The job of a data scientist is in greater demand than ever in the business world. But what must a data scientist be able to do? Of course, this includes knowledge of data processing and statistics, but is that all? First, let’s take a look at the definition of data science.

WHAT IS DATA SCIENCE

Data science is a term for systems, algorithms, methods, and processes with which knowledge is extracted from existing data. This is based on theories and techniques from the fields of IT, mathematics, probability theory, and statistics.

The data analysis with the aid of computer-aided methods began as early as the 1960s. However, its application was limited to the scientific field for a long time. It was not until the widespread use of IT in the 1990s that data science found its way into companies. In particular, data was used at that time to derive marketing measures and corporate strategies. Now many research institutions, large corporations, and small and medium-sized companies need data scientists.

WHAT SKILLS DOES THE DATA SCIENTIST NEED?

A data scientist has to deal with problems on different levels in every Data Science project, for example, data access does not work out as planned or the data has a different structure than expected. A data scientist may spend hours debugging his source code or learning new Data Science packages for the chosen programming language. Also, the right algorithms for data analysis have to be selected, properly parameterized, and tested, sometimes it turns out that the selected methods were not the optimal ones.

SQL SKILLS AND TECHNIQUES

A data scientist works primarily with data, and this data is rarely stored directly in a CSV file, but rather in one or more databases that are subject to their own rules. In particular, business data, for example from the ERP or CRM system, is stored in relational databases, often from Microsoft, Oracle, SAP, or an open-source alternative.

A professional in data science needs to know SQL, should be also aware of the importance of relational relationships, to know the principles of normalization. Other types of databases, so-called NoSQL databases (Not only SQL) are based on file formats, a column-oriented or a graph-oriented approach. Examples of widespread NoSQL databases are MongoDB, Cassandra, and Neo4J.

A data scientist must therefore be able to cope with different database systems and at least have a very good command of SQL – the quasi-standard for data processing.

ACCESSING THE DATA FROM DATABASES

If data is available in a database, a data scientist needs to know how to export data from it. For one-off actions, export as a CSV file may be sufficient, but even here parameters have to be considered, such as meaningful separators, encoding, text qualifiers, or splits for particularly large data. For direct data connections, interfaces such as REST, ODBC, or JDBC come into play. Some knowledge of socket connections and client-server architectures sometimes are also very useful. Furthermore, every data scientist should be familiar with synchronous and asynchronous encryption methods.

PROGRAMMING LANGUAGES ARE ALSO VERY IMPORTANT

Programming languages are tools that data scientists use to automate processing data. Despite the fact data scientists are usually not real software developers, a certain basic knowledge of software architectures can be very helpful. So, it is very important to understand object-oriented programming.

KNOWLEDGE OF DATA SCIENCE TOOLS

Once a data scientist has loaded his data into his favourite tool, for example, one from IBM, SAS, or an open-source alternative like Octave, his core work is just beginning. These tools are not necessarily self-explanatory, and that’s another reason why there are a variety of certification offerings for various data science tools.

Anyway, data science is not simply an operator of tools, but he or she must use the tools to apply his or her analysis methods to data that he or she has selected for the specified objectives.

CONCLUSION

In conclusion, it must be noted that Data Science is diverse, and data scientists usually need to know much about the sphere they are working in. If you want to do analyses for businessmen, engineers, scientists, medical professionals, lawyers, or other interested parties, you also need to be able to understand them. Well, even casinos, possibly also National casino Canada sometimes hire data analysts for analysing, evaluating, forecasting, and reporting on various aspects of the casino.

So, what data science looks like in professional practice certainly depends a lot on whether it is applied in a company or science. The range of tasks of a data science all-rounder covers more than just the core area.