مساقاتي الالكترونية على التعليم الالكتروني

This course is an introductory course to the Object Oriented Design. Topics covered include the C++ programming concepts, structures, functions, objects and classes, Class members: Data members (fields) and member functions (methods). Class member visibility (private, public, protected). Class hierarchies. Single and multiple inheritance. Inter-class relationships. Class variables and instance variables. Class methods and instance methods. Service methods and support methods. Scope. Constructors and destructors. Object initialization. Memory management. Garbage collection. Methods and messages. Method signatures. Method and operator overloading. Method overriding. Abstract classes. Dynamic (late) binding. Polymorphism. Software reuse. Subclasses (derived classes). Super classes (base classes). Invocation of super class methods and constructors.

This course provides a comprehensive introduction to the fundamental concepts and principles of computer networking. The students will explore the layered architecture of computer networks, beginning with the application layer and progressing through the transport, network, and link layers. Key topics include:

This course provides a comprehensive introduction to the fundamental concepts and principles of computer networking. The students will explore the layered architecture of computer networks, beginning with the application layer and progressing through the transport, network, and link layers. Key topics include:

• Chapter 1: Computer Networks and the Internet: An overview of network structures, protocols, and the Internet.
• Chapter 2: The Application Layer: Principles of application layer protocols such as HTTP, DNS, and email.
• Chapter 3: The Transport Layer: TCP and UDP protocols, reliable data transfer, and congestion control.
• Chapter 4: The Network Layer: Data Plane: IP addressing, routing algorithms, and packet forwarding.
• Chapter 5: The Network Layer: Control Plane: Routing protocols, SDN, and network management.
• Chapter 6: The Link Layer: Ethernet, wireless LANs, and local area network technologies.
• Chapter 7: Wireless and Mobile Networks: (Covered briefly) Wireless networking standards and mobile communication.
• Chapter 8: Network Security: (Covered briefly) Common network security threats and countermeasures.

This course provides a comprehensive introduction to the fundamental concepts and principles of computer networking. The students will explore the layered architecture of computer networks, beginning with the application layer and progressing through the transport, network, and link layers. Key topics include:

• Chapter 1: Computer Networks and the Internet: An overview of network structures, protocols, and the Internet.
• Chapter 2: The Application Layer: Principles of application layer protocols such as HTTP, DNS, and email.
• Chapter 3: The Transport Layer: TCP and UDP protocols, reliable data transfer, and congestion control.
• Chapter 4: The Network Layer: Data Plane: IP addressing, routing algorithms, and packet forwarding.
• Chapter 5: The Network Layer: Control Plane: Routing protocols, SDN, and network management.
• Chapter 6: The Link Layer: Ethernet, wireless LANs, and local area network technologies.
• Chapter 7: Wireless and Mobile Networks: (Covered briefly) Wireless networking standards and mobile communication.
• Chapter 8: Network Security: (Covered briefly) Common network security threats and countermeasures.

This course provides a clear understanding of wireless networks’ concepts, architecture, protocols, and applications. It describes various security architectures for different wireless communication networks to design secure wireless networks. It will also discuss 5G and Internet of Things. Finally, the course will discuss all different types of wireless networks: wireless LAN, wireless MAN, wireless PAN, and wireless WAN from design, use, and security point of view.

Course Description:

This course, Data Analytics, provides a comprehensive exploration of data analysis techniques and tools, equipping students with the skills to extract meaningful insights from diverse datasets. Students will learn to apply statistical methods, data visualization, and machine learning algorithms using industry-standard tools like Excel, Tableau, Power BI, and Python libraries. Through hands-on exercises and a capstone project, students will develop the ability to analyze data, build predictive models, and communicate findings effectively, preparing them for careers in data science and related fields.

Course Objectives:

Upon successful completion of this course, students will be able to:

1. Understand the fundamental concepts of data analytics and the data science lifecycle.
2. Apply statistical methods for data analysis, including correlation, regression, and hypothesis testing.
3. Utilize Excel for data manipulation, analysis, and visualization.
4. Create interactive dashboards and visualizations using Tableau and Power BI.
5. Program in Python for data analysis, manipulation, and visualization using NumPy, Pandas, Matplotlib, and Seaborn.
6. Implement machine learning algorithms using Scikit-Learn.
7. Build and train neural networks using TensorFlow and Keras.

Course Schedule:

Week 1:

• Introduction to Data Analytics: Overview, Importance, Types, Data Science Lifecycle.
• Lab: Setting up Excel and Jupyter Notebook environments.

Week 2:

• Descriptive and Diagnostic Analytics.
• Introduction to Statistical Analysis: Counting, Probability, and Probability Distributions.
• Lab: Excel data cleaning and formatting.

Week 3:

• Predictive and Prescriptive Analytics.
• Sampling Distributions, Estimation, and Hypothesis Testing.
• Lab: Excel Data Validation and Conditional Formatting.

Week 4:

• Correlation and Regression, Scatter Diagrams, Imputation Techniques.
• Data Analytics using Excel: Text to Columns, Concatenate.
• Lab: Excel Pivot Tables.

Week 5:

• Excel: Time and Date Calculations, IF Function, Pivot Tables and Charts.
• Lab: Excel Charting and Slicers.

Week 6:

• Introduction to Tableau: Connecting to Data Sources, Basic Reports.
• Lab: Tableau Basic reports creation.
• Week 7: Midterm Exam

Week 8:

• Tableau: Parameters, Grouping, Sets, Data Labels, Sorting.
• Lab: Tableau Charts.

Week 9:

• Tableau: Various Chart Types (Area, Bar, Box, etc.).
• Lab: Power BI Introduction.

Week 10:

• Introduction to Power BI: Concepts, Data Sources, Report Creation.
• Lab: Power BI Visualizations.

Week 11:

• Power BI: Visualizations, Tiles, DAX Functions.
• Lab: Python Data Structures.

Week 12:

• Python Libraries for Data Analytics: NumPy, Pandas, Data Cleaning.
• Lab: Python Data Visualization.

Week 13:

• Python Data Visualization: Matplotlib, Seaborn.
• Machine Learning with Python: Introduction, Maths for ML.
• Lab: Python Machine Learning implementation.

Week 14:

• Machine Learning Algorithms: Classification and Regression.
• Deep Learning with Python: Keras and TensorFlow.
• Lab: Python Deep Learning implementation.

Week 15:

• Big Data and Distributed Computing: PySpark.
• Capstone Project: Proposal and Development.
• Lab: PySpark implementation.

Week 16:

• Capstone Project: Final Presentations.
• Final Exam.

Assessment:

• Quizzes and Assignments: 30%
• Midterm Exam: 30%
• Final Project Presentation: 40%

Recommended Textbooks and Resources:

• Python Data Science Handbook by Jake VanderPlas
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
• Online Courses: Jerash Course Platform, DataCamp, Coursera,
• Software Tools: Tableau, Power BI, Excel, Python (NumPy, Pandas, Matplotlib, Scikit-Learn), Jupyter Notebook, and other essential Python libraries

  lecture 12-3-2024

The main objective of this course is to introduce students to the basic concepts of a selected language (such as C++) and the ability to write simple correct programs. Topics to be covered include: I/O, data types, function definition, visibility and storage classes, parameter passing, loops, arrays, pointers, strings, files, enumerated type, introducing classes and objects, constructors and destructors, function prototypes, private and public access, and class implementation. The practical part of this course is covered in the lab through exercises, practical assignments, and tutorials.