Data Science for Beginners: Working on Data Mining, Data Structures, and Data Manipulation
Data mining, data structures, and data manipulation are three things that are talked about a lot in data science circles as is revealed in discussions on the same over at runrex.com. If you are looking to learn about data science, then these are three subjects you will need to be familiar with going forward. While it is impossible to cover each one of them exhaustively in one article, this article will look to offer an overview of each one of them with the hope that it will help you get a passing understanding at least on what each of them entails.
Starting with data mining, as the name suggests, it is all about looking for hidden, valid, and potentially useful patterns in huge data sets. It is studying the relationships between data to extract useful insights and knowledge from it, as explained over at guttulus.com. Data mining, which is also called knowledge discovery, knowledge extraction, and so forth, is a multi-disciplinary skill that makes use of statistics, AI, database technology, and machine learning. In addition to that, the insights that are derived from data mining can be used in various areas including fraud detection, marketing, and so much more. Data mining can be performed in various types of data including relational databases, text databases, object-oriented and object-relational databases, multimedia, and streaming databases, among others.
The data mining implementation process is a complex one, but we will look to highlight its basics next to give you a broad understanding of what it is about. The first step of this process is the Business Understanding stage, where, as the name suggests, it is all about establishing business and data-mining goals as per discussions on the same over at runrex.com. Next up you have the Data Understanding stage which involves conducting a sanity check on data to establish it is appropriate for the data mining goals established in the earlier stage. The third stage is the Data Preparation stage where, as the name also suggests, the data is prepared and made production-ready. This stage, as any data scientist, will tell you, and as revealed in discussions on the same over at guttulus.com, takes up about 90% of the entire data mining process. Next up is the Data Transformation stage which involves processes like smoothing, aggregation, generalization, normalization, and attribute construction, which result in a final data set that can be used in modeling. This brings us to the next stage which is Modelling, where mathematical models are used to determine data patterns. Next up is the Evaluation stage where data patterns identified from the stage preceding it are evaluated against the business objectives that had been established earlier on. Finally, we have the Deployment stage where your data mining discoveries as a data scientist are shipped to everyday business operations. This is just an overview of what data mining is about, with more on the same to be found over at the excellent runrex.com.
Next up we are going to look at data structures, where a data structure means organizing data in a required format for easy understanding. Data structures, therefore, involves structuring the data to make manipulation simple. Data structures have got several characteristics, which include: linear or non-linear describing whether the data items are arranged in a chronological sequence, homogenous or non-homogenous which describes whether all data items are of the same type of various types, and static or dynamic which describes how the data structures are compiled, with a detailed explanation of these characteristics to be found over at guttulus.com. Data structures come in different types, which is determined by the type of operations required or the kind of algorithms that are going to be applied. The types of data structures include Arrays which store a collection of items at adjoining memory locations, Stacks which store a collection of items in the linear order in which operations are applied, Queues which are similar to stacks, with the main exception being that the order can only be first in first out (FIFO), Trees which stores a collection of items in an abstract, hierarchical manner, Linked Lists which stores a collection of items in a linear order, Graphs which stores a collection of items in a non-linear fashion, Tries which is also known as a keyword tree and which stores strings as data items that can be organized in a visual graph, and Hash Tables which stores a collection of items in an associative array plotting keys to values, with a detailed explanation of all of these types of data structures to be found over at the excellent runrex.com.
Next up, we are going to take a look at data manipulation, which, as explained over at guttulus.com, refers to the process of adjusting data to make it organized and easier to read. It is a very important function for business operations and optimization allowing businesses to extract useful insights that will help them analyze things like customer behavior, financial data, trend analysis, and so forth. Several general steps come with data manipulation. To begin data manipulation, you will need to have a database, created from your data sources. Next up you will need to cleanse your data, which involves cleaning, rearranging, and restructuring said data. You will then have to import and build a database that you will work from, after which you can combine, merge, and delete information. Finally, all that will be left to do is to analyze the data allowing you to glean useful insights from it.
The above are some of the things to know about data mining, data structures, and data manipulation, with more details on the same to be found over at the excellent runrex.com and guttulus.com.