Digital Skills and Business Decisions Using Python

Introduction

The Course “Digital Skills and Business Decisions Using Python” is aimed at undergraduate and postgraduate students of Economics, recent graduates, jobseekers and working professionals who wish to acquire basic programming and data analysis skills; no prior programming experience is required.

The aim of the Course is to take participants from zero experience to writing, understanding and debugging Python code, as well as loading, cleaning, exploring and visualising a real-world dataset with the pandas and NumPy libraries. The emphasis is on data analysis rather than general software engineering, using text as the example, so that participants are able to follow the Unit “Text Analytics with Python”.

The Course is delivered entirely by distance, through asynchronous learning, through the Google Colab, without the need to install any additional software. Its total duration is 12 weeks and 90 hours of training. The learning process includes five (5) thematic units, each lasting two weeks, which combine asynchronous educational material with laboratory and practical exercises, with a total duration of 70 hours. During the last two weeks of the Course (weeks 11–12), trainees carry out a final data analysis project, lasting 20 hours, using a real dataset of ratings (review dataset), aiming at the practical application of the knowledge and skills acquired during the training.

Scientific Director

Eleni Kyrkopoulou,

Assistant Professor, Department of Economics, University of Piraeus

Target Audience

The Course is aimed at undergraduate and postgraduate students of Economics, recent graduates, jobseekers and working professionals who wish to acquire basic programming and data analysis skills. No prior programming experience is required.

Admission Requirements

Participants must have:

Secondary education
Very good knowledge of English
Basic computer skills
Internet access
A personal email account

Holding a higher education degree (from a university or an equivalent Greek or foreign institution) is a key selection criterion.

Delivery Method

The Course is delivered entirely by distance learning, using a dedicated e-learning platform. It is asynchronous distance learning supported by notes, which makes it particularly convenient for working people. This type of learning is a flexible form of training, as it is not restricted by time or place. It is based on the principles of open education and caters for the needs of personalised learning.

Successful completion of the Course requires attendance of all Teaching Units, completion of the weekly lab exercises, successful completion of the final data analysis project, through which the knowledge and skills acquired during the Course are assessed, and full payment of the fees.

Type of Certificate

Trainees who successfully complete the Course are awarded a Training Certificate by KEDIVIM of the University of Piraeus or a Certificate of Successful Attendance.

See a sample of the certificate awarded here

Units

Teaching/Thematic Unit 1: Introduction to Google Colab and Programming Principles with Python

Teaching/Thematic Unit 2: Data Structures & Control Flow

Teaching/Thematic Unit 3: Functions & Text Processing

Teaching/Thematic Unit 4: NumPy & First Steps in pandas

Teaching/Thematic Unit 5: Data Management & Exploratory Data Analysis (EDA)

Teaching/Thematic Unit 6: Final Project

Learning Outcomes

1In terms of knowledge:

Upon completion of the Course, trainees will be able to: describe the fundamental concepts of Python, such as variables, data types, control flow, functions and string handling, distinguish between the basic data structures (list, dict, tuple, set) and justify the criteria for choosing the appropriate structure for each task, explain basic concepts relating to vectors, such as the dot product, the norm and cosine similarity, define the basic concepts of tabular data (Series, DataFrame), as well as the concept of the dependent variable (Dependent Variable – DV).

2In terms of skills:

Upon completion of the Course, trainees will be able to: write and debug basic Python code, reading and interpreting error messages (tracebacks), process text using basic Python features (indexing, slicing, f-strings, string methods) and develop simple processing tools, such as word frequency counters, load, clean, filter, group and explore real-world datasets using the pandas and NumPy libraries, create clear and easy-to-understand exploratory data visualisations using the Matplotlib and Seaborn libraries.

3In terms of competences:

Upon completion of the Course, trainees will be able to: independently carry out Exploratory Data Analysis (EDA) on real-world datasets, identify and define the dependent variable in the context of a data analysis, present and effectively communicate the results of a data analysis through a concise and reproducible notebook, combining appropriate code with a clear and well-documented interpretation of the findings.

Assessment

Learning outcomes are assessed through:

1. Weekly laboratory exercises (Labs 1–5)

2. A final data analysis project during weeks 11–12

Trainees are assessed on the basis of weekly lab exercises and the submission of a final data analysis project at the end of week 12, using a real review dataset. The final project is assessed by the instructor on the correctness and completeness of the submitted code and interpretations in a reproducible notebook, and is accompanied by written feedback.

Duration:	3 Months / 90 Hours
ECTS:	5.35
Start Date:	30/10/2026
Field of Study:	Professional Skills Development
Language:	English
Scientific Director:	Eleni Kyrkopoulou

Educational Material

(+30) 210 414 2481, 2106, 2025

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