How to become a Data Analyst

Job description, qualifications and career opportunities

We live in a digital world that is becoming a more connected with each passing day. This connected world is increasingly driven by data thanks to AI, machine learning and data science. As a data analyst, you will therefore have your finger on the pulse and be able to positively influence economic, but also social developments thanks to a good, analytical understanding. It’s a job that comes with a lot of responsibility. Where you work with data, you also have to act with great caution. It’s therefore no surprise that a great deal of well-known companies are looking for good data analyst candidates. We’ll show you how to switch careers and become a data analyst, what you need to do it, and what exciting opportunities the profession, billed as the hottest job of the decade, will offer you on a daily basis.

What is data analysis and why is it so important?

Data analysis is the process of extracting insights from unorganized information (data). Data can take many different forms. For example, vacation photos and voice files are just as much data as a first name and a phone number. By systematically examining this data for patterns and relationships, data analysts aim to draw useful insights from it and communicate them to others in an understandable way.

A data analyst’s work begins with what is called raw data. Raw data is initially unorganized. Unless it is first cleaned up, the data analyst cannot derive any insights from it. In other words, order has to be brought into the chaos for the data to be usable. That’s the job of a data analyst. Collecting, cleaning and organizing data is a large part of data analysis. To do this, data analysts use various methods from statistics, programming, and visualization. However, data analysts don’t have to be one hundred percent proficient in these disciplines, because many of these steps have now been automated and run partly on their own. You just need to understand these processes well in order to manage them and control the results.

Data analysis is important for two reasons. First, it facilitates decision-making, and second, it makes decisions provable based on facts. That’s what makes it so valuable for companies that need to incorporate lots of different data in order to develop smart strategies. So instead of just relying on your gut feeling, as a data analyst you make decisions based on facts. Even though data analysis is not always 100 percent accurate, it is the best tool to predict trends and developments. In practice, it can point to future sales figures and helps with product development and sales strategy. However, it does not have to be used only in a business sense. Data analyses are also used in the health sector, agriculture or in government bodies.

The tasks of a data analyst

Put simply, a data analyst collects and prepares a large amount of data and then performs analysis on those data sets. As a Data Analyst, you look for ways to harness data to gain insights, answer questions, and solve problems. The analyses a data analyst creates should lead to better, more informed business decisions. With the results, a company can reduce manufacturing costs, increase customer satisfaction, or solve other problems that cost a company money. To do this, the data analyst works as part of a team with data scientists and data engineers.

How is this different from being a data scientist?

Often the distinction between the two job roles is blurred because the field surrounding data science is still relatively young and evolving constantly. A data scientist differs from a data analyst primarily in their advanced job skills. Both roles share many, similar tasks, are equally qualified to analyze and interpret data, but data scientists also have in-depth knowledge of programming and mathematical modeling. This allows the data scientists to tackle deep problems, such as automating processes and programming artificial intelligence. A data analyst takes on the role of a contractor and oversees tasks defined by other departments. A data scientist, on the other hand, independently looks for opportunities for improvement. While a bachelor’s degree is sufficient to start as a data analyst, a master’s degree is often required for a data scientist.

Your tasks as a data analyst

1 Formulate the question

Your analysis starts with the definition of what you want to find out. The results of a data analysis are only as good as the question with which the analysis was started. To formulate the right question, you need to define the goal of the analysis. It is, in some ways, the most important part of the analysis process. The crux is that quite often, the obvious problem does not lead to the core of the solution. Albert Einstein once said, “You can never solve problems with the same mindset that created them.” So, it’s important to first look at a problem from different perspectives and have a basic understanding of a company’s needs and requirements.

2 Collect data

Once you’ve figured out the right question, it’s a matter of determining what data you need to answer it. Two scientific approaches will help you here: quantitative and qualitative analysis. Quantitative analysis is about large amounts of data, while qualitative analysis is about getting the most meaningful feedback possible, such as customer reviews. To do this, data analysts use three different data sources:

  • First-party data, which is collected directly by you or your company
  • Second-party data, which comes from another company
  • Third-party data that comes from other sources such as social media.

