Big data analysis represents a pretty intensive study area with a profound effect on manufacturing and scientific-research fields which require deep analysis of massive data warehouses.
We shall identify the most obvious and in-demand ways to apply big data analysis to your advantage.
# Routing analysis
The first case we shall discuss is a regular analysis which would imply statistics in all the forms and formats.
It’s been well known that specialists of different market segments have to deal with data, studying, and processing it, analyzing the info and drawing conclusions. Nevertheless, we don’t really come to think about how confusing those processes are due to the fact that a high level of complexity is peculiar to the mentioned analysis techniques.
We’d like to distinguish certain methods that serve to assist with statistic survey and associated data analysis:
- stats monitoring;
- categorizing the findings of stats monitoring;
- absolute & relative statistic numbers;
- qualified auditioning;
- correlative & regressive research;
- behavior rows.
# Health-related studies
The presented method would be a perfect solution for medical app development since such research shall provide all data required to build a successful healthcare program.
After having analyzed clinical info, you’ll be capable of taking advantage of the following:
- possibility to collect data and plan clinical studies;
- a chance to visualize data by means of histograms;
- opportunity to determine statistically critical discrepancies between samples;
- possibility to examine drivers’ dependencies;
- opportunity to conduct survival analysis;
- a chance to calculate the needed size of samples;
- possibility to forecast medical therapy outcomes.
In this instance, we are to consider methods of classifications and neuronal solutions. In fact, the two given means represent a vital piece of cutting-edge machine learning technologies.
We shall review a specific case of how classification can tackle a specific issue.
There could be a case of having numerous pieces (situations) that get segregated into particular categories (let’s conditionally define those pieces as an initial set). In addition, you also have a final set of items and you understand what category they actually relate to. We shall call this set “the training sample”.
The question is what categories the rest of the items refers to.
To solve the riddle we’ll need to create a specific data analyzing algorithm that would manage to classify the random pieces from the source set.
The given algorithm could be successfully applied to remedy certain problems:
- assess obligors’ creditworthiness;
- predict customers’ outflows;
- recognize speech;
- detect spam;
- classify documentation.
# Web scraping
That would be one more way to take advantage of data analysis.
Broadly defined, web scraping represents the process of collecting info from diverse online resources. You’ll find the following categories of data valuable:
- catalogs of products;
- pictures of any kind;
- video files;
- website textual content;
- public contact data (say, emails, cellphone numbers, and the like).
# Social media reviews
Here we should not fail to mention the graph theory.
The mentioned theory falls within discrete math, and it’s applicable for resolving diverse issues that occur in various fields of operation. On that list, you can find programming, sociology, communicating, and economics.
Social graphs assist with such processes, as:
- identifying the audience;
- social searching;
- gathering open info to model graphs;
- generating recommendations for choosing the required media content;
- identifying the so-called “real” relationship.
When you apply data analysis for social media, you might experience certain difficulties with coded social data and distinctions between different social platforms.
# Detecting fraudulent transactions
Specific data analysis processes can also serve to identify financial fraud operations.
To achieve the desired, a particular fraud forecast shall be carried out. Such forecast shall have a certain confidence zone of the final results which would operate based on the quadratic law. Consequently, solely 2-3 forecast time gaps (meaning, 2-3 days) will provide accurate and reliable results.
The trouble is that the process of detecting fraudulent activities takes place sometime after they actually happen, thus, the calculations shall include compensation payments as well.
Taking into account everything mentioned about fraud identification, it will make more sense to, first of all, figure out the kind of payment: whether it was a usual payment, a sketchy one, or fraudulent. In this case, key elements in space must be classified applying the techniques of nearest neighbors. Neural networks could be used as well.
We fervently hope that our brief review of big data analysis applications will prove useful and valuable.