Introduction
The Graduate Diploma in Data Science at ACT - The American College of Thessaloniki aims to provide to participants the necessary skills to comprehend big data through hands-on practice, enabling them to deliver their expertise on multiple scientific domains. Through this hybrid delivery program, you will develop an up-to-date understanding of modern data analytics, learn the foundations of data analysis, develop and practice basic and advanced methods for analysis through the use of relevant tool sets as well as delve into the fascinating world of Machine Learning.
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Who should consider this diploma
The Graduate Diploma in Data Science is designed for professionals from diverse backgrounds such as Science (Mathematics, Physics, Chemistry, Biology), Engineering, Bio-medicine, Computer Science, Pharmaceutical and Medical sector, etc., willing to enhance their personal skills and expertise on the state-of-the-arts area of Big Data and Artificial Intelligence and directly apply this knowledge to their respective fields. The flexibility provided by the hybrid format of this program allows candidates to balance their studies with their busy schedules and sustain their work-life balance. The Graduate Diploma in Data Science is an ideal choice for those seeking to make a positive impact in their organizations and industries.
Learning objectives
- Develop a deep understanding of the principles and practices of Data Science, Big Data and Artificial Intelligence.
- Design and implement strategies for problem solving and decision making.
- Evaluate the effectiveness of different schemes and methodologies relevant to Big Data, Data Analytics, Databases and Artificial Intelligence.
- Understand the role of Data Science in real-life systems.
Career outcomes
Upon successful completion of the Graduate Diploma in Data Science, participants will be capable of pursuing careers and roles in a wide range of industries. Upon program completion, they will have acquired such knowledge, skills and competences that are highly valued by today’s competitive market and will provide the graduates of this program the opportunity to pursue careers as Big Data Engineers, Data Architects, Database Managers, Data Scientists, Data Analysts, Database Developers, Machine Learning Engineers, Research Scientists and NLP Engineers, in diverse sectors such as Computer Science, Bio-medicine and Medicine.
Courses included
MSDS 600: Introduction to Data Science
Introduces foundational topics of the data science life cycle. It covers essential concepts and techniques necessary for working with data, extracting insights, and building predictive models. The key topics covered in this course include: data manipulation, exploratory data analysis, data visualization and statistical modeling. Additionally, it explores various data science challenges and applies cutting-edge algorithms such as classification, regression techniques and recommendation systems to solve them. Both R and Python programming languages are used throughout the course for data processing and modeling tasks. Prerequisite(s): Completion of Python self-assessment, or Python coding experience. Consult your admissions counselor, academic success coach, or faculty advisor on the details regarding the Python prerequisite.
MSDS 610: Data Engineering
Presents techniques for designing, building, and managing information with relational databases, NoSQL databases, and big data infrastructure. Provides hands-on experience running the MapReduce algorithm on Hadoop ecosystem. Prerequisite(s): MSDS 600.
MSDS 650: Data Analytics
Focuses on techniques for exploring and analyzing large datasets to uncover hidden patterns, relationships and insights that can aid in decision-making. Techniques include experimental design, hypothesis testing, probability distributions, classification and clustering algorithms. Additionally, the course presents valuable processing and modeling techniques for text data specifically applied in Natural Language Processing (NLP) and Information Retrieval (IR). Prerequisite(s): MSDS 600.
