Career Guide – How to Start Your Data Science Career in Vienna?

Data science is a combination of various methods, algorithms, and practices of machine learning to discover secret trends in the original datasets.

No, data science isn’t the same thing as data analysis. Being a data scientist is different from being a data analyst, with a few overlapping areas. The data analyst usually analyzes the history of the data to discover valuable insights. On the other hand, the data scientist analyzes the data, identifies trends, and uses machine learning techniques to predict future events based on the collected data.

Basically, data science is present to make decisions and predict future events. To perform this, data scientists use predictive and prescriptive analytics and machine learning. Data scientists have to discover new ways of looking at the same data.

“The job of the data scientist is to ask the right questions. If I ask a question like ‘how many clicks did this link get?’ which is something we look at all the time, that’s not a data science question. It’s an analytics question. If I ask a question like, ‘based on the previous history of links on this publisher’s site, can I predict how many people from France will read this in the next three hours?’ that’s more of a data science question.”

―Hilary Mason, Founder, Fast Forward Labs

New technologies have enabled data science to grow from pure statistics to a pervasive, sophisticated science. The new area includes machine learning, data mining, business intelligence, deep learning, and many, many other methods.

Many people still believe data science is just a trend that will go away in a few years. Well, they couldn’t be more wrong. Data science is just getting started. We have only seen a small drop in the ocean of benefits that data science has the power to bring.

Data science is one of today’s careers that have the most significant potential for the future. According to the US Bureau of Labour Statistics, the rising demand for data science will create around 11.5 million job openings by 2026. What is more, a World Economic Forum report expects data scientists to become the most emerging role in the world by 2022.

The world still doesn’t have enough data scientists. If you decide to pursue a career in data science, you will come across a much weaker competition than other tech professions. It’s a field of countless opportunities waiting for you to explore them.

Next, data science implementations are countless. Industries like healthcare, finance, consulting, and e-commerce are actively using data science. Thanks to its versatility, you could get the chance to work in multiple areas.

Furthermore, data science eliminates tedious tasks by simplifying repetitive processes. Companies utilize past data to teach computers to execute repetitive activities. This has minimized the exhausting manual tasks previously performed by people.

And, let’s face it. Money does matter. According to Glassdoor, data scientists make an average of $113,309 per year, which makes this profession one of today’s highest-paid jobs.

>> Read about Data Science Bootcamp – Final Project: NLP Chatbot

Data scientists are people who leverage their deep experience in specific technical fields to solve complicated computer problems. They use statistics, modelling, mathematics, computer science, machine learning, and methods from various other areas.

They take advantage of new technologies in seeking answers and making decisions essential to the company’s growth and progress. To do this, they process the raw data accessible from both organized and unorganized sources, converting it into actionable information.

When the data scientist gets the task to solve a data-related problem, they don’t always clearly explain the assignment. The data scientist then has to turn the challenge into a specific and measurable case, work out how to tackle it and bring the answer back to all the stakeholders.

Here’s how the process goes:

1. Identify the problem. What is it that you have to deliver? How is it supposed to help your company? How do you turn their vague question into a challenge that is clear and understandable?
2. Gather the data that revolves around the problem. Identify the resources you have to collect data from. Choose which ones are relevant and which ones aren’t. Decide how much time and effort you need to collect all this raw data.
3. Process the data. The data you collect will be corrupt and full of errors. You need to clean it and translate it into understandable information that is easier to analyze.
4. Analyze the data. This is the part where you identify trends and patterns in the understandable information you now have. Decide which trends are more relevant than others.
5. Uncover predictions. This is where machine learning algorithms and statistical models come in. They need to help you extract predictions based on the patterns you’ve identified.
6. Present the findings. You have to explain your scientific findings to your stakeholders in an understandable way. They should leave the meeting convinced about the reliability and accuracy of your results.

Not all companies use this process. In some cases, you’ll have to identify analytics problems that are of the highest importance on your own. To do this, you’ll have to have a deep understanding of the datasets your company operates with. You’ll often have to deal with unstructured sources like images. You’ll have to identify new opportunities for the company by analyzing data.

If you start asking people who work as data scientists what they did before, you’ll get tons of different answers. The backgrounds of data scientists are very diverse, from anthropologists to philosophers. Some own Master and Ph.D. degrees in various fields, but others have become some of the best industry professionals by learning on their own.

The background doesn’t matter if you focus on developing the knowledge you need to overcome the issues you need to solve and the challenges you need to handle. Some coding and statistics knowledge would be helpful to have for starters, but not crucial.

