Data Science, or data science, plays an extremely important role in
today's world, transforming the way we make decisions,
conduct business and solve problems. In an era of tremendous growth in the amount of
data generated daily by a variety of sources, from social media
to information systems, the skillful use of this data is becoming a key
success factor. Data Science combines statistical techniques,
data analysis, machine learning and big data processing to uncover
hidden patterns, relationships and information that can lead to more accurate
conclusions and better business decisions. Companies using Data Science can
optimize their processes, improve efficiency, better understand
customers' needs and predict market trends. In addition, Data Science plays
a key role in the development of new technologies such as artificial intelligence,
process automation and the Internet of Things (IoT). With the growing importance of
data as a strategic resource, Data Science skills are becoming
increasingly sought after on the job market, highlighting the central role of this
field in today's world.

The use of deep learning methods, advanced statistical analysis and
machine learning classifiers in Data Science opens the door to a revolution in
data analysis and decision-making. Deep learning methods, based on
multilayer neural networks, make it possible to automatically detect
complex patterns and relationships in data to create accurate
predictive and classification models. Advanced statistical analysis,
including techniques such as cluster analysis, principal component analysis or
feature space analysis, enables the identification of significant relationships between
variables and the reduction of data dimensionality, leading to more
transparent and interpretable results. Machine learning classifiers,
including models based on decision trees, SVM (Support Vector
Machines) algorithms, or k-nearest neighbor algorithms, allow efficient
data classification and the creation of recommendation systems. Combined, these
techniques create powerful data analysis tools that find application in
various fields, from business to medicine, enabling a better understanding of
data, predicting trends and making more accurate decisions.
Modern Data Science relies on the synergy of these methods, leading to
innovative solutions and driving advances in data analysis.

Career prospects for graduates of the
Data Science specialty are promising and booming in view of the growing
importance of data in the modern world. Graduates with
advanced knowledge in data analysis, machine learning, statistics
and programming are becoming highly sought-after professionals on the job market.
Employment opportunities for them are extremely broad, covering sectors such as
finance, medicine, retail, marketing, industry, and research. In
data analytics, they can work as data analysts, data scientists or

data engineers, engaged in collecting, processing and analyzing
data to generate business insights and make strategic decisions.
In the area of artificial intelligence and machine learning, they can serve as
researchers, creating and refining algorithms and predictive models. Companies
technology companies, corporations, government agencies and academic institutions are looking
for qualified professionals to develop and implement innovative
data-driven solutions. Additionally, there is no shortage of opportunities to work as consultants or
freelancers, providing data analysis services to various clients. With
increasing demand for data experts, career prospects for
Data Science graduates are promising and offer many
opportunities for professional and personal growth.

Sample thesis topics:

1. "Application of deep learning in medical image analysis for
   disease diagnosis".
2. "Investigating the effectiveness of various classification algorithms in predicting
   customer behavior in e-commerce."
3. "Social media sentiment analysis as a tool for
   monitoring public opinion"
4. "Using machine learning to predict market trends
   from financial data"
5. "Creating a recommendation system for a streaming platform based
   on user preferences."
6. "IoT sensor data analysis for process optimization in
   smart buildings"
7. "Development of predictive models for personalized medicine based on
   genetic and clinical data."
8. "Automatic fraud detection in banking transactions using
   machine learning algorithms."
9. "Cluster analysis of geolocation data to understand
   customer behavior and plan point-of-sale locations"
10. "Application of natural language processing techniques.

Lab

The latest IT infrastructure PJAIT, which provides students with access to modern tools and technologies necessary for effective learning and practical application of knowledge, is always used for the implementation of specializations.

List of supervisors:

- dr hab. Grzegorz Marcin Wójcik, Prof. PJAIT
- dr hab. Andrzej Wodecki, Prof. PJAIT
- Dr. Dominik Deja
- Dr. Bartłomiej Balcerzak
- Dr. Dominika Pawlik
- Dr. Wojciech Oronowicz-Jaśkowiak

et al