To collect second and third-party data, you need to come up with a good strategy and use data from surveys, observations from social media posts, or online tracking from a website. Once you have collected enough data, it needs to be cleaned.

3 Clean the data

The raw data you have now is not yet organized, has not been brought into a consistent format, or is partially incomplete. To prepare it for data analysis, you first have to bring it into a consistent state. First you look for errors, duplicates, outliers and placeholders and eliminate them. Then you check again whether the data now meets your requirements or whether you need to review it again. Data analysts also refer to this procedure as data wrangling.

4 Analyze data

Once you have cleaned your data set, it’s ready for analysis. Here, data analysts have several options available to them, and you must decide which approach is best for the question at hand:

  • Descriptive analysis summarizes characteristics of a data set and attempts to describe them. While it does not allow you to draw definitive conclusions, it is very useful in determining which what the next steps for the analysis should be.
  • Diagnostic analysis is designed to understand causes and relationships. Examining the relationship of two values, can help identify problems. This step also helps to review and adjust your research question if necessary.
  • Predictive analysis helps to read trends and developments based on past data.
  • Prescriptive analysis can be useful in deciding how to proceed and is also used as a machine learning technique when computers need to learn to make predictions on their own.

5 Visualize and communicate data

After you have applied the analysis techniques to the data, there is one last important step. You still need to communicate the results of your analysis and explain them in an understandable way to others who don’t have the same analysis skills, if any. To do this, you visualize the data and create appropriate charts, graphs, presentations, reports or interactive dashboards that visually support your findings. This step is very important and its goal is that everyone can immediately interpret the results correctly at a glance and understand them themselves so that they can put them into their own words. If the visualization is simple and clear, it is of high value for further decision making.

You need these 9 skills to be a data analyst

A career in data analytics can require a wide variety of skills, including technical and non-technical. This also includes the specific application of tools. What skills you need depends entirely on the company, the industry, and your exact role. With that said, let’s take a look at the most important and most common nine skills that are important for you to have when starting out as a data analyst.

Technical skills

1 Programming skills

Most of the time, a job as a data analyst requires, first and foremost, the collection, preparation, and analysis of data. For this, programming skills are an advantage in order to be more flexible and independent of analysis software. This can save time in your day-to-day work. Python skills in particular are in high demand. Of all the programming languages that exist, Python is the preferred one for data analysis. If you don’t have any previous knowledge, that’s not a big deal. Even as a complete beginner, Python skills are relatively easy to learn with on-the-job training.

2 Databases and their languages

Managing data requires understanding the particular language of a database. A database comes into play where Excel’s capacity reaches its limits. It is a system in which very large amounts of data are stored and retrieved. A data analyst must be able to recognize the structures of databases in order to gather the information needed for analysis.

3 Data manipulation

In very few cases is the data ready for analysis from the start. As mentioned at the beginning, data is often incomplete, not structured or is in different formats. Data sets must therefore first be cleaned. Analysts often use a language such as Python to prepare data for analysis. This process is called data manipulation.

4 Data visualization tools

What does a data analyst do with the results of their analysis? Data visualization is a powerful skill for communicating results. Tools like Matplotlib, JavaScript d3.js, and Tableau help with this. Mastering these tools is important, but understanding the principles of how to successfully visualize data and communicate it in an understandable way using data storytelling is even more important.

5 Mathematics and Statistics

Statistics and linear algebra are skills that a data analyst should possess or learn. They are crucial when it comes to running tests and, in very advanced cases, are also necessary to decide how to optimize algorithms. For the second case, programming knowledge in Python is already a great advantage.

Non-technical skills

6 Logic

A bright mind will get you far. A useful skill for data analysts is to use logical thinking skills that do not require higher mathematics. Often, a data analyst needs to be able to think well into the context of their analyses in order to ask interesting questions and critique the results. Data analysis is a broad field with a variety of tasks. Depending on the task, special knowledge is needed. It is important to have a basic understanding of mathematics and statistics, but it is even more important to be able to look at problems in an analytical way. 80 percent of a data analyst’s work consists of preparing, sorting, and visualizing data, which usually does not require advanced mathematics.

