“Every company needs a Data Analyst as data is generated almost everywhere today”.
Pavlin Mavrodiev, one of the lead trainers in the first edition of our Upskill Data Analyst program, shared.
Pavlin is a Full-Stack Data Scientist at the leading bank in Switzerland (UBS) and holds a PhD in modelling complex systems from ETH Zurich.
Why Data makes the world go round
In today’s digital age, companies, regardless of their type or field, generate massive amounts of data. Making the most of it can help a company make better business decisions, improve operations, identify new products, services, or foresee upcoming challenges and be better positioned to prevent them. Thus, data analyst is considered one of the most resilient role since mid-2022 (according to 2024 Developer Skills Report).
That’s why people who can analyze data are highly valuable and sought-after. And that’s why if you are one of them, you’ll have a bright career future (and we know how to help you be this person).
But let’s start from the beginning - who are these professionals and what is their role? We’ll give you a hint – they are Data Analyst. Their role is more than just making sense of large data chunks – while it requires great technical skills, excellent communication skills are equally as important. In Upskill Data Analyst we help you develop all those skills so you can start your Data Analyst (DA) career right after graduation.
Ready for a versatile and exciting profession that can be a great first step towards a rewarding career? Apply for Upskill Data Analyst by March 10.
Meet the Data Experts leading your way
Pavlin Mavrodiev is а Full-Stack Data Scientist at UBS, Switzerland with a PhD in modelling complex systems from ETH Zurich. Georgi Smilyanov is currently the General Manager at Move Digital Bulgaria with a Master of Science in Management, Technology, and Economics from ETH Zurich. Last, but not least - Nikolay Trifonov, Director Data Insights Services at Coca Cola European Partners with over 15 years of experience in Business Operations & Analytics, Product Strategy & Delivery in both startup-minded organizations and in multi-national companies.
Let’s start with the big question - does every company need a Data Analyst, and what is the value they bring?
Nikolay: 90% of the companies need this role. The skillset of a Data Analyst is very broad and it’s good to have a dedicated role instead of putting the pieces together using several others. It’s a widespread issue for companies to have vast amounts of data and a lack of understanding of how to use it best. That’s why the role of the Data Analyst is much needed - to translate chaotic, large chunks of data into business knowledge.
Pavlin: I’d go one step further and say that every company needs a Data Analyst. Data is generated almost everywhere nowadays, and it’s way easier to collect and work with it. Even companies that are not historically data collectors now do it.
How important are communications skills for a Data Analyst?
Nikolay: A Data Analyst needs to be able to translate complex technical data and terms into a simple business language. A DA has the great advantage of communicating through graphics and reports. Sometimes, with a single graphic you can illustrate the same information that would otherwise take 3 sheets of paper to communicate.
Georgi: Data Analysts are not just the people who make the analysis, they also have to sell it to the business management. They must be convincing and express why data collection and analysis are important and why innovation is needed to improve the process - a lot of creativity is involved in how Data Analyst communicate with others.
Pavlin: The knowledge extracted from data is valuable, but it’s even more important how it’s communicated. For example, the Marketing team’s goal is to boost product or services sales The experts working in that department are very close to the clients and may resist adopting various automations. Тhe Data Analyst closely observes how they work and propose solutions for increased efficiency. However, the Data Analyst must also be prepared that these suggestions may not be welcomed with open arms so they must defend their proposal.
Part of the Data Analyst role is to understand other business departments and convince them of the benefits of using data. This makes communication skills crucial as at the end of the day, no matter how proficient you are with Python, most of these people are primarily concerned with the business. And it’s on you to convince them of the value that data can bring to it.
What technical skills are most important for a career as a DA?
Nikolay: Basic data science and statistical models and knowledge of tools like SQL, PowerBI, and Tableu. If you’re transferring from a development role, engineering or a similar field, you don't need to upgrade your technical skills that much rather than work on your ability to extract data insights and present them.
As for people coming from business backgrounds, like marketing experts, they need to build technical skillset as they most likely already have presentation skills.
Georgi: Developers and people from technical roles would need to learn Python, but transition should be easy with their existing skills.
More independent work is required for people with non-technical backgrounds. They need to have a strong interest in data. It can be tough for someone to work as a DA but not enjoy coding, for example.
Pavlin: A lot of roles now require some data knowledge. For example, a Marketing specialist who can analyze research data independently can become more valuable in their role instead of counting on someone else to do it.
You need to be interested in data. I’ve had cases where people with good technical knowledge of SQL and Python were not even a bit curious about the data itself and thus made mistakes in their work. The mindset and curiosity about the data are very important as it’s not just about executing automated tasks.
I think anyone can master the technical skills needed for a junior data analyst. Surely, if you’ve never encountered coding before, it would be harder for you, but I think anyone can learn it.
What’s your advice to people who have never thought about starting in the Data Analyst field?
Georgi: Regarding experience, you need some basic database and statistical knowledge, critical thinking and curiosity.
Nikolay: The first thing is to develop the basic skills - sending an inquiry, extracting data from databases and working with it. While Python is the standard, as a junior you have other options too. The key is to extract data, take a thorough look, consider edge cases and build a basic model. Even if you already have technical knowledge, you must have curiousity about the business and how data can impact it. Data Analysts can often serve as translators, bridging the gap between highly technical people and business executives.
Pavlin: To grow from junior to senior, you need to contribute knowledge and curiosity to the business. Knowing the basic SQL functions is important for a Data Analyst, followed by proficiency in Python. Some people prefer YouTube tutorials, I personally like to choose a topic of interest, as there are a lot of open databases around,and learn a new skill through hands-on data work.
Is AI and LLMs getting into the DA profession and how does it help?
Georgi: Certainly, there are now specialized tools for DA professionals, although they are not yet ready for mass use. For example, there’s a tool where you can ask a question using natural language, and it creates the database and visualizes it for you. Even if someone works with a tool like that, they still need to know the basics. How does the question you type get transferred to SQL? How is the graphic created, and how to interpret it? How do you communicate insights with other people in the organization?
Pavlin: What would be interesting is to be able to give the AI tool the structure of your database and ask a question using natural language. Having the database structure, the tool could generate the SQL code which you otherwise need to manually write. The Data Analyst would simply need to check it for mistakes. I think this review would always be necessary, emphasizing the importance of knowing how to do it manually by yourself.
Nikolay: The data needs to be well structured for an AI to work with it, and this is why the Data Analyst’s role will remain crucial. Nevertheless, AI tools will significantly save time in the coming years.
Exciting times ahead for Data professionals!
You can jump on that ship with the expert-led Upskill Data Analyst program by Telerik Academy. Start your career in data with our intensive 3-month training course and see where the future takes you. Apply for Upskill Data Analyst by March 10!