Data Tools for Financial Management

Modern finance leaders must be fluent in data. This course develops the analytical mindset and technical toolkit needed to turn complex financial and economic data into actionable insights. Participants learn how to extract and structure data from diverse sources, apply statistical and econometric models, and present findings using Excel, Power BI, and Python. By the end of the course, learners can support high-stakes financial decisions with clarity, precision, and credibility.

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Practical info

Course Date

17.10.2026 - 28.11.2026

Capacity

3 ECTS (78 academic hours, of which 16 in classroom and 62 individual work)

Lecturers

Samuli Saarinen

Language

English

Method

In class

Prerequisites

Bachelor’s degree or equivalent qualification.
At least 3 years of work experience in business or entrepreneurship
Prior studies or experience in finance or economics. Or knowledge of finance, accounting and statistics.
Participants from non-EU countries must already hold a valid temporary residence permit. No permit will be issued for short courses.

Location

EBS University, A.Lauteri 3, Tallinn

Price

€565

This course gives you practical skills in financial data analysis using Excel, Power BI, and Python. You'll learn to turn data into insights and support smart decisions, essential for today’s tech-savvy finance professionals

The course builds a strong foundation in analytical thinking and data literacy, enabling finance professionals to confidently navigate complex datasets, collaborate across functions, and contribute to strategic decision-making in a tech-driven business environment

Who is Data Tools for Financial Management intended for?

This course is intended for finance professionals, auditors, business analysts, entrepreneurs, and career changers who want to strengthen their data-driven financial analysis, reporting, and decision-making skills using modern tools.

Explore the course

The course takes place between October 17 and December 12. The detailed schedule will be confirmed in June.
After the sessions, participants are expected to work independently to prepare and present their final report.

Topics Covered

Participants will learn how to acquire, clean, and structure macroeconomic and company-level financial data from various sources, including APIs. The focus is on ensuring data quality and preparing datasets for reliable analysis using modern tools such as Excel and Python.

The course covers statistical and econometric methods to explore financial relationships and identify key insights. Participants will learn how to visualize and clearly present analytical findings using tools like Power BI and Matplotlib to support evidence-based financial decision-making.

Intended Learning Outcomes

Upon completion of the course, the student will be able to:

1. Acquire and integrate macroeconomic, industry, and company-level financial data from various sources, including APIs.
2. Clean, structure, and manage financial datasets to ensure accuracy and analytical consistency.
3. Apply statistical and econometric methods such as regression and introductory time-series analysis to explore financial relationships.
4. Visualize and communicate financial insights effectively using tools such as Excel, Power BI, and Python (Matplotlib).
5. Interpret data-driven results to support evidence-based financial and business decision-making.
6. Demonstrate independent problem-solving and analytical skills through practical data analysis projects.

Requirements for completing the course

To complete the course, learners must pass all courses with at least a grade of E (51–60%). Assessment is course-based, with differentiated grading (A–F) based on practical tasks and learning outcomes.

Assesment criteria

Homework Assignments – Data acquisition via APIs, cleaning, exploratory analysis, and application of regression and time-series models.
Final Project – Comprehensive financial data analysis with visualizations (Excel, Power BI, Python), demonstrating decision-support skills.
Active Participation – Engagement in practical exercises, discussions, and peer feedback.

Description of the learning environment

The course is delivered on-site at EBS with access to a modern learning environment, including the cloud-based Canvas platform, study materials, and the EBS library. Digital tools and financial data sources (APIs, databases, reporting platforms) are used throughout. Software tools include Excel, Power BI, and Python (with Pandas and Matplotlib). Participants must bring a laptop with at least 8GB RAM, Excel, free Power BI Desktop, and a working Python environment.

Prerequisites

The course is intended for participants who already have the ability to read and interpret financial statements and who understand the core principles of corporate finance, business mathematics or similar

Digital tools and financial data sources—including APIs, databases, and reporting platforms—are used throughout the learning process. The course applies software tools such as Excel, Power BI, and Python (including the Pandas and Matplotlib libraries). Participants are expected to have a laptop with at least 8GB of RAM and the following installed: Microsoft Office (Excel required), Power BI Desktop (free), and a functioning Python environment with Pandas and Matplotlib libraries.

Course Instructor

Samuli Saarinen

Research Fellow

Samuli Saarinen holds a Bachelor's and Master's degree in International Business Administration from Estonian Business School and is currently pursuing a PhD in Management. His research focuses on applying statistical models and social media data to improve AML risk detection and automation. He has practical experience in logistics and banking, including roles at Nordea and K2 Integrity, and has published several peer-reviewed articles. Samuli is also developing a business application based on his research and is proficient in R, Python, and SQL.