One thing that is for sure a must is the desire to study. You won’t get anywhere if you don’t dedicate enough time and effort to learn the profession’s secrets and become an outstanding data scientist.

Admissions are open for the Fall Data Science Brainster Bootcamp. Join us and future-proof your career by building a top-notch and job-ready portfolio.

How do you determine if you have what it takes to become a good data scientist? There’s a set of characteristics that can make you stand out of the crowd.

First, you’re a life-long learner. You are very curious, and you never stop learning. You always want to know new things. There are so many fields to be explored and so many data points to be evaluated, that a data scientist needs to have an overwhelming desire to search for answers.

Second, you have to be organized. As you’ll be dealing with enormous datasets with many data points, you’ll have to be able to hold your assets. Active organization and management will lead you to the right conclusions by the end of your research.

Next, you are a persistent person. A very, very persistent person. Data science is all about problem-solving. And sometimes, you won’t be able to find the solution immediately. It might take a lot of time and effort until you get to the foundation of the problem. That’s why you should be persistent, and you shouldn’t give up easily.

Staying focused, creative, and aware of details are beneficial skills when you’re a data scientist. But, most importantly, you should like data. You’ll live with data. You’ll probably start dreaming about it. Do you often calculate the probability of different scenarios in your life? Do you base your decisions on facts? If yes, then data science is right for you.

• Statistics is the crucial skill you need to become a data scientist. You must know both descriptive and inferential statistics. Descriptive statistics explains the data and summarizes the results. The inferential statistics draw assumptions from smaller samples and imply them over large groups. Both descriptive and inferential statistics include various statistical methods. You will also need to learn the fundamentals of hypothesis testing and experiment design to understand the significance and the background of the results.
• Mathematics & Statistics. Dealing with large datasets is very difficult. That’s why you need to have a deep understanding of math. You must know the fundamentals of linear algebra, discrete math, calculus, and optimization theory. Linear algebra and calculus are essential to understand machine learning. While discrete math is the math you need to deal with databases. Optimization theory enables you to take advantage of data efficiently. You also need to learn how to handle data matrices and provide a basic knowledge of the mathematics behind the numbers.
• Coding. A data scientist must know how to bring their expertise into action beyond the understanding of statistics & mathematics. Coding helps you to show your analysis practically. In data science, you will use frameworks of Python and R, SQL and noSQL, and big data technologies such as Apache Hadoop and Apache Spark.
• Domain expertise. Like any other employee, you need to know about the organization and the industry you work for. While getting in-depth knowledge of the organization you work for, you would also need to have some deep understanding of the market to provide context to your findings. For example, if you work in pharmaceuticals, you won’t be able to use the jargon from banking.
• Communication skills. To be able to present your insights in the best possible way, you need excellent communication skills. This is one of the crucial soft skills a data scientist should possess. The key fields where you’ll show your communication skills are storytelling and data visualization.

Here are the careers you could pursue with data science knowledge:

• Data scientist. You’ll collect, clean, and analyze data for your company. Data scientists need to be able to interpret vast quantities of raw and structured material to identify trends that can favour an enterprise and help make strategic business choices. Data scientists need more technical knowledge than data analysts.
• Machine learning engineer. You’ll develop data funnels and deliver technical solutions. To perform these tasks, you need excellent statistical and coding abilities, as well as development experience.
• Data analyst. Adapt and exploit massive datasets to match the company’s preferred analysis. This function can also involve monitoring website analytics and evaluating A/B tests. During the decision-making phase, data analysts often support producing reports for corporate stakeholders. That efficiently interpret the patterns and perspectives gleaned from their research.
• Data engineer. Create and maintain data infrastructure. With more coding skills required, this role requires you to build data pipelines for various company sectors. This way, you are expected to develop a connected and accessible data system.
• Business intelligence (BI) analyst. Analyze data to identify market trends and opportunities that will engage customers. Using various BI tools, you need to use the information to help your company make better business decisions.
• Statistician. They gather, analyze, and communicate data to all the relevant stakeholders to improve the organizational decision-making process.

>> Read about Why you should land a Data Science job in Vienna?

Experts in data science are necessary for nearly every area, from government protection to medicine. Thousands and thousands of corporations and government department heads focus on big data to achieve success and improve customer satisfaction. The data science roles aren’t going anywhere anytime soon.

