[interview] Beyond bits and bytes — insights from data science

More and more businesses are witnessing a surge in data creation, setting the stage for transformative possibilities. While navigating this data-rich environment, the question arises: How can organizations harness the potential within this vast ocean of information to stay competitive and meet the dynamic needs of modern customers?

In this interview, we explore the data science field with Katarzyna Hewelt, a Data Scientist at CodiLime. We'll talk about Katarzyna's perspectives on the evolving field, and how she navigates through the domains of data, technology, and personal growth. In a world flooded with data, Katarzyna provides valuable insights into the art and science of transforming raw information into actionable intelligence.

Could you introduce us to data science? What should we know about it and why do companies need it? 

Katarzyna Hewelt: Data science is a multidisciplinary field that combines statistics, computer science, and problem-solving skills to analyze and interpret data, playing a crucial role in todays business world. It empowers companies to make smarter, data-driven decisions by transforming raw data into valuable insights. This is achieved through the use of programming languages like Python and various statistical methods to uncover patterns within data. A significant advantage of data science is its ability to provide deep insights into customer behavior and preferences, allowing companies to tailor their products and services more effectively. 

Additionally, it aids in the early identification of market trends, giving businesses a competitive edge. Another key benefit is the enhancement of efficiency; automating data processes saves considerable time and resources, streamlining operations.

In essence, data science is an invaluable asset for companies, enabling them to understand complex data landscapes and leverage this understanding for strategic planning, innovation, and maintaining a competitive stance in their respective industries. 

How has data science influenced the IT industry? 

K.H: In the IT industry, data science has been a game-changer by enhancing computing capabilities and introducing advanced analytical techniques. It's reshaped how we handle and interpret vast amounts of data, leading to more efficient and intelligent systems.

For instance, machine learning, a key part of data science, has become essential in developing smarter software that can learn and adapt. Big data management, another critical aspect, allows us to process and analyze large datasets more effectively, uncovering insights we couldn't see before. Predictive analytics, a result of data science, helps in forecasting trends and user behaviors, enabling IT companies to anticipate and solve problems before they occur. 

Lastly, data science has greatly improved user experiences by allowing for more personalized services and products, making technology not just more powerful, but also more user friendly. Overall, data science has not just been a tool but a transformative force in the IT sector.

What are the most common challenges related to starting to use data science? 

K.H: I would say that both data quality and quantity are crucial. Having sufficient data of good quality is essential. Data wrangling, which is the process of transforming and mapping raw data into a more usable and understandable format, can be challenging. This includes tasks like cleaning, structuring, and enriching raw data, especially with incomplete data sets. I also want to mention tool proficiency - there are many data science tools and libraries, which can be overwhelming. As with every programming field, these tools are constantly changing, and staying up to date with them is necessary. 

Moreover, data science is not just about coding, cleaning datasets, and looking for patterns; soft skills are also desirable in this field. It can be challenging to present findings in a clear, concise, and actionable way, especially for beginners who may focus more on technical aspects.

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What does a data scientist's working day look like?

K.H: As a data scientist working remotely, my day is structured yet dynamic. It typically begins with tackling tasks assigned in a sprint. A sprint is a set period during which specific work has to be completed and made ready for review. It's a fundamental part of the Agile methodology, which is all about adaptive planning and fast delivery.

Every day, our team has a stand-up meeting – and no, it's not stand-up comedy, although we do have our light-hearted moments! This meeting is a quick, daily check-in where each team member reports on their progress, discusses any obstacles they might be facing, and asks for help if needed. It's essential for keeping everyone aligned and the project on track.

The rest of my day can vary. A good chunk of it is dedicated to coding and data analysis. I might also have pair programming sessions, where I collaborate with a colleague to work through complex pieces of code or troubleshoot issues together. This collaborative approach not only helps solve problems more efficiently but also fosters knowledge sharing and skill development.

Depending on how I'm progressing with my sprint tasks, I also set aside time for my own development goals. This could involve studying new data science techniques, exploring the latest tools in the field, or even participating in online webinars and communities to stay updated with industry trends. Continuous learning is a big part of being a data scientist as the field is always evolving.

Let’s talk about trends. What changes can we expect in the future related to data science?

K.H: In the future, data science is set to become even more closely intertwined with AI and machine learning. As these technologies, including the rapidly evolving Large Language Models (LLMs), continue to advance, they're becoming integral to data science. This leads to sophisticated, self-learning algorithms that allow for more efficient data processing and deeper insights. However, amidst the current hype around AI and LLMs, with new tools and solutions emerging daily, we might see a period of stabilization in the near future. This anticipated cool down could lead to the development of more mature, stable solutions and frameworks.

The demand for data scientists will surge as more sectors recognize the importance of data-driven decision-making, leading to a focus on specialized education and training. Alongside this, with growing concerns about data usage and AI decision-making, we'll see a stronger emphasis on ethical AI and data privacy. This shift aims to ensure more responsible use of data and transparency in AI processes.

A crucial aspect will be the rapid evolution of AI tools themselves, including a move towards more stable and mature frameworks following the initial excitement and rapid growth phase. Data scientists will need to continuously update their skills to keep up with these advances, which are significantly enhancing efficiency and capabilities in data processing and analysis.

Automation will also play a vital role, taking over routine tasks and allowing data scientists to concentrate on more complex and strategic aspects of their work. So, looking ahead, the field of data science isn't just about handling more data but doing so in smarter, more ethical, and more efficient ways, while adapting to the evolving landscape of AI and LLM technologies.

And the last question. From your experience, what skills are crucial for individuals looking to succeed in data science?

K.H: In data science, success hinges on a mix of technical skills, analytical thinking, and strong communication abilities. Proficiency in programming languages like Python or R, and a firm grasp of statistics and machine learning are foundational. Equally important is the ability to interpret data and apply insights to real-world problems. Good problem-solving skills are essential for tackling complex challenges. 

I’d personally favor effective communication as it is key, as it's crucial to translate technical findings into clear, actionable insights for non-technical stakeholders. Moreover, with the rapid evolution of the field, a commitment to continuous learning and staying updated with new technologies and trends is vital. Collaboration is also important, as data science often involves working in teams and with diverse groups. In short, blending technical expertise with soft skills like communication and teamwork is crucial for thriving in the dynamic world of data science.

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Katarzyna Hewelt, Data Scientist at CodiLime 

Unlike the traditional path through computer science studies, Katarzyna pursued power engineering studies but found her calling in data through a coding bootcamp. Now, she is a proficient data scientist with a strong machine learning and NLP foundation. 

She is an active member of TEDxWarsawWomen, reflecting her commitment to empowering women in technology. She actively engages with the community, demonstrating her commitment to fostering diversity and inclusion in the tech industry. This year, Katarzyna will also be a speaker at the Data Science Summit and DevAI. 

Katarzyna is currently an integral part of the CodiLime team, contributing to the company's expertise in large language models. She plays a crucial role in leveraging her data science expertise to provide valuable insights for the company's clients.

Beyond her professional achievements, she is a people person who enjoys connecting with others and is always open to new experiences and knowledge. 

Katarzyna combines her passion for data with a love for travel, exploring new destinations worldwide (especially during the cold winter time when she prefers to work remotely from more sunny places than Poland).