KI TAKES STATISTICAL EVALUATION TO A NEW LEVEL
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Data analysis, visualization and AI
The art of collecting and analyzing data.
Understanding data through visualization
Data is described as the gold of the Internet age, but what is really behind it? Data has been collected for a very long time in order to analyze it in statistics. There are 2 sticking points:
- What data is collected?
- What is the connection between the data and how is it evaluated?
Google started the data collection frenzy that has since coined the term big data. Today, it is not only Google that collects data, but even our dental machine. AI has found ways to optimize statistical analysis in order to assign new data in a meaningful way. In the context of big data, the big challenge is to process disorganized data in order to either visualize it in various forms or use it for forecasting purposes.
The greatest challenge in statistics has always been to compare meaningful data sets. In order not to produce nonsense, it is necessary to relate to the real problem.
Good analytical skills are required to implement this. Visualization primarily serves the purpose of understanding. The various forms and possibilities of visualization are also of great importance in marketing. We associate different models with specific topics. At the same time, libraries have been created in programming that allow enormous creativity in the creation of statistical graphics.
Examples generated with d3.js (Data Driven Documents)
Where can I provide support?
Visualization of the data
I work with the Javascript library D3.js (Data Driven Documents) for the visualization of data on the Internet, which can draw various forms of SVG (Scalable Vector Graphics). I can advise you on how best to collect the data. At the same time, I offer you the service of presenting this data online in the most comprehensible form and integrating it into websites. I can also advise you on the use and structuring of databases.
Types of databases are:
– Relational databases (SQL derivatives such as MySQL and Oracle)
– Hierarchical databases (XML databases, JSON databases such as MongoDB, LDAP databases)
– Document databases
All database types have their justification and areas of application. They behave very differently when inserting, updating and querying data records. I would be happy to advise you on data modeling.
Processing large quantities of unorganized data records
Read more about this topic in this blog article on the topic of unorganized data sets with AI.
My approach
Online visualization of data
If a database already exists, I will be happy to make suggestions as to how it can best be presented online in various charts. If charts are to be integrated into an existing website, I will check in advance whether this will work. If the website is also to be redesigned, this needs to be integrated into the concept from the outset. The collected database may need to be expanded in the course of a business analysis.
Collecting the data
Let me know what you want to achieve with data and which systems are already in use. Based on this, I will make you a proposal as to where the data should come from and what data is required for this purpose. It should be noted that the collection of private data is becoming increasingly regulated by law. This is not fundamentally good or bad. It requires correct, legally compliant handling and confirmation/consent from the person disclosing their data.
Storing the data
Storing data in a database is a science. When dealing with relational databases, it is usually a matter of normalizing data. I would be happy to advise you on which database is appropriate for the corresponding use case. Speed and availability with regard to hosting (today mostly in cloud environments) must be taken into account. At the same time, security aspects need to be checked.
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