Credit Risk Analysis with Python Pandas
Learn to analyze credit card data using Python's pandas library. Master data exploration, visualization, and risk assessment in just one hour.
Learn to analyze credit card data using Python's pandas library. Master data exploration, visualization, and risk assessment in just one hour.
This hands-on guided project teaches you how to perform credit risk analysis on a credit card client dataset using Python's pandas library. In just one hour, you'll gain practical experience in data analysis, a crucial skill in finance, economics, and various other fields. The course covers importing required libraries, exploring datasets, analyzing data, and visualizing results. You'll learn to use pandas for loading, processing, and analyzing large datasets with SQL-like queries. The project emphasizes pandas' advantages, including efficient data representation and simplified code for handling complex data. By working with a real-world credit card dataset, you'll develop job-ready skills in preliminary data analysis and credit risk assessment. This course is ideal for those with basic Python knowledge looking to enhance their data analysis capabilities using one of the most popular tools in data science.
Instructors:
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What you'll learn
Import and utilize Python libraries for data analysis, focusing on pandas
Explore and understand the structure of a credit card client dataset
Perform preliminary data analysis on financial datasets using pandas
Conduct credit risk analysis using data-driven methods
Create dependencies among existing attributes in a dataset for deeper insights
Visualize data analysis results using various plot types in Python
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
This guided project focuses on teaching data analysis using Python's pandas library, with a specific application to credit risk analysis. Participants will work with a credit card client dataset, learning how to import and explore data, perform calculations, and visualize results. The course covers key aspects of data analysis, including data exploration, building dependencies among dataset attributes, and creating various types of plots for data visualization. By using pandas, learners will experience its advantages in handling large datasets efficiently, which is crucial in fields like finance, economics, and web analytics. The project provides hands-on experience with real-world financial data, helping participants develop practical skills that are immediately applicable in data science and analytics roles. The use of a cloud-based IDE with pre-installed software ensures that learners can focus on the analysis without setup complications.
Fee Structure
Instructor
2 Courses
Pioneering AI Professor and Expert in Complex Systems Modeling
Dr. Yaroslav Vyklyuk has established himself as a distinguished academic leader serving as a full professor at the Lviv Polytechnic National University's Department of Artificial Intelligence Systems. His prolific academic career is marked by authoring over 210 scientific works and 10 monographs and books, while actively serving on the Editorial Board of six international scientific journals. As a member of the Academic Councils for Ph.D. and DrSc thesis defense in Mathematical modeling and computational methods, he plays a crucial role in shaping future researchers. His extensive research portfolio encompasses Data Science, Applied System Analysis, and Mathematical Modeling, with particular emphasis on decision-making in complex dynamic systems including socio-economic, geographical, tourist, and crisis systems. Dr. Vyklyuk's innovative approach combines cutting-edge technologies such as Artificial Intelligence, Data Mining, Big Data, and Parallel Calculations with traditional methodologies like Statistics, Econometrics, and Econophysics, implementing these advanced mathematical methods into practical applications across information systems, web platforms, and geographic information systems
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