If Bitcoin is digital gold, then data is digital oil. Data is the lifeblood of the internet. It seems like everyone these days is in the business of buying and selling data. According to Experian, 85% of organisations see data as one of their most valuable assets. That’s because data can inform us on so many aspects of a business or service. Where the business is succeeding and where they are failing. The location of our customers, the language they speak, how much they are willing to spend.
In the age of data, businesses need employees who can read and understand raw data to then translate it into a medium the whole business can understand. This person is called a data analyst. They are the middleman between a specific question and a database. In this comprehensive guide, we will explore what a data analyst is, what they do, the skills required, the tools they use, and how to become a data analyst.
A data analyst uses data visualisation tools and programming languages to interpret data in a way that is easy to understand for non-technical employees. For example, a non-technical employee (i.e. sales person, head of department, etc,.) might have a question regarding company data (i.e. number of abandoned carts in Q1 vs Q2). They’ll ask a data analyst to extract relevant data to answer this query. The data analyst will gather data from a database, clean the data, and then use their mathematical and coding skills to answer the query.
What does a Data Analyst do?
The primary tasks of a data analyst are to gather, analyse, and interpret raw data from a database. The majority of their time is spent creating reports or dashboards for company departments. The most common process is like this: A ticket will be created for the data analyst. The ticket will outline the query and the deliverables (i.e. report or dashboard). The data analyst will gather the data then connect it up to a dashboard in Power BI or Tableau (data visualisation tools). Then they’ll make sure the data is correct and clean, finally handing over the dashboard to the stakeholder.
Since data is informing almost every aspect of the business there are a lot of queries that overlap or repeat. For example, you might want to know all the key sales information over a period of time, and you want to know this each week or month. So a data analyst will also be able to set up automated processes, reports and dashboards so that you don’t need their support for future questions. Their time can be spent on more complex and important problems.
What’s the difference between a Data Analyst and Data Scientist?
Data analysts and data scientists are often confused, these are not interchangeable terms, they are different roles. Data analysts are focused on interpreting existing data while data scientists are involved in the development of algorithms and predictive models to forecast future outcomes and capture new data. Put simply, a data analyst works in the present and past and a data scientist is trying to predict the future and expand the data a company has access to.
In smaller companies, I’d imagine there would be some overlap between these two roles but generally these two roles are kept separate. An analyst usually comes from a business background whereas scientists have a deeper knowledge of programming and statistical techniques.
Data Analyst skills
There are four main hard skills of data analysis. They need to be proficient with excel (or similar), a query language, programming language, and a visualisation tool. You can see the categories below and the most common/popular choices for businesses and analysts.
Data analysts need to be skilled with Excel as this is one of their primary reporting tools. It’s also a primary tool for sorting and cleaning data. There are other tools but most businesses work with Excel - it’s universally adopted. They also need to know a query language like SQL as this is how they request data from a database. Once the data is retrieved, an analyst will need to visually display the data or create a dashboard, so they’ll need to be skilled with either Tableau or Power BI.
In terms of soft skills, there is a lot of back and forth between data analysts and stakeholders. It’s important that an analyst can communicate with stakeholders to understand their needs and deliver the correct results. Analysts also present a lot and share data with the wider company, so public speaking skills will be a bonus.
SQL (most popular)
Python (most popular)
Data visualisation tools:
Power BI (most popular)
Excel (most popular)
How to become a Data Analyst
The most common route to becoming a data analyst is through university. Data analysts generally have a background in statistics, computer science, or mathematics. A smaller percentage of people have come from related fields like marketing, business or finance. It is possible to become a data analyst without a degree, if you are switching careers or just starting out, here are a few steps you can take.
Start with Excel - Almost every company uses excel which makes it a very valuable skill (even by itself). Get good at using excel and learn the formulas. Excel Practice Online is a great website where you can learn and practise dozens of Excel functions using data.
Learn a Query language - There are a few languages to choose from. Given that SQL is the second most requested skill for data analysts, it’s going to be your best choice and a good stepping stone to more serious programming. There are plenty of courses available online, but the best way to learn is by doing. W3Schools will teach you everything you need to know about SQL to get started.
