – A Tutorial – Part 5 – The Data Analytics Process | Kato Mivule's Tech[…] 1, Part 2, Part 3, Part […]

on April 1, 2016 at 6:23 PMData Analytics Simplified – A Tutorial – Part 6 – Data Analytics Challenges | Kato Mivule's Tech[…] 1, Part 2, Part 3, Part 4, Part […]