DATA SCIENCE

AIM

This course introduces the basics of data science as the rapidly emerging most popular domain for researchers and practitioners in the 21st century. It highlights the basic skills to be acquired by a data scientist with various applications from medicine, homeland security, engineering, finance, etc. The objectives of the course are (1) introducing the concept of knowledge discovery in data and discuss the steps to be followed including the problem definition, data collection, integration and management, data analysis, and visualization. (2) highlighting the importance of dealing with various aspects of data, including volume, variety, velocity, veracity, value, etc., (3) introducing the basic statistical and machine learning techniques which could be effectively used for knowledge discovery, (4) covering network modeling and graph analysis as powerful alternative mechanisms for making sense from data, (5) illustrating how data visualize is effective for communication, and (6) covering basics of recommendation systems.

CONTENT

This course contains; Introduction to Data Science, probability, statistics, linear algebra,Basic data models, Entity-Relationship model, Relational Model and SQL.,From SQL to NoSQL, non-relational databases and related data models, XML Model and Xquery. ,NoSQL Databases, the case of Mongo DB. ,Sources and types of big data, frequent pattern analysis. ,Presentations by students research articles / tools. ,Presentations by students research articles / tools. ,Midterm overview,Clustering,Classification, Incremental data analysis and Scalable methods for Data management and analysis. ,Network model and graph analysis. ,Data visualization,Recommendation systems.

1. 1

   Understanding of basic network modeling and graph analysis techniques to handle data science tasks.

   Taught by: Project Based Learning Model, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

2. 2

   1. Understanding the basics of data science and the skill sets distinguishing a data scientist.

   Taught by: Project Based Learning Model, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

3. 3

   2. Understanding the basics of data collection, modeling and management for data science tasks.

   Taught by: Project Based Learning Model, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

4. 4

   3. Understanding of basic statistical modeling and analysis for data science tasks.

   Taught by: Project Based Learning Model, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

5. 5

   4. Understanding basic machine learning algorithms and techniques needed to cope with data science tasks.

   Taught by: Project Based Learning Model, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

6. 6

   4. Understanding basic machine learning algorithms and techniques needed to cope with data science tasks.

   Taught by: Project Based Learning Model, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

7. 7

   6. Understanding of basic approaches to visualize data for effective communication and understanding.

   Taught by: Project Based Learning Model, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task, Quiz

WEEKLY PLAN

1. WEEK 1

   Introduction to Data Science, probability, statistics, linear algebra

   Preparation: Lecture Notes, Week 1.

2. WEEK 2

   Basic data models, Entity-Relationship model, Relational Model and SQL.

   Preparation: Lecture Notes, Week 2.

3. WEEK 3

   From SQL to NoSQL, non-relational databases and related data models, XML Model and Xquery.

   Preparation: Lecture Notes, Week 3.

4. WEEK 4

   NoSQL Databases, the case of Mongo DB.

   Preparation: Lecture Notes, Week 4.

5. WEEK 5

   Sources and types of big data, frequent pattern analysis.

   Preparation: Lecture Notes, Week 5.

6. WEEK 6

   Presentations by students research articles / tools.

   Preparation: Literature survey.

7. WEEK 7

   Presentations by students research articles / tools.

   Preparation: Literature survey.

8. WEEK 8

   Midterm overview

   Preparation: All the topics till Week 8.

9. WEEK 9

   Clustering

   Preparation: Lecture Notes, Week 9.

10. WEEK 10

    Classification

    Preparation: Lecture Notes, Week 10.

11. WEEK 11

    Incremental data analysis and Scalable methods for Data management and analysis.

    Preparation: Lecture Notes, Week 11.

12. WEEK 12

    Network model and graph analysis.

    Preparation: Lecture Notes, Week 12.

13. WEEK 13

    Data visualization

    Preparation: Lecture Notes, Week 13.

14. WEEK 14

    Recommendation systems

    Preparation: Lecture Notes, Week 14

ASSESSMENT

• Rate of Midterm Exam to Success30%
• Rate of Final Exam to Success70%

WORKLOAD

READING

• No specific text book, notes will be made available, including in class notes, (sometimes) slides, research papers, book chapters, etc.

TEACHING STAFF

• Prof.Dr. Reda ALHAJJCOORDINATOR
• Prof.Dr. Reda ALHAJJ