Financial management in data-driven organizations. Get to know the key topics in data science for finance.
Suggested by: Coursera (What is Coursera?)
No prior knowledge required
No unnecessary risks
The integrated course program is designed to introduce the applications of data science in the financial market, as well as to introduce the fundamentals of analytics in the field of finance to meet the growing demand for more advanced tools for collecting, analyzing, and processing data.
At the end of this program, you will be able to understand the main management models and the aspects related to implementing and monitoring business strategies using data-driven techniques and key platforms in the financial market.
The integrated course program does not require the development of a practical project. Assessment activities are carried out during each course.
Course 1
30 hours
4.4 (17 ratings)
Welcome to the Data-Driven Finance course. In this course, you will learn that data has become the central asset in business today. With the rise of big data and new technologies, organizations in the financial market are constantly innovating and discovering new ways to analyze the potential of the data available to them.
The course is divided into four modules, offered in learning weeks. Each module consists of videos, readings, and tests to assess learning.
Course 2
30 hours
4.3 (33 ratings)
In this course, you will learn the entire data science process and the use of technology in the connected world. The course is divided into four modules, offered in learning weeks.
Course 3
0 minutes
Welcome to the Data Science Tools: Introduction to R course. In this course, you will learn about the leading analytical tools on the market, including the basics of the R language.
Course 4
27 hours
4.6 (10 ratings)
Welcome to the Fundamentals of Artificial Intelligence for Finance course. In this course, you will learn about digital innovation in finance and its impact on finance and accounting functions.
At the end of the course, you will be able to understand topics such as machine learning, deep learning, and artificial intelligence.
The course is divided into four modules, offered in learning weeks.



