The most important questions about the scope of duties, requirements, salary and career entry.
Science fiction or reinvention? The data scientist profession regularly encounters a wide variety of reactions, although it has existed since the 1960s. In their day-to-day work, data scientists analyze large volumes of data, search for correlations among the data and use these as a basis for generating predictions for developments and trends.
Many companies already collect data, but it’s only valuable if it can be analyzed in a useful way. Then the large amount of data, known as big data, can be turned into an aid for decision-making, also known as smart data. To make this happen, a data scientist uses advanced analytical methods to predict trends with the highest possible reliability. This method is also called predictive analytics and helps companies arrive at the best possible decision, giving them a competitive advantage over their rivals.
Data scientists are in demand
Data experts such as data scientists are currently in greater demand than ever on the job market. In addition to their core competency in handling data, they typically need to possess other technical skills such as Python programming to be valuable to many companies. Many companies are already aware that demand will continue to increase and are countering this by training and educating their workforce. That’s why more and more employees are interested in this future-proof and respected job. Before you start a career as a data scientist, you should know what skills a data scientist should have, how to get started and what role mathematics, programming and statistics play in the profession. In this article, we’ll explore these questions and more to help you get started.
What does a data scientist do?
Our world is digital and becoming more connected every day. Due to the progress of digitalization, the amount of data and the importance of using it actively is also increasing. Accordingly, the need for data experts such as data scientists who can handle this flood of data and turn it into valuable analyses or reports is also growing. Translated from English, the job title Data Scientist means something like data scientist. Put simply, data scientists are analytically-minded data experts who use the company’s own data sources as well as third-party data to make predictions and thus identify economic challenges and opportunities for the company early on.
Instead of wondering what happened, data scientists ask what will happen. Of course, data scientists aren’t psychic or just following their gut. They base their assumptions on real data, which they evaluate systematically to derive insights. First, you have a hypothesis that you want to test. To do this, you need a good understanding of the business context. As a data scientist, you identify suitable data sources, collect the relevant data and create a model that you continuously adjust until the hypothesis and reality are as close as possible. Often, as a data scientist, you have to look for creative solutions on how to improve data quality in order to perform analyses that would otherwise not be possible. The insights gained from a data scientist’s work are designed to help business leaders make the right business decisions and be strategic in their implementation. To do this, you don’t just rely on the data that the company has, but also draw on sources such as social media. As a data scientist, you use scientific approaches and methods from computer science, mathematics and statistics to systematically evaluate large amounts of data, some of which is unstructured.
What this looks like in practice on a day-to-day basis depends largely on the area in which a data scientist works, business or science, and can vary from industry to industry and company to company. A data scientist may work on customer buying behavior, self-driving cars, or automation in finance, and each project brings its own set of challenges. As a data scientist, you have a lot of responsibility, because companies and customers depend on the results of your analyses. Analyses in the medical field or training artificial intelligence for self-driving cars have a great impact on individuals and society. That’s what makes the data scientist profession so versatile and fulfilling.
How is it different from being a data analyst?
Unlike data analysts, who are responsible for more traditional data analysis, data scientists have a stronger scientific focus. While it is impossible to separate the two job descriptions because many of their activities overlap, as a general rule, you can assume that a data scientist’s responsibilities require more advanced, scientific methods of analysis and prediction. For this reason, data scientists usually have an academic background and come from disciplines such as mathematics, physics, or computer science. From there, they bring important skills to the data scientist profession such as statistics, analytics, or probability, which are imperative for machine learning and artificial intelligence applications. A solid knowledge of mathematics and statistics is the cornerstone of a data scientist’s work. It’s particularly important that they not only analyze data in the privacy of their own home, but can also communicate it verbally to others. Good communication skills characterize data scientists and make them valuable advisors to company management.
Your tasks as a data scientist
Now that we’ve clarified who a data scientist is and why the profession is in such high demand, we’ve come to what exactly data scientists do, what their role in a company is and which areas of responsibility they have.
