![](https://csdnimg.cn/release/download_crawler_static/11135634/bg14.jpg)
Chapter 1 ■ an IntroduCtIon to data analysIs
2
At the end of the data analysis, you will have a model and a set of graphical displays and then you
will be able to predict the responses of the system under study; after that, you will move to the test phase.
The model will be tested using another set of data for which we know the system response. These data
are, however, not used for the definition of the predictive model. Depending on the ability of the model to
replicate real observed responses, you will have an error calculation and a knowledge of the validity of the
model and its operating limits.
These results can be compared with any other models to understand if the newly created one is
more efficient than the existing ones. Once you have assessed that, you can move to the last phase of data
analysis—the deployment. This consists of the implementation of the results produced by the data analysis,
namely, the implementation of the decisions to be taken based on the predictions generated by the model
and the risks that such a decision will also be predicted.
Data analysis is a discipline that is well suited to many professional activities. So, knowledge of what it
is and how it can be put into practice will be relevant for consolidating the decisions to be made. It will allow
us to test hypotheses, and to understand more deeply the systems analyzed.
Knowledge Domains of the Data Analyst
Data analysis is basically a discipline suitable to the study of problems that may occur in several fields of
applications. Moreover, in processes of data analysis you have many tools and methodologies that require
good knowledge of computing and mathematical and statistical concepts.
So a good data analyst must be able to move and act in many different disciplinary areas. Many of
these disciplines are the basis of the methods of data analysis, and proficiency in them is almost necessary.
Knowledge of other disciplines is necessary depending on the area of application and study of the particular
data analysis project you are about to undertake, and, more generally, sufficient experience in these areas
can just help you better understand the issues and the type of data needed to start with the analysis.
Often, regarding major problems of data analysis, it is necessary to have an interdisciplinary team of
experts made up of members who are all able to contribute in the best possible way in their respective fields
of competence. Regarding smaller problems, a good analyst must be able to recognize problems that arise
during data analysis, inquire to find out which disciplines and skills are necessary to solve the problem,
study these disciplines, and maybe even ask the most knowledgeable people in the sector. In short, the
analyst must be able to know how to search not only for data, but also for information on how to treat them.
Computer Science
Knowledge of Computer Science is a basic requirement for any data analyst. In fact, only one who has good
knowledge of and experience in Computer Science is able to efficiently manage the necessary tools for
data analysis. In fact, all the steps concerning the data analysis involve the use of computer technology as
calculation software (such as IDL, Matlab, etc.) and programming languages (such as C ++, Java, Python).
The large amount of data available today thanks to information technology requires specific skills in
order to be managed as efficiently as possible. Indeed, data research and extraction require knowledge of the
various formats. The data are structured and stored in files or database tables with particular formats. XML,
JSON, or simply XLS or CSV files are now the common formats for storing and collecting data, and many
applications also allow their reading and managing data stored on them. For the extraction of data contained
in a database, things are not so immediate, but you need to know SQL query language or use software
specially developed for the extraction of data from a given database.
Moreover, for some specific types of data research, the data are not available in a pre-treated and
explicit format, but are present in text files (documents, log files) or in web pages, shown as charts, measures,
number of visitors, or HTML tables that require specific technical expertise for the parsing and the eventual
extraction of these data (Web Scraping).
www.allitebooks.com