Data Science: Turning Data Into Information

Data Science: Turning Data Into Information

The professional world is transforming at an unprecedented rate. Digitization is the reason behind this, which constantly requires new job descriptions due to the development of modern technologies. Big data – dealing with a large amount of data is increasingly important. Therefore, businesses are desperately searching for experts in this field, such as data scientists. A data scientist is responsible for exploring, analyzing, and interpreting large amounts of data and deriving helpful information from trends and development to support a company’s strategic decisions. This article will explore what a data scientist does and how to learn data engineering and become a data scientist.

What does a data scientist do?

A data scientist must wear multiple hats within an organization and work closely with different areas of the company. If there is some problem, the data scientist needs to search for clues like a detective. His job requires combing through the data for relevant information, its evaluation, and interpreting it so that the respective department gets an answer to their question and makes their decision on its basis. Moreover, the data scientist usually significantly influences the company through his work and bears a large share of the responsibility.

In data science, the procedure is as follows:

  1. Selecting the right data: In the initial phase, the data scientist is responsible for collecting data material from different sources and organizing and filtering it. To identify the correct data source, a data scientist must ask himself the following questions: What data must be analyzed to find the correct answer? Under what aspect does the analysis take place?
  2. Analyze Data: The chosen data will be then examined and processed to answer data-related questions. To do this, the data scientist performs complex data analysis which helps them gain valuable insights.
  3. Document results: In the next phase, all results and findings are appropriately noted and documented.
  4. Interpret results: The data scientist must also interpret the analysis and derive conclusions, which he needs to present. In addition, he’s also responsible for future forecasting based on his findings, as well as suggesting possible next steps for the organization.
  5. Communicate results: For other departments to understand the results, the data scientist must present them in the simplest way possible and “convert” them into understandable language. He is responsible for creating reports and dashboards, visualizing the data using tables, graphics, diagrams, etc., and recommending actions.

Data Scientist vs. Data Analyst

Data scientist and data analyst professions are pretty similar, but there is one significant difference. While the data analyst is only responsible for analyses, the role of the data scientist goes one step further, including deriving conclusions from the obtained results. So his responsibility not only requires searching for an answer but also working across the board, which is why he must understand the business relationships in a company.

How do you become a data scientist?

There are multiple ways to become a data scientist. Since the profession is relatively young, the training paths to learning data engineering are inconsistent but offer great variety and several opportunities. For instance, you may become well suited for this job as a developer, computer scientist, or business scientist with further training in IT. In addition, data engineer certification, academic education in data science, statistics, business analytics, mathematics, or similar is also recommended.

However, a degree is not necessary – there are also several data engineer certification courses and further education for career changers with which one can easily qualify for the profession. In addition, various training programs are also available, which can help learn data engineering. It is also recommended to take further training in online marketing and e-commerce since these topics will increasingly be used in data analytics in the future. There is no “best” path to data analytics professions, as there are high degrees of specialization here. But this is the exact reason that makes the job so exciting.


If you are looking forward to becoming a professional data scientist, terms such as machine learning, data engineering, statistical modeling, AI, data visualization, and data mining should not be foreign to you. In addition to a comprehensive computer science and programming knowledge, you also need basic knowledge of business administration. Moreover, you should also have an understanding of statistical programming languages like R or Python, as well as database languages like SQL. To become a data science expert, you should also have good statistical and mathematical knowledge, management skills, organizational talent, quick learning skills, and communication skills.

Data analysis also requires analytical thinking skills. Comprehensive knowledge of Business Intelligence (BI) tools, data integration, preservation, acquisition, and data governance is also essential. This also includes legal and ethical aspects of data processing. Since the digital world is constantly transforming, a data scientist never stops learning. It is therefore essential that you are always willing to learn.

Wrapping Up

The demand for data scientists is booming at an unprecedented rate. Aspiring data scientists and career changers can quickly get a foot in the door by starting as data analysts and building experience from there. If you are looking forward to becoming a data scientist, you can opt for data engineer certification from Herovired and start your specialization step by step.