What am I, a data analyst or data scientist?

We understand. Big Data Analyst is translated as a big data analyst by whom I work, and Data Scientist is translated as a specialist in the study or processing of data. Yandex Zen gives such a formulation and breaks down abilities into such criteria. Differences between a data analyst and a data scientist: what is the difference between specialties (yandex.ru)

I will talk about myself based on this table.

My education is technical, specialty – information security, I was trained to work in the PolyAnalyst data analytics system. I have not studied programming, which I do not need. I have mathematical knowledge.

At work, I analyze large amounts of information provided by the company. I visualize analytical conclusions and build charts in the company’s BI system. I don’t know Python and I don’t process data in it.

A data scientist processes a variety of data. The data with which it works conditionally can be divided into several key groups.

1. structured data represents factual and accurate information. Most often they are presented in the form of letters and numbers that fit well into the rows and columns of tables. Structured data usually exists in spreadsheets like Excel or Google Sheets files. Structured information includes data received from a cash register or from other devices.

2. semi-structured data is a subset of structured data. These include messages that come to e-mail, statistical data from certain event trackers.

3. Unstructured data does not have a predetermined structure and is presented in a wide variety of forms. This is video, sound, images. This includes text files such as DOC or PDF. One type of unstructured information is social media posts.

Due to the fact that most of the information data is not structured, there are some difficulties with the analysis. And to achieve the desired result, the Data Scientist uses machine learning (Machine Learning) and deep learning (Deep Learning), or other technologies. This allows you to find the required data, as well as identify hidden patterns. I also use all types of data in my work, but I don’t build forecasts, I aggregate, summarize and process data at the moment. I display the results of my analysis on the dashboards of the PolyAnalyst analytical platform.

The last point for comparison remains – soft skills (flexible skills). What it is? We find out.

Flexible or overprofessional skills (also English, soft skills) is a set of general skills closely related to personal qualities. They include the ability to organize teamwork, negotiate and negotiate with colleagues, creativity, the ability to learn and adapt to change.

Analytical work often involves working in a team, especially on a large project, interacting with other employees, collecting information for analysis. Adaptation and the ability to negotiate are simply necessary for communication with the client, the ability to understand the goals and objectives of the project, the requirements and wishes for working on it. I also have flexible skills.

Summing up: I turned out to be a 100% data analyst according to all the criteria stated in this table, since I have all the professional competencies necessary for successful work in my field.

Our progressive world does not stand still. And in order to be in demand on the wave of development, to bring progressive methods of analysis into your work, you need to constantly learn new things and gain innovative knowledge.

I am currently reading a bookMeasure the most important John Dorr and take a course “Basics of working with DataLens”.