It depends directly on where this data can be obtained

[Mst.Rina1R]

I would like to point out that a web analyst does not need to master all the tools and technologies listed in the table. For example, the programming languages ​​R and Python are related to Data Science. Databases and SQL are also a separate area that does not intersect with web analytics. Finally, BI platforms solve business analytics (Business Intelligence) problems, and an internet analyst will be more likely to be their user than a developer.

In general, if your company needs to solve the tasks listed in the table, most likely you will have to hire several specialists at once, at least a web analyst, a database administrator (DBA), and a business analyst (BI) or a data specialist (Data Scientist). Finding a specialist with skills to work with all the above tools at once is not an easy task.

Web analytics counters are located on their own websites, but what to do if you don’t have your own data, but you need to conduct research?

The necessary information can probably be obtained either colombia telegram by parsing websites or from open data sources like the World Bank. Such tasks partly relate to the field of Data Mining, or rather to its section on data extraction. The same R language has quite extensive functionality for this, for example, the rvest and RSelenium packages. There is a good book on this topic by Dmitry Kharlamov, “Data Collection on the Internet in R”. If you need some data and this data is somewhere, then all that remains is to choose a tool with which you can get it.

How important is the "Garbage in, garbage out" principle? Are data sources a key factor in web analytics?

As for "garbage at the input", this situation is quite common, especially if the necessary data has to be obtained from external sources. Here again, there are a number of tools that will help bring the received data set into a form suitable for further analysis. If we talk about the R language, then it is worth paying attention to the tidyr package.

In order to avoid getting “garbage at the output”, before starting the analysis, it is necessary to clearly formulate the questions that you plan to get answers to during the analysis, otherwise you will most likely not get any results from conducting it.