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Data Tools_Financial Managemen

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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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.

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.

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.

Certificate

Participants who complete the course will receive a continuing education certificate with ECTS credits.

Course structure

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.

Course details

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


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.

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.

  • 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.

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.

Data Tools for Financial Management is in preparation

If interested, add your email to the waiting list and we'll send you a notification when the course details are ready and registration is possible.

Join waiting list

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.

Learning Journey Advisor

Kadri Osula

Learning Journey Advisor

kadri.osula@ebs.ee
Kursus on osa mikrokraadist