Professional Diploma Outline

1.Core Courses:

Introduction to Data Engineering (3 credit hours)

The emergence of massive datasets provides the primary drive for the field. Data can be video surveillance, social media, speech, text, telecommunications, astronomy, etc. This course emphasizes the practical techniques for working with large-scale data.

Introduction to Machine Learning (3 credit hours)

The course covers basic statistical principles of supervised machine learning, as well as some common algorithmic paradigms such as deep neural computing, and kernel methods.

Computer Systems for Data Engineering (3 credit hours)

This course is an introduction to computer architecture and distributed systems with emphasis on warehouse scale computing systems. Topics include fundamental tradeoffs in computer systems, hardware and software techniques for exploiting instruction-level parallelism, data-level parallelism and task level parallelism.

Exploratory Data Analysis and Visualization (3 credit hours)

This course covers fundamentals of data visualization, such as layered grammar of graphics, perception of discrete and continuous variables, introduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.

Data Engineering Lab 1 (1 credit hour)

This lab covers main programming paradigms for machine learning as well as platforms, including programming in Python, R, using tools such as Anaconda.

Data Engineering Lab 2 (1 credit hour)

This lab covers more advanced projects in data engineering in addition to data management and cloud engineering.

2.Elective courses:

In addition to the core courses, participants can choose 2 elective courses (3 credit hours each) from the list below:

• Probability and Statistics for Data Engineering
• Topics in Engineering: Applied Machine Learning
• Bioinformatics
• Applied Machine Learning for Image Analysis
• Deep Learning for Computer Vision, Speech, and Language
• Introduction to Big Data in Finance
• Introduction to Big Data in Analytics

Admission Requirements

1. A bachelor’s degree from a practical faculty (or any other equivalent national or international certificates approved by the Supreme Council of Universities in Egypt) with satisfactory prior knowledge in relevant disciplines.
2. Showing proficiency in the English language by providing one of the following:
   • TOEFL score of 60 IBT
   • IELTS score of 5.5
   • proof of studying in English
   • passing an English exam conducted by E-JUST
3. Passing the entrance examination.
4. Passing a personal interview.
5. Applicants who do not have educational background may be required to take complementary courses before starting regular courses or in parallel.

Admission Process:

1. Fill in the online application and upload all the required documents.
2. If the application meets the criteria, you will receive an email inviting you for an interview.
3. Final acceptance email and payment of the first tuition fee installment.

For more information or inquiries, call us on 16448 or email us on itu@ejust.edu.eg