1 Formulate the question
Before you can start your analysis, you need to define what you want to find out. The result of your analysis is only as good as the question on which it is based. This makes this step the most important in the whole analysis process. A house is only as stable as the foundation on which it is built. So in order to determine the right question, you need to determine the objective of the analysis. Sometimes you will feel like Sherlock Holmes, because often the obvious problem is not the one that leads to the core of the solution. So you will have to look at the problem you want to solve from different perspectives first to get the whole picture. You may even find that, with enough experience, you’ll start looking for optimization opportunities within the company on your own and identify exciting use cases.
2 Collect and clean up data
Now comes the step in which you gather the appropriate data to answer your question. To do this, you first identify the right data sources:
– First-party data, which is collected directly by you or your company.
– Second-party data that comes from another company
– Third-party data that comes from external sources such as social media.
To collect second and third-party data, you need to think about a good strategy and use data from surveys, observations from social media posts, or online tracking from a website. In some cases, it may even be necessary to develop your own models and algorithms for data mining. Once you have collected enough data, it will need to be cleaned because it will be in both structured and unstructured form. By cleaning and validating the data, you can ensure its accuracy, completeness, and consistency.
3 Analyzing data
A particular skill of data scientists is exploratory analysis. In exploratory data analysis, you set aside your initial assumptions, hypotheses, or data models and instead try to look at the underlying structure of the data. This allows you to identify important variables and spot outliers and anomalies. The subsequent data processing phase involves entering the data into a system. This can be a CRM like Salesforce or a data warehouse.
4 Modeling data
Data modeling depicts how data flows through a software application or the company’s own data architecture. You can think of it like a blueprint that shows how data is collected and stored. When you model data, you act like a civil engineer, overcoming unexpected hurdles and trying to fix pieces until everything fits together. You’ll find the most efficient way to store information while providing complete access and reporting.
5 Documentation, visualization, and presentation.
A data scientist is expected to document your processes and provide enough descriptive information to help you in your own work, as well as colleagues and other data scientists in the future. Not everyone can read and understand statistics. Visualization is therefore one of the most important skills to ensure that the collected findings are understood by key business functions and that they can act upon them.
What skills do you need to be a Data Scientist?
Data scientists need a wide range of skills, from technical knowledge of programming languages and statistical software to communication and storytelling. Undoubtedly, the exact skill requirements for a data scientist will depend on the client, job role, and career stage. To help companies make smarter decisions and develop better products, data scientists need two types of skill sets:
As a data scientist, you should have a solid understanding of math and statistics, as you will very often need statistical models to analyze data. In addition to knowing how to collect, clean, and manipulate data, Data Scientists need to be proficient with statistical modeling software, such as SQL databases and the Hadoop platform. Furthermore, programming skills are of great advantage, because you can extend modeling functions on your own and are independent from rigid software solutions. In addition, Python and R are considered the must-have programming languages in the field of data science.
Programming: Python, SQL, Scala, Java, R, MATLAB.
A data scientist must understand programming languages on the one hand, but also be able to manage databases. Programming is your means to process and analyze data.
Machine learning: Natural language processing, classification, clustering, deep learning
A data scientist uses the intelligent processes of machine learning to automate decision making by computers that was previously done by humans. A computer does not initially know which decision is the right one, so a data scientist has to continually train that computer until the computer’s predictions are accurate enough.
Data visualization: Tableau, SAS, D3.js, Python, Java, R
A data scientist also needs to translate their findings into images to present and communicate to others without having to use their expertise.
Big data: MongoDB, Oracle, Microsoft Azure, Cloudera.
A data scientist uses both structured and unstructured data. It’s not uncommon for data analysis to use big data techniques.
A data scientist must be able to do more than simply use the right tools, they use them to analyze data in different ways. To do this, they uses their knowledge of statistics, probability or A/B testing. This doesn’t just require a lot of creativity, but also a solution-oriented approach. Data scientists often have to design new databases or develop new algorithms. Last but not least, a data scientist must be able to communicate very well, not only with their own team, but also with managers or customers. So the job requires not only hard skills, but also very unique soft skills.