7 Creativity

Data storytelling and presentations go hand in hand. But good presentation skills are not a given. The ability to tell a compelling story with data is critical to properly communicating insights and engaging audiences. It’s an art in itself to express complex relationships simply without distorting the content. For this reason, data visualization and good data storytelling are important skills for a data analyst to master.

8 Social Skills

It is important to understand who the customer of an analytics project or the audience of a presentation is and what their prior knowledge is. This is especially true when insights cannot be easily and quickly identified. Other departments often lack the knowledge to properly interpret the results of a data analysis. A data analyst knows what information their audience needs and how best to convey it.

9 Business experience

Understanding what a company’s goals are is critical when it comes to making informed, data-driven decisions. Data analysts should not only understand how data can impact their company’s decisions, but they should also know how to interact with engineers or product managers, for example, or bring industry knowledge to the table. Both a technical and non-technical understanding of the business and its internals is essential to the data analyst job.

Tools used

Microsoft Excel

As a conventional data analysis tool, Microsoft Excel offers a variety of functions. These range from sorting and manipulating data to displaying that data as charts. A good understanding of Microsoft Excel is very helpful for data analysts.

SQL

SQL (Structured Query Language) is a programming and database language and is used for database management.

Python

Python is a very common and almost essential programming language for data professionals. Fortunately, it is easy to learn and understand. Moreover, it is open source, which means that you can customize and develop it on your own.

SAS

SAS was developed by the SAS Institute for functions such as advanced analytics, building predictive models, business intelligence, and data management. It is very useful because it can easily handle any kind of statistical modeling of large amounts of data.

R

An alternative to SAS is R, a programming language and software environment for statistical computing and graphics creation. The advantage of R is that, as free open source software, it invites you to customize it yourself for your purposes.

At first, learning these tools and languages may sound difficult and you may not know where to start. We can banish that worry. As with any new skill you learn, it’s all about starting small. Whether you’re learning to cook, play an instrument, or paint, you always start with the basics. And these can even be learned on the side, if you keep at it. In the end, what counts is: practice makes perfect.

How do you become a Data Analyst?

Your career as a data analyst starts with learning job-relevant skills. As a newcomer with no math or programming background, this means first and foremost familiarizing yourself with basic skills and understanding the analysis process. Once you’ve gathered the essential skills, you can start thinking about applying for a job as a data analyst. Even though this process sounds effortless, a career change like this requires a lot of commitment from you. In order to learn everything you need for this change and to access the expertise of experienced data experts, it’s worth considering online courses. These courses, if successfully completed, can mean certification as a data analyst, which is an important alternative to a conventional university degree. To take the first step on your new career path, you can find out about course offerings and funding opportunities through the German Federal Employment Agency or Job Center on the StackFuel website.

A day as a data analyst

The day-to-day work of a data analyst depends entirely on the industry or company. They may be responsible for creating dashboards, maintaining databases, and performing analyses for various departments in the company. Most of them work closely with IT teams, management, business departments and data scientists. Depending on the job, the skills and tools a data analyst uses will vary. Some data analysts don’t even use programming languages and work primarily with statistical software and Excel. Because the field of data analysis is so varied, a data analyst may spend the morning, for example, cleaning data and the afternoon creating concrete, customized solutions. Let’s try to outline a possible day in the life of a data analyst.

In the morning, data analyst Lisa uses SQL to retrieve data from a database and then analyze it. To do this, she works in a table with customer data such as name, age, zip code, and value of goods, among other things. Lisa wants to find out where customers shop most frequently, but there are many gaps in the data in the table, so she discusses with her manager how to deal with the gaps. He decides that she needs to coordinate with different groups of people or stakeholders. With IT, she needs to discuss how the data gaps came about so that she can sort them out afterwards. She has to coordinate with her contact from the business department that commissioned the analysis on how to deal with the data gaps. They decide to use artificial intelligence and machine learning to fill in the gaps. To do this, Lisa continues to work with an expert on her team. Later that day, Lisa works to visualize the results of her latest analysis and turn them into a presentation to share with her team to determine next steps together. In doing so, Lisa makes it a point to ensure that the presentation of the results is coherent and easy to understand in order to facilitate a decision.

