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Liudmila relies on practical experience and scores points for exactly that. In June 2021, she started her further Data Analyst training with a focus on the Python programming language with the “Women in Data” scholarship and started her new job as a Digital Analyst at a leading real estate portal shortly after graduation. She says it’s never too late to retrain and start a new career. Those who focus on practical experience are ahead of the game. Find out why in Part 1 of this two-part interview.
Dear Liudmila, you made an incredible career change and successfully started your job as a digital analyst last year. How did you manage to do that?
Thank you. When I moved to Germany in 2017, my visa requirements did not allow me to choose another Master’s degree program and I had to continue my previous degree program from Russia. However, being more of a practitioner than a theorist, I decided to pursue other avenues to achieve my goals. The main triggers were my passion for data and my willingness to be thrown into the deep end.
During the beginning of the Corona period, I started intensive self-study and tackled my first projects. At first, I wasn’t clear myself where this path would lead me, so I focused on my development and pursued my fascination with data.
Self-learners tend to be self-critical and often worry that their skills and knowledge might not be enough. Yet that’s not necessary at all. Even after graduating from university, we first enter the working world empty-handed. The theory often has little to do with the real work requirements. That’s why internships and student jobs are so important for gaining real-world experience. I applied for various jobs while I was still a student and that’s how I got my current job.
What role did the “Women in Data” scholarship and the Data Analyst training certificate play in your career transition?
The “Women in Data” scholarship by StackFuel and Telefónica Germany and the Data Analyst training came into my life at an absolutely right time and helped me a lot to make the breakthrough. I became aware of the scholarship at the time because it was specifically aimed at women/migrants/career changers like me. I particularly liked the fact that the Data Analyst training at StackFuel, through its practical focus, gave me the opportunity to work with real industry data and tackle realistic data analytics business scenarios.
Over the course of the five months of my Data Analyst training, I went through every step of a typical data analyst pipeline and learned to use the necessary Python modules. For me, it was like an internship of sorts. The topics and tasks were so close to reality that I could be sure that I would not only receive a vague theoretical knowledge, but that I would be able to deal with real business problems after graduation.
In the final project of the Data Analyst training, I was able to combine my accumulated knowledge and gain data-driven insights from the dataset of a telecommunications company. To help the company execute two major marketing campaigns, I analyzed the causes of high customer churn and identified target cities for campaigns. I was able to predict individual customer behavior to determine which customers needed to be targeted before they would unsubscribe.
Looking at my finished final project, I was really surprised at how much I learned during my time in Data Analyst training. Furthering my education at StackFuel also gave me the opportunity to meet other women who were interested in getting started in the tech industry. Their amazing stories inspired me to keep going.
What three characteristics excite you most about working with data?
First, data accompanies us in absolutely every aspect of life. The ability to see developments, truths, and trends behind simple numbers can help us not only improve existing approaches, but also create something entirely new through improved decision-making.
Working with data can be compared to an onion: You have to examine layer by layer. This is called a top-down approach, where you first look at a primitive level and then dig deeper to discover patterns and glean information from them.
Second, working with data is inextricably linked to working with people. Each analyst must have a thorough understanding of the industry and company in which she/he works. To do this, one must meet and communicate with various specialists and departments. To do this, we must present the results of our analyses to a non-technical audience and make them understandable.
For data to inspire action and change, one must tell the story that lies hidden within it. As a culture, we are made to tell and retain stories. Of course, this requires not losing sight of the technical side and not adapting the data to the story, but the story to the data.
Last but not least, data is inextricably linked to business problems and their solutions. When working with data, what matters most is the business vision, that is, the ability to add value to the business. It doesn’t help to dig into data and create incredibly complex models if the specifics of the business are not understood and considered. It’s about finding the most important questions for the business and answers to them. And these are very individual. This does not always require particularly sophisticated analyses.
We thank Liudmila for the interview and for taking the time to talk to us. If you want to read more from Liudmila, don’t miss part 2 of her interview on lifelong learning, how she built on her Data Analyst training and women in data.