MSDS 680: Machine Learning
Emphasizes on the use of data in the field of Machine Learning (ML), examining both theory and software implementation of models, methods and learning algorithms. Topics span over different ML paradigms, supervised, unsupervised, self-supervised and reinforcement learning. It starts with the basics of the Artificial Neuron and extends up to Deep Neural Networks (DNN) and Deep Learning methods, with source code provided for example solutions on tasks that use diverse types of data. Additionally, classic ML approaches for classification and clustering (Bayesian, k-NN, Decision Trees, k-Means) are investigated and compared using proper performance metrics, according to a quantitative evaluation assessment strategy. Prerequisite(s): MSDS 650
Delivery
Flexible Delivery: One week face-to-face and one week online teaching (synchronous teaching and asynchronous work)
Fall 2024 - Term 2
Date
|
Course
|
Time Slot
|
Delivery Format
|
Friday, November 29, 2024
|
MSDS 600
|
18:00 - 22:30
|
Face-to-Face
|
Saturday, November 30, 2024
|
MSDS 610
|
11:00 - 15:30
|
Face-to-Face
|
Friday, December 6, 2024
|
MSDS 600
|
18:00 - 20:00
|
2-hours online + asynchronous
|
Saturday, December 7, 2024
|
MSDS 610
|
11:00 - 13:00
|
2-hours online + asynchronous
|
Friday, December 13, 2024
|
MSDS 600
|
18:00 - 22:30
|
Face-to-Face
|
Saturday, December 14, 2024
|
MSDS 610
|
11:00 - 15:30
|
Face-to-Face
|
Friday, December 20, 2024
|
MSDS 600
|
18:00 - 20:00
|
2-hours online + asynchronous
|
Saturday, December 21, 2024
|
MSDS 610
|
11:00 - 13:00
|
2-hours online + asynchronous
|
Friday, January 17, 2025
|
MSDS 600
|
18:00 - 22:30
|
Face-to-Face
|
Saturday, January 18, 2025
|
MSDS 610
|
11:00 - 13:00
|
Face-to-Face
|
Friday, January 24, 2025
|
MSDS 600
|
18:00 - 20:00
|
2-hours online + asynchronous
|
Saturday, January 25, 2025
|
MSDS 610
|
11:00 - 13:00
|
2-hours online + asynchronous
|
February 3-7, 2025
|
Reading week and Final Examinations. Dates to be announced.
|
Spring 2025 - Term 3
Date
|
Course
|
Slot
|
Delivery
|
Friday, February 14, 2025
|
MSDS 650
|
18:00 - 22:30
|
Face-to-Face
|
Saturday, February 15, 2025
|
MSDS 680
|
11:00 - 15:30
|
Face-to-Face
|
Friday, February 21, 2025
|
MSDS 650
|
18:00 - 20:00
|
2-hours online + asynchronous
|
Saturday, February 22, 2025
|
MSDS 680
|
11:00 - 13:00
|
2-hours online + asynchronous
|
Friday, February 28, 2025
|
MSDS 650
|
18:00 - 22:30
|
Face-to-Face
|
Saturday, March 1, 2025
|
MSDS 680
|
11:00 - 15:30
|
Face-to-Face
|
Friday, March 7, 2025
|
MSDS 650
|
18:00 - 20:00
|
2-hours online + asynchronous
|
Saturday, March 8, 2025
|
MSDS 680
|
11:00 - 13:00
|
2-hours online + asynchronous
|
Friday, March 14, 2025
|
MSDS 650
|
18:00 - 22:30
|
Face-to-Face
|
Saturday, March 15, 2025
|
MSDS 680
|
11:00 - 13:00
|
Face-to-Face
|
Friday, March 21, 2025
|
MSDS 650
|
18:00 - 20:00
|
2-hours online + asynchronous
|
Saturday, March 22, 2025
|
MSDS 680
|
11:00 - 13:00
|
2-hours online + asynchronous
|
March 31 - April 4
|
Final Examinations. Dates to be announced.
|
Completion requirements
To successfully complete the academic credit-bearing Graduate Diploma in Data Science, participants must achieve a minimum GPA of 2.33 in the four courses taken. Note: A GPA of 2.33 is equivalent to a C+ (0-4 scale).
Continue your education with an MS in Data Science at Regis University
Regis University will accept into its MS in Data Science program any recipient of ACT’s Graduate Diploma in Data Science who properly applies to Regis for admission, fulfills all applicable admission requirements, including, but not limited to, demonstrating through an official transcript issued by ACT that s/he maintained a cumulative grade point average of 3.0 or higher at ACT.
Regis University has been designated as a National Center for Excellence in Information Assurance Education by the NSA and DHS since 2007, is home to ABET- and GAC-accredited programs, and we're proud to produce graduates at both the undergraduate and graduate level who continue on to become leaders and stewards of society.
The MS in Data Science offered at Regis University has been selected as one of the Best Online Master's in Data Science Programs, as well as Most Affordable Master's in Data Science in 2023 (source: Fortune Education).
Admission requirements
- A Bachelor’s degree from an accredited institution in Biology, Engineering, Information Technology, Economics, Business, Mathematics, Psychology or other Science and wish Upon program completion, the participants
- Proficiency in the English language evidenced through a B2 certification in English Language. ACT graduates and graduates from other accredited English-speaking institutions are not required to submit evidence of Proficiency in the English language.
If you have already set up your mind and wish to apply to this program, please submit the application available here.
For more information
Reach out with any questions or concerns about the program:
ACT Admissions Office
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Tel: (+30) 2310 398 398
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