Data Analysts’ many options

It is an indispensable foundation that a company operates digitally. The more digitally oriented the company, the more data it has at its disposal. Classically, therefore, data experts are urgently sought especially in the finance, insurance, online retail, energy, telecommunications and healthcare industries. Among data analysts, there are also different specializations such as financial, marketing, weather or risk analysts, among others. These job descriptions have different names, but their tasks are so similar that certification as a data analyst qualifies you for these and other analyst jobs. This is not only particularly future-proof, but they also make the skills of a data analyst among the most sought-after of the 21st century. In many areas of business and society, it is very important to actively evaluate data. Only in this way can companies gain important insights into their products, services, customers and even internal company processes and how they can be improved. In this way, they can gradually gain competitive advantages and overtake the competition. Data analysts are also in demand in socially relevant industries. The healthcare industry needs to analyze and mine patient data to get information on the best treatment options and develop healthcare products. So the opportunities for data analysts are endless and people switching careers can even stay in the industry they know if they want to.

How much will you earn?

When it comes to choosing a suitable career, it is important to keep several factors in mind. Money alone doesn’t make you happy, but there’s no question that it’s very motivating and a material way of showing appreciation for most employees if they are also well compensated for their work. These factors are definitely present in the case of data analysts. On average, a data analyst in German-speaking countries receives an annual gross salary of around 42,000 to 60,000 euros. As a team leader in this area, you can earn up to 90,000 euros gross annually. As is often the case, the salary varies depending on work experience, industry, specialization, particular skills, region and negotiating skills in the job interview. Currently, there are several thousand data analyst jobs offered across Germany, on Stepstone 6971, Indeed 2.636, Glassdoor 2589 or Kimeta 3.879. With generous compensation and this large supply of unfilled positions, you no longer have to worry about your professional future as a data analyst.

If you are serious about a career in data analytics, there are many ways to learn the skills presented in this article and start a new career as a data analyst. One of the most efficient ways to do this is through a training course that will prepare you for the challenges of the profession. While many large companies are desperately trying to find and hire data experts on the job market, more and more companies are now investing in training their employees to become data analysts and data scientists. This is mainly because there is already a major skills shortage in this area and this is expected to increase. An additional advantage is that these employees already have invaluable experience in the industry, the market and the internal affairs of the company.

Find out more about becoming a data analyst and our funding opportunities.

Sources:

StackFuel (2020): „Einfach erklärt: Was ist Big Data?“ [10.03.2021]

Get in IT (2021): “EINSTIEGSGEHALT FÜR INFORMATIKER 2021“ [07.04.2021]

Stepstone (2021): “Data Analyst Jobs” [13.04.2021] 

Indeed (2021): “Data Analyst Jobs” [13.04.2021] 

Glassdoor (2021): “Data Analyst Jobs” [13.04.2021] 

Kimeta (2021): “Data Analyst Jobs” [13.04.2021] 

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Laura Redlich
Laura Redlich
As an authentic Berlin girl, Laura quickly joined the creative scene. After completing her bachelor's degree in media and communications management at the Mediadesign - University of Applied Sciences, Laura worked part-time as a booker in film and later as a production assistant. She started in marketing at MyToys’ email marketing department. Most recently, Laura lead the content marketing at IQPC and got a taste of Big Data and AI. In her private life, she is passionate about sustainable and mindful living - whether it's vegan food, meditation or yoga – Laura is always keen on trying out new things to develop herself.

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StackFuel ist Deutschland führender Anbieter für zertifizierte Online-Weiterbildungen und -Umschulungen in Data Literacy, Data Science und KI. Zur Bewältigung der digitalen Transformation und der bevorstehenden Qualifikationslücke im Bereich Daten und KI unterstützt StackFuel Unternehmen, Mitarbeitende effektiv und effizient in zukünftige Jobrollen weiterzuentwickeln. Die innovativen Online-Trainings bieten Teilnehmenden eine moderne und flexible Lernerfahrung mit einer interaktiven und Cloud-basierten Lernumgebung, in der sie mit Industriedatensätzen selbstständig Algorithmen entwickeln.

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