Communication: Effective communication is the most important non-technical skill you need as a data scientist. As a storyteller, you know how to visualize data and communicate its findings to key stakeholders who may not have a technical background.
Critical thinking: Critical thinking is a very valuable skill in any profession, but especially for data science. It helps you as a data scientist to find insights, formulate questions accurately, and be able to understand how the result of data analysis benefits a company and what next actions need to be initiated.
Analytical skills: These skills help data scientists search for answers while challenging initial assumptions. Data scientists always tend to ask “why” and follow their urge to uncover new questions – especially those that have never been asked before. As a data scientist, you’ll rely on your sleuthing skills to uncover underlying truths and go on the hunt for answers.
Problem solving ability: In your day-to-day work as a data scienstist, you’ll need to identify opportunities and find solutions to all sorts of problems by challenging existing assumptions, identifying resources, and putting on your detective hat to find the fastest path to resolution. The ability and desire to solve problems is at the core of data science.
Lifelong learning: data scientists see themselves as a “work in progress.” They understand that no one can know everything, and since data science is always evolving at a rapid pace, even experienced data scientists must always follow developments to keep up with the times. A new problem, can often lead to new research. This natural curiosity will drive you forwards as a data scientist.
How do you become a Data Scientist?
Believe it or not, even career changers can become data scientists with the right training. Data scientists tend to be more highly skilled than data analysts. According to KDnuggets, 88 percent of data scientists have at least a master’s degree and 46 percent have a doctorate. It may sound like a science career is a prerequisite for a data scientist career. While that is very helpful, it is not mandatory for this career path. After all, even though STEM majors provide important prerequisites, there are definitely career changers in the field of data analysis and science. In fact, it can be an advantage to have already gained experience in other specialist departments in order to successfully support them in turn. Someone who knows the processes, procedures and ways of working of departments also recognizes hidden potential for optimization, data collection and solution finding. A data scientist must have a technical understanding of a department in order to be able to advise it well. Probably the most important prerequisite is therefore curiosity and a willingness to learn.
What does a typical day look like for a data scientists?
Again, there’s no such thing as a typical day for a data scientist. Nevertheless, to illustrate the daily routine, let’s choose the example of a data scientist in the field of personalized advertising. Let’s call them Robin.
Their day starts with checking whether there is new data in the data warehouse that they need for a project. It’s personal data, so first Robin has to anonymize it. Before Robin can continue, they first have to check that all the data is complete and that the text variables are encoded correctly. They find an error in the data from the last two weeks and bring this bug, a technical error, to the attention of a data engineer on the team. Robin feeds the data they already have to a model and tries different analysis approaches.
In the afternoon, they get creative and sees what kind of performance they can get out of the data. Robin discusses ideas with colleagues, Googles possible approaches and consults technical literature. In the following days, the data engineer will provide Robin with the missing data, which they will then transfer into a larger model along with the existing data, where it will be applied to personalized advertising. Robin will then prepare visualizations and present the results of their analysis and recommended actions to other departments.
What does a Data Scientist earn and what are the career opportunities?
Since the job is in high demand right now, salaries can be quite high. As a career starter, you’ll start with an average annual gross salary of €45,000, according to the specialist site Get in IT. As in any career, the amount you receive at the end of your data career depends on various factors such as the industry, the size of the company and your position in the company. There are of course no rigid salary limits, but usually the maximum salary is between 99,800 and 108,200 euros annual gross, depending on professional experience and personnel responsibility.
As a data scientist, you can work wherever large amounts of data are collected and there is an interest in learning from this data and optimizing processes:
Logistics – Where routes and schedules need to be optimized to find the fastest routes.
Online retail – Where it is necessary to figure out how to reduce returns by suggesting the right product to customers in the first place.
Energy supply – If consumption peaks and bottlenecks can be predicted, supply can be adjusted accordingly.