The Importance of Programming in Data Analytics

Technologies have become a part of our lives; they literally surround us everywhere: television, the Internet, Internet banking, cellular communications, and so on. They help us save lives, do our homework, and even study. Today, we can forget about boring homework. The main IT trend is analytics. This is a fairly new profession, but it is one of tremendous importance. The amount of information generated by a person is increasing every year. A data analyst is becoming one of the most in-demand IT professions.

According to market research analysts, the demand for professionals is significantly greater than the supply. At the turn of the century, analysts collected data into tables and analyzed it manually. With the growth of the amount of information, the tools for its processing, storage, and analysis have also changed.

What is the Role of a Data Analyst?

Data and statistics

An analyst is a person for whom huge scattered streams of information lead not to horror but to a state of professional excitement.

A data analyst is a person who helps the team:

  • Make decisions more objectively, use facts and data (as opposed to opinion, intuition, and experience).
  • Look for points of growth for the product and business.

He analyzes the so-called big data and knowledge, skills, methods of how to process, classify, structure, store, transform data and present it in a convenient form to all those “in need.” Such analysts are called mathematicians, mathematicians-programmers, information analysts, and sometimes systems analysts too, but with knowledge of working with big data. A data analyst may find that using cloud ELK may help them with conducting analysis of data from a range of sources.

About The Advantages

The advantages include its relevance in the labor market, and such specialists belong to an elite tier of employees in their companies and receive a good salary. You can perform your duties not only in the office but also remotely. In addition, for people with an analytical mind, such work is an opportunity to realize their full potential. A pleasant and useful overview, useful acquaintances with influential people, and a special status in the management of the company.

A specialist needs a wide range of knowledge in the fields of:

  • economics and business processes
  • information technology, software, programming languages
  • business process automation
  • system analysis
  • statistical analysis
  • accounting department
  • lead manufacturing
  • auditing

The Role of Programming in Analytics

Laptop with code

SQL

Many SQL analysts and data analysts have never encountered programming in this language because SQL is primarily a Structured Query Language, not a full-fledged programming language. However, it does have software capabilities, albeit rather modest ones. In short, it is a powerful data analysis and transformation tool that allows developers to work with a variety of databases.

Other programming languages

In addition to SQL, data analysts need to know tools for statistical data analysis: these are highly specialized packages – SPSS, statistics, and various programming languages ​​- SAS, R, Python, which have functionality for data analysis and visualization, and very lightweight solutions like Gretl. And the most popular set of tools among analysts is, perhaps, Python + SQL. Despite the fact that Python is considered a universal programming language. For web development and the creation of special solutions it gained the greatest popularity in the field of Big Data.

Python has recently been increasingly used for data analysis, both in science and commerce. This was prompted by the simplicity of the language as well as great open-source libraries. Python offers countless tools for data analysis projects and can help you with any task in the process. While Python is considered to be one of the main languages ​​for data analysis, there are other options. R is a second language for data analysis and is often compared to Python. It is used for data and graph creation.

How to Become a Data Analyst?

Man staring at laptop

Most analysts come to Data Science from other, completely different fields. These are mainly developers and marketers, but there are also unexpected professional backgrounds: doctors, actors, biologists, sound engineers, and even physicists.

Most often, the study of analytics begins with the literature, feature articles, authoritative blogs, and specialized channels in messengers. There is a lot of theoretical information in the public domain, where you can collect a basic pool of theory and practice. And yet, to make the first step, a system is needed. It is easier and faster to immerse yourself in practical analytics in educational courses.

Data is very important information for any company. It can be used to predict customer behavior, track demand for a particular product, improve service, and increase sales. The processing of values and indicators has formed a whole profession – a data analyst.

IT is one of the fastest-growing industries in the world economy. Many specialists work in this area – programmers, system administrators, developers, testers, and data analysts. Today, in order to perform the tasks of an analyst in an IT company, it is not enough just to understand business processes – it is important to know how to implement them technically, to understand what databases can be used, and to know one or even several programming languages.

Of course, a company can hire a programmer. With whom you will communicate. However, it will be much better to do everything yourself – faster and more efficiently than hiring experts online to provide you with expert help with programming assignments and data analytics projects.

The work of an analyst is suitable for assiduous and sociable people with developed logical thinking because analytics work with systems, multiple systems and programs, data collection, and their further analysis. This takes a lot of time since the correctness of the decisions made depends on the completeness of the information received. Programming always comes to the rescue. Therefore, if you want to become a data analyst, get ready to learn to program.

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