Kristijan Janušić, copywriting | BLOG

Blog o digitalnom marketingu, odrastanju, tehnologiji, web dizajnu, data science i nekim novim iskustvima.

It’s Almost 2017. and You Should Really Know The Importance of Data Science and its Future

Data science is a complex blend of several disciplines including technology, algorithm development, and data interference. The basic goal of data science is to solve analytically multiplex problems. As the name suggests, data is at the core of this specific type of science. In its essence data science is all about using different systems and processes, related to the disciplines we’ve mentioned before, in order to extract information, insights or knowledge from data available in different forms. Sometimes this data is unstructured and sometimes it is well-structured. All data possible can be of extreme use, if you find the right way to use it. Learn more about it in the following lines.

Just like many other types of science, data science is not focused on theories and techniques that are exclusively used in one field of knowledge. Namely, it relies on methods and theories from at least five large areas – computer science, information science, operations research, statistics, and mathematics. Data science also uses techniques and methods related to big data and even though this science is close to big data this is not the only field of interest of data science.

Data science has an impact on applied and theoretical research in many different areas like speech recognition, machine translation, search engines, robotics, medical informatics, digital economy, finance, economics, businesses, social sciences and health care. If you’re eager to learn more about data science, cyber security or computer science in general, I really recommend you start following Harbour.Space’s blog posts, courses and if possible events.

Harbour.Space University

Harbour.Space University

In the recent period, data science is often mentioned in the context of services. Although this use of data science is still fresh, it certainly provides good results which are why more and more people are interested in it. Those who have performed some research have probably noticed that there are many companies in the market providing services related to data science, but their definition is not clear.

There are many definitions of this service, but the simplest one is that data science represents a user-friendly analytics system/program that provides analytics data to managers and business users. The implementation of data science methods usually goes through external software solutions and tools with an ability to automate the process of analysis by relying on data storage from the organization.

Here at IN2Data Science Company, we really know how to use your data and advance your business and we’re eager to turn your data into action too.

Providing an example is the best way to understand how data science works and why is it so useful. For instance, a customer service center has basic systems that allow employees to check the customer’s name, email, phone, address whenever they are calling the center. In this way, the employee can see what this customer has bought in the past and they can skip the explanation in the beginning. However, with the help of tools that rely on data science, employees will be able to get more information about the caller like their return history, the ratings they gave to the company in different surveys, the amount of money they’ve spent on products/services etc. In other words, thanks to data science, the employee will not only figure out what the customer’s problem is, but they will also understand the frame of mind of each customer.


As previously mentioned data science covers many different areas and relies on different kinds of analysis. One of the processes that have a huge impact on data science is social media analytics. This specific type of analytics is part of social analytics and represents a process of collecting data that comes as a result of stakeholder interactions conducted on digital media.

Once the data is gathered, it is processed into well-structured insights that serve as a material that can help business owners and organization leaders make more sound decisions. Even though most people use the term social media analytics, it is not unusual to find this part of social analytics under few other names like social media intelligence, social media monitoring and social media listening. In its pure essence, this is my role at IN2Data and if you’d like to find out more about it; e-mail me at kristijan.janusic AT in2data DOT eu :-) 

Social media analytics uses dozens of different digital media sources to achieve the aforementioned goals. For instance: blogs, social media platforms, image sharing sites, online forums, video sharing platforms, classifieds, aggregators, Q&A, complaints, reviews etc. Social media analytics has proven to be very helpful and useful when users want to find out more about the patterns that are not clearly visible in a big amount of social data associated with specific brands. It is good to mention that social media monitoring tools don’t work equally for every company and that’s why it is highly recommended to perform tests before any of these tools is employed.

The current state of the job market in data science

According to many experts, a data scientist is one of the most promising jobs in the 21st century. Yet, many people avoid this profession because they believe that a successful data scientists must have skills and knowledge related to a wide range of fields including data mining, software development, machine learning, statistics, data visualization and databases. However, this doesn’t mean that people must be experts in all these fields in order to practice data science.

There are a few data skills that every data scientists is expected to know. For example, they must know how to use the so-called tools of the trade like the use of database querying language (SQL) or statistical programming language like Python. In addition, a good data scientist should also have knowledge in basic statistics – likelihood estimators, distributions, statistical tests etc. Linear algebra and multivariable calculus are needed regardless of the company where data scientists are applying. The same goes for machine learning.

Of course, those who want to join big corporations where large amounts of data are processed every day or any other company where the activities are data-driven should have a higher level of knowledge in this field.Having knowledge in data communication, data visualization, and software engineering is a big plus and in most cases a must for those looking to start their careers as data scientists.

Never underestimate the knowledge you could gather through Coursera or Udemy too. Having knowledge in data communication, data visualization, and software engineering is a big plus and in most cases a must for those looking to start their careers as data scientists.


It is interesting that data scientist jobs are still not clearly defined and many data scientists will learn whether they are qualified or not only after finishing their interviews. Obviously, the more skills you have, the greater chances for employment you’ll get.

The future of data science

It is very difficult to make predictions for a science that includes so many different disciplines. This means that you have to think about the current trends and perspectives for each of these disciplines. Yet, the past and the current state of data science should serve as a good starting point for making predictions for the future of data science.

So, in the past and today, data science is focused mostly on descriptive analytics. This means that data science relies on collecting information and describing what happened in the past. However, thanks to the rapid advance of technology, experts expect that in the future, data science will turn to more sophisticated kind of analytics including real-time and predictive analytics. Once again, the business sector will have a huge impact on the look and goals of data science.

It is also expected that machine learning as one of the basic elements of data science will be greatly changed. Instead of paying most of their attention to mechanics of this learning, data scientists will have to unleash their creativity and use different types of models. Although data scientists have a good level of productivity, if they want to remain competitive in the future they will have to boost their productivity and changing the way they practice machine learning is one way to do this,

As stated before, it is not easy to predict the future and the trends that we can expect, but some scientists believe that there are few things that simply must happen in the future based on our experience with data science so far.

First of all, we will witness the emergence of new sources of data. The Internet of Things is not something new, but the interconnection between devices that this concept supports will grow in the future leading to connections between different kinds of electronic devices. Today, data scientists are using clickstream data, purchase data and sales data, but in the future they will have to include data gathered from different retail environments, manufacturing streams, offices, vehicles, employees etc.

Furthermore, it is very likely that the tools used by data scientists today will become more advanced making complicated tasks look much simpler. This is actually something that we are already witnessing with so-called BI tools as well as with open-source libraries. Only a decade ago, many of the algorithms had to be created starting from zero. Today there are ready-made codes that can ease this task. With the advance of technology, it is expected that even beginner analysts will be able to perform cross-validation and machine learning on their own.


Another thing that will most likely happen in the near future is the increased level of cooperation between data scientists and system engineers. The first example of this kind of cooperation has proven to be very beneficial for the overall productivity of the company.

Finally, data scientists will be more focused on two basic tasks. The first one is to prepare input data with the help of the domain and business knowledge they have. The second is analyzing and interpreting the output generated by the tools they use.

Data science is not a new concept, but it is gaining its momentum now and according to many experts its importance will be much greater in the future. 

My name is Kristijan Janušić; I’m a creative director at Storyboard Agency and a very proud father to Korina. Same as Ana, she is my forever-muse.