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Course Code |
Course Title |
Weekly Hours* |
ECTS |
Weekly Class Schedule |
T |
P |
AID402 |
Applied Data Engineering |
3 |
2 |
6 |
|
Prerequisite |
None |
It is a prerequisite to |
|
Lecturer |
|
Office Hours / Room / Phone |
|
E-mail |
|
Assistant |
|
Assistant E-mail |
|
Course Objectives |
• By the end of this course, attendees will gain comprehensive knowledge of the essential components of Big Data Analytics and its associated ecosystems.
• Participants will possess the skills to design, build, test, and sustain architectures such as databases and high-capacity processing systems. They will be capable of conducting batch and real-time streaming analytics on both structured and unstructured data, and demonstrate proficiency in professional data management. Additionally, they will be equipped to create visually appealing visualizations and dashboards.
• This course offers a thorough, progressive, and practical hands-on learning experience.
|
Textbook |
There is no required textbook. |
Additional Literature |
|
Learning Outcomes |
After successful completion of the course, the student will be able to: |
- Analyze the characteristics of data-intensive applications that rely on streaming data and propose appropriate solution architectures. This includes considering a combination of batch and streaming data processing techniques.
- Develop software applications that incorporate the analysis techniques and technologies covered in the course.
- Utilize frameworks for large-scale distributed machine learning in their data analysis and processing tasks.
|
Teaching Methods |
The course will begin with a one-hour theoretical overview, providing a thorough explanation of the topic's background. Following that, we will proceed to practical programming exercises and hands-on practice. |
Teaching Method Delivery |
Face-to-face |
Teaching Method Delivery Notes |
|
WEEK |
TOPIC |
REFERENCE |
Week 1 |
Features of Data Engineering |
|
Week 2 |
Metadata Management |
|
Week 3 |
Consolidating Multiple Data Sources |
|
Week 4 |
Data Ingestion, Cleansing and Transformation |
|
Week 5 |
Hadoop Architecture and Ecosystem |
|
Week 6 |
Flat Files Ingestion into Hadoop |
|
Week 7 |
RDBMS and Hadoop Integration |
|
Week 8 |
Hive Data Processing |
|
Week 9 |
Interactive Query using Impala |
|
Week 10 |
Log Files Handling and Processing |
|
Week 11 |
Introduction to Spark |
|
Week 12 |
Processing Data using PySpark |
|
Week 13 |
Spark Data Query |
|
Week 14 |
Real-time Data Analytics in Spark Streaming |
|
Week 15 |
|
|
Assessment Methods and Criteria |
Evaluation Tool |
Quantity |
Weight |
Alignment with LOs |
Final Exam |
1 |
30 |
|
Semester Evaluation Components |
Midterm |
1 |
25 |
|
Quizzes |
3 |
15 |
|
Term project and presentation |
1 |
15 |
|
Lab assignments |
7 |
15 |
|
*** ECTS Credit Calculation *** |
Activity |
Hours |
Weeks |
Student Workload Hours |
Activity |
Hours |
Weeks |
Student Workload Hours |
Lecture hours |
3 |
14 |
42 |
Assignments |
3 |
7 |
21 |
Active labs |
2 |
14 |
28 |
Home study |
1 |
14 |
14 |
In-term exam study |
10 |
1 |
10 |
Final exam study |
11 |
1 |
11 |
Term project/presentation |
2 |
12 |
24 |
|
|
|
|
|
|
|
|
Total Workload Hours = |
150 |
*T= Teaching, P= Practice |
ECTS Credit = |
6 |
Course Academic Quality Assurance: Semester Student Survey |
Last Update Date: 01/09/2023 |
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