Data Science – Software Development

Data Science – Software Development

Applied-Data-Science-scaled

Information

Location: Androutsou 21, Thessaloniki, GR
Duration: 29 weeks, 6h/week (Oct 2025)
Full Price: 200€/month, discount based on the tech assessment results
Participants’ Total: 25
Scholarships: 2 Scholarships available based on the test-out assignment given upon completion of October 2025 Bootcamp
Seminar Language: Greek
Course Coordinators: Giorgos Kantasis, Dimitris Tragoudaras

Timeline

Application Opens: 20 May
Bootcamp Starting Date: 1 October
Bootcamp Schedule: 3 days/week, 3h/day
Core Program Starting Date: November
Core Program: 2 days/week, 3h/day

Selection Criteria

Technical Proficiency (40%) – Strength of technical background and potential.
Motivation (40%) – Clear enthusiasm and alignment with the program’s employability goals.
Program Fit (20%) – Contribution to the diversity of perspectives in the program.

Data Science Program Expectations

High potential for employment through our hiring partners.
Hands-on Learning with real-world projects and simulation of working environments.
Portfolio Development.
Learn in-demand technologies.
Regular guidance from experienced instructors and industry mentors.

Course Description

A Hands-on Educational Program for Developers to Learn Data Science

Course Objectives

Teach core Data Science technologies
Provide real-world project experience & build your portfolio
Expand your career prospects
Get mentored by industry experts
Build connections within the Data Science Community

Application Process

Screening
One-One & Group Calls
Tech Assessment
Evaluation

Candidate Eligibility

University Graduate
Competency in English
Able to attend most of the classes delivered in-person

Scholarship Structure

Type: Single-tier scholarship

Curriculum

Bootcamp Phase

Week 1: Introduction to Git and IDEs

Session 1: Git and Code Editor Setup
Session 2: Advanced Git and GitHub
Session 3: Working with Git and GitHub

Week 2-3: Programming, Control Flow and Data Structures

Session 4: Basic Programming Concepts
Session 5: Control Flow
Session 6: Practice Control Flow
Session 7: Basic Data Structures
Session 8: Basic Algorithms
Session 9: Practice on Data Structure and Algorithms

Week 4: Project Management and Final Assessment

Session 10: Introduction to Project Management Tools
Session 11: Final Assessment
Session 12: Career-Stream Guidance

Semester A: November 2025 – March 2026 | Data Science and ML in Python

Phase 1: Fundamentals of Python Programming and Statistics (Weeks 1-5)

Week 1: Introduction to Python and Basic Syntax
Week 2: Control Flow and Functions
Week 3: Data Structures in Python
Week 4-5: Introduction to Statistics

Phase 2: Data Preparation and Pre-processing (Weeks 6-10)

Week 6: Data Handling with NumPy
Week 7: Data Manipulation with Pandas
Week 8: Advanced Pandas
Week 9-10: Data Cleaning and Pre-processing Strategies

Phase 3: SQL and Data Visualization (Weeks 11-15)

Week 11: SQL Basics
Week 12: Advanced SQL
Week 13: Data Visualization with Python
Week 14-15: Data Visualization with PowerBI

Phase 4: Machine Learning and AI with Python (Weeks 16-20)

Week 16: Introduction to Machine Learning
Week 17-18: Supervised Learning Algorithms
Week 19: Unsupervised Learning Algorithms
Week 20: Model Evaluation and Tuning

Semester B: April 26 – June 26 | Advanced Topics in Data Science – ML and AI

Week 21-22: Neural Networks
Week 23-24: Natural Language Processing (NLP)
Week 24-25: Support Vector Machines (SVM)
Week 25-26: Time-series Analysis
Week 27: Ensemble Methods
Week 28: XGBoost
Week 29: Large Language Models

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