Glassdoor reviews say that the average salary of a data scientist in Austria is €43,542 per year. However, according to Salary Expert by ERI, a data scientist’s average hourly rate in Vienna, Austria, is €51. The report predicts that the data scientist’s salary in Vienna will increase by 9% by 2025.

Source: Salary Expert by ERI

With a significant number of open data science-related positions, the data science community in Vienna is emerging. You’ll be part of a growing movement that already counts over 1,200 members on Meetup.

Gjorgji Strezoski is a Ph.D. candidate in deep learning in the artistic paintings domain at the University of Amsterdam. Besides finishing up his PhD., Gjorgji also teaches Fundamentals of Data Science, works as a Computer Vision researcher in the Rijksmuseum, and consults on AI-powered Art in Paradox StudiosNL. Here’s what he told us on how a data science career path should look like:

“In my opinion, the ideal road to a career in data science means starting with a solid mathematical background, continuing with a detailed understanding of the basic concepts about the basic algorithms being used, interesting projects that spark curiosity, constantly upgrading the skills and deep understanding of the concept of data.

I salute Brainster’s initiative to enter the data science scene in Europe. I had the opportunity to gain insight into the programme and I can confirm that it encompasses the crucial aspects that are needed for a successful career in data science.”

Kiril Cvetkov, one of our data science lecturers, is the founder of Beautif.AI, a photo editing software based on AI and computer vision. He also works as a machine learning engineer at symphony.is. Kiril is a life-long learner. He enjoys designing, building, and improving complex systems and processes. He works to solve technically-challenging problems and help others improve their efficiency.

Dr. Frank Benda is a lecturer at the University of Vienna, where he obtained his Ph.D. in machine learning and deep learning. He’s also a member of the Austrian Society of Artificial Intelligence and works as a scientific assistant at ELG.

Josip Lazarevski is an Amsterdam-based data scientist with 15 years of experience in bringing companies to the next level through data-driven methodologies, machine learning, and AI. He focuses on helping key stakeholders understand the way data-driven solutions upgrade their organization.

Hutchison Drei wants experts in the fields of data and data analysis. In this position, they use sophisticated data analysis methods, data mining, and analytical procedures to identify new business potential and find innovative solutions. The company puts an accent on work-life balance, giving its employees a great working environment, nature and sports spaces, and many other benefits.

solicon IT is a young, innovative Austrian company based in Graz and Vienna and focuses on comprehensive solutions in data management, business intelligence, and business analytics. The company offers exciting projects with challenging tasks and the corresponding assumption of responsibility, integration into the environment, continuous education and training opportunities, and exceptional career opportunities through individual career design.

Adverity is an advanced marketing analytics tool that allows data-driven marketers to make smarter, quicker, and more straightforward decisions that boost results. The company offers modern and stylish office space, many workplace activities with a diverse and cutting-edge team, and technology-flat structures. Their employees work with clients such as IKEA, Red Bull, GroupM, Unilever, Omnicom, Barilla, JD Sports, and Forbes.

MOSTLY.ai is a high-tech startup that provides AI solutions for synthetic data. The startup looks for candidates who love deep learning and continuously push the boundaries of unsupervised machine learning. They offer five weeks of paid vacation, lunch subsidies, free public transportation tickets, snacks and drinks, and exciting team events.

• Data science is a combination of various methods, algorithms, and practices of machine learning to discover secret trends in the original datasets.
• Data science is one of today’s careers that have the most significant potential for the future. According to the US Bureau of Labour Statistics, the rising demand for data science will create around 5 million job openings by 2026.
• Data scientists use statistics, modeling, mathematics, computer science, machine learning, and methods from various other fields to extract insights that can help companies make better business decisions.
• If you want to become a data scientist, your background doesn’t matter. If you focus on developing the knowledge you need to overcome the issues you need to solve and the challenges you need to handle.
• With a data scientist background, you can become a data scientist, a machine learning engineer, a data engineer, a data analyst, a BI analyst, a statistician, etc.
• With a high number of open data science-related positions, the data science community in Vienna is emerging. You’ll be part of a growing movement that already counts over 1,200 members on Meetup.

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It looks like Harvard was right. Being a data scientist is an exciting role that can transform the way a company works.

As companies possess tons of data, it should be leveraged to take businesses to the next level. Data can help make critical strategic choices and accelerate major market transformation through accurate analysis and exploration of actionable insights. It may be used to maximize consumer satisfaction and accelerate retention and progress.

Data scientists may have a substantial positive effect on the performance of a company. They can unintentionally trigger economic loss, which is one of the main factors that make the recruitment of top talents necessary.