BI tools - There are a few of these tools to choose from. Tableau, Power BI, or Qlikview. In the beginning, you should focus on becoming proficient in one tool and then you can start adding more. This will increase your employment opportunities. Datacamp has some good courses on these tools. In some cases you’ll need to get a certification (i.e. Tableau DS), there are some great courses on Udemy which will work you through the process and prepare you for the test.
Programming language -Finally, you’ll need to learn a programming language to manage data structures. The most common languages for this are R and Python. Most analysts prefer Python because it is easier to learn and use, plus the most common programming language in the world – which means it’ll have a bunch of other applications outside of just analysis. You can learn Python for free online.
Practise & apply - Make sure you put your skills to use every single day. The compound efforts of daily work will eventually pay off in the form of a job. Once you’re confident in your abilities start applying for work. Clean up your LinkedIn profile so recruiters can find you, and start applying for every job you can – getting your foot in the door is the most important thing, employment history will be the most important factor moving forward.
What is the future outlook for data analyst jobs?
The demand for data analysts is increasing quite significantly. The US Bureau of Labour Statistics estimated that data analyst jobs would grow 23% between 2021 and 2031. It’s on an upward trajectory and outpacing the majority of other occupations. So if you were thinking about a career in data analysis, there’s never been a better time.
Data Analyst salary
According to data from StackOverflow, the median worldwide salary for a data analyst is $69,000 USD. Even though this data includes salaries from outside of Europe and the United States it is still quite high compared to other tech salaries (i.e. mobile developer $56,220). In the US, a data analyst earns an average salary of $66,000 (Glassdoor) and in Germany, around €55,000. Here’s a snapshot of average analyst salaries from Europe and the US.
The more experienced the analyst the more salary they will earn. The average entry-level salary in the United States is around $63,000 — which is okay, but this figure will certainly vary depending on the company you work for. For example, a junior Data Analyst working at Google can earn around $98,000 per year. This is quite a difference from the average. A senior analyst (5+ years) can earn a much higher salary. According to TechPays, there are senior analysts earning around €120,000 per year.
Avg. Salary (USD)
Data Analyst jobs
In Europe, there are hundreds, if not thousands of data analyst jobs. If you’re a data analyst looking for a new position, we have some great options for you in Austria, Germany, and Switzerland. Check out our job board for the latest positions. Or if you are looking to hire a skilled data analyst at your company, we can help you attract the right talent from our developer community.
What skills does a Data Analyst need?
A Data Analyst typically needs skills in data manipulation, programming languages (e.g., Python, R, or SQL), statistical analysis, data visualisation, critical thinking, and problem-solving. Additionally, communication skills and domain-specific knowledge can be important.
What tools and software do Data Analysts use?
Data Analysts commonly use tools like Excel, SQL, R, Python, and SAS for data manipulation and analysis. Visualisation tools such as Tableau, Power BI, or Google Data Studio may be used to present findings and insights.
How is a Data Analyst different from a Data Scientist?
While both roles involve working with data, a Data Analyst focuses on analysing and interpreting data to support decision-making, whereas a Data Scientist employs advanced techniques like machine learning and predictive modelling to develop data-driven solutions and forecasts.
What are some common industries for Data Analysts?
Data Analysts can work in various industries, including finance, healthcare, retail, marketing, technology, and manufacturing. Any industry that generates and utilises data can benefit from the expertise of Data Analysts.
What is the career path for a Data Analyst?
A Data Analyst may begin their career with an entry-level role and progress to senior or lead positions, or specialise in specific domains. Some Data Analysts may choose to transition into related roles, such as Data Scientist, Business Analyst, or Data Engineer.
How can I improve my data analysis skills?
To improve data analysis skills, consider taking online courses, attending workshops, or pursuing certifications in relevant areas. Additionally, practise your skills by working on real-world projects, participating in data competitions, or contributing to open-source initiatives.
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