Data science: what it is and how to become a professional in this area

More and more firms are waking up to the potential and power of data in enhancing all aspects of business concerning the consumer and inward. In that context, the role of a data scientist has emerged as one of the hottest in technology—as well as in much of the business world.

Making a difference in the long term, it makes all the difference in the world if you are hiring a data scientist with years of experience and refined skills, complementing great ideas to join your team.

As already stated, we will try to explain what data science really is, what a data scientist does, and how a company can benefit from such a professional. Stay tuned!

What is data science?

The general definition for data science would be a study on data, its origin, what it represents, and how it can be converted or transformed into invaluable input and resources to formulate business and IT strategies.

Why do companies need data science?

We’ve come a long way from working with small structured data sets to large mines of unstructured and semi-structured data from a variety of sources.

Consequently, data science comes with more advanced tools to work on large volumes of data coming from different types of sources, such as financial records, multimedia files, marketing forms, sensors and instruments, and text files.

What does a data scientist do?

Data scientists interpret and help companies deal with data, working out extensive problems which require expertise in different data niches. They have a strong background in computer science, modeling, statistics, analytics, and mathematics, together with leading business sense.

They are typically experts in technologies such as Hadoop, Pig, Python, and Java. Their work may focus on data management, analytical modeling, and business analytics . Because they tend to specialize in a narrow niche of the science, they often work on teams within a company.

In terms of credentials, it’s not uncommon for a data scientist candidate to have a PhD. However, in addition to that, they’re expected to incorporate professional skills such as communication, leadership, collaboration, creativity, discipline, and a driving passion for truth in information.

Data scientists can be true change agents within an organization, providing insight that can illuminate the company’s path toward its ultimate business goals. They are essential in supporting leaders and developers in creating better products and paradigms. And as their role in large companies becomes increasingly important, they are becoming increasingly scarce.

When is a scientist needed by a company?

The possibilities of data science are extremely diverse. As a result of changes in work and consumption patterns, new digital technologies and value chains, it has become a necessity in many companies.

It’s thus worth having a closer look at which questions data scientists really answer and what value they bring to the business and its stakeholders.

“What happened?”

Data is the documentation of facts or processes, such as business processes, program states, or results. Therefore, it is important to first understand what the data shows and what conditions are recorded.

On the one hand, this requires a certain understanding of the context, the problems, the company and the target states. On the other hand, the data scientist has to implement certain steps, such as the ETL process, on a technical level.

Within the ETL process, data must be extracted, integrated and transformed from various sources, such as IT systems or databases. This means that scientists first collect all relevant data together and transform it into a uniform and analyzable form.

In the next step, they load the transformed data into one or more tools, such as Microsoft Power BI or IBM Cognos Analytics, for their analysis. These tools can then be used to answer what happened. For example, are there any deviations from the target state? Bottlenecks or delays? Are there previously undiscovered weaknesses? The next question arises directly from these questions.

“Because it happened?”

Once data scientists have gained an impression of the current situation and identified any anomalies, the intuitive question arises: why? Why did this state or event occur? Depending on the context, different techniques are used to answer this and the previous question. Exploratory data analysis, for example, which is a branch of statistics and is mainly used when there is a lack of knowledge about the data and its connections.

For process data, i.e. based on a process, process mining methods will also be used. The “Process Discovery” and “Actual Target Comparison” techniques visualize the actual process and identify unwanted deviations.

“What will happen?”

This is perhaps one of the greatest questions to stakeholders in a company. In aid of past data, a data scientist predicts as accurately as possible to answer the same.

It is applied to estimate what, when, and how. Most of the time, this above-mentioned information is gathered using artificial intelligence methods, such as Machine Learning or Deep Learning. Machine learning is used to identify patterns or rules in data that form the basis for predictions.

These predictions accompany us through all aspects of our professional and private lives, from the daily weather forecast to the expected journey time on public transport or the estimated reading time for a blog post, to the expected stock price.

“What’s the best thing that could happen?”

Finally, data scientists answer the question of what the best conditions are for achieving this. This goes hand in hand with the question of how this can be achieved. Here, too, artificial intelligence is often used. This enables scientists to provide explicit, data-driven recommendations for action to stakeholders. For example, to avoid bottlenecks, delays or to optimize processes.

Data scientists must therefore not only deal with large databases, but also apply statistical methods and mining techniques to answer specific questions. At the same time, they need a certain understanding of industry-, company- or process-specific relationships. Knowledge of industrial engineering is particularly relevant for data processing.

Now you know what data science is and what a data scientist does. Data analysis is like a springboard for business transformation. And data scientists transform data into relevant business information . They are able to analyze and explain past transactions and business processes. They can use historical data to make accurate predictions. This not only creates transparency but also identifies optimization potential and possible strategies for achieving it.