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An Enriching Experience

Being a professor at San Francisco State University is an enriching experience.

Beside discovering and participating at the “behind the scene” of University environment, I am also able to interact with a diverse group of scholars and students. Teaching at the undergraduate and graduate level in the Information Systems department allowed me to improve my understanding of this field as well as my pedagogical skills.


I teach the following classes in fields of database design, data analytics and machine learning

Application Development for Data Analytics

This course is an introduction to the use of Python programming for data analysis. It includes an overview of Python fundamentals, an exploration of Pandas libraries for data analysis and studies of statistical and graphical tools for data analysis and basis of model development.​

By the end of the semester, students should be able to:

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  1. Possess a basic understanding of Python programming language

  2. Describe the data analytics process

  3. Utilize Python libraries to visualize, manipulate and analyze data

  4. Apply statistical methods to develop data models

 

The topics covered in this class are :

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  • Fundamentals of Python programming language including:

    • Variables

    • List, Dictionary and Set

    • Condition Structures

    • Loops

    • Function

    • Libraries

  • Data Frame and Series

  • Statistical Plotting

  • Data Manipulation

  • Data Modeling

Managing Enterprise Data

The course is an introduction course to the design, use, and implementation of databases in organizations, and to the functions and use of database management systems (DBMS). It includes a study of data models and physical representation of databases; the conceptual and logical design of databases; and database definition, control, and manipulation using SQL.

By the end of the semester, students should be able to:

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  1. Explain the purpose of the database concept and identify its strength and risks

  2. Represent a database following the Entity-Relationship model and explain the components of this design

  3. Discuss concept of logical database and design relational databases using normalization

  4. Use SQL commands to define database structures and perform operations in databases

  5. Discuss basic concept of data warehousing and overall data management and analysis

 

The topics covered in this class are :

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  • Database modeling using E-R diagrams

  • Relational Model

  • Database development using SQL

  • Introduction to Query programming using SQL

  • Data Warehousing

  • Big Data management

  • Data and Database Administration

This graduate course studies the use of Python programming language for data analysis, data processing and development of predictive data models for business analytics. It includes the study of Pandas libraries for statistical and graphical data analysis.

Programming and Applications for Data Analytics

By the end of the semester, students should be able to:

​

  1. Possess a good understanding of Python programming language

  2. Describe the data analytics process

  3. Utilize Python libraries to visualize, manipulate and analyze data

  4. Apply statistical methods to develop data models


The topics covered in this class are :

​

  • Fundamentals of Python programming language including:

    • Variables

    • List, Dictionary and Set

    • Condition Structures

    • Loops

    • Function

    • Libraries

    • Files

  • NumPy Arrays

  • Data Frame and Series

  • Statistical Plotting

  • Advanced Data Manipulation

  • Advanced Data Modeling

Data Management for Analytics

This graduate course presents the fundamentals of database management in the context of business analytics processing with an introduction to the principles, design, security, and use of database management systems in business. It includes a study of data models and physical representation of databases; the conceptual and logical design of databases; and database definition, control, manipulation using SQL and big data management.

By the end of the semester, students should be able to:

​

  1. Explain the purpose of the database concept and identify its strength and risks

  2. Represent a database following the Entity-Relationship model and explain the components of this design

  3. Discuss concept of logical database and design relational databases using normalization

  4. Use SQL commands to define database structures and perform operations in databases

  5. Discuss basic concept of big data, data warehousing and overall data management and analysis

​

The topics covered in this class are :

​

  • Database modeling using E-R diagrams

  • Relational Model

  • Database development using SQL

  • Advanced Query programming using SQL

  • Data Warehousing

  • Big Data management

  • Data and Database Administration

AI and Machine Learning for Business Applications

This course is an in-depth, project-based exploration of AI and Machine Learning (ML), from fundamentals to advanced deep learning. ML has been instrumental in breakthroughs across industries, including autonomous vehicles, search engines, genomics, medical diagnosis, image recognition, and social network analysis. The course explains supervised, unsupervised, semi-supervised, and reinforcement learning techniques, applying them to real-world business challenges like price prediction, customer churn, risk assessment, and image classification. The course implements the best practices for data-driven decision-making and ethical considerations in AI/ML applications.

ML_course.png

By the end of the semester, students should be able to:

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  1. Explain core AI and machine learning concepts and their applications in business and organizations

  2. Use Python and libraries such as NumPy, Pandas, Scikit-Learn, and Keras to build and evaluate ML models

  3. Develop end-to-end machine learning pipelines from data preparation through model validation

  4. Apply unsupervised learning methods to identify patterns, reduce dimensions, and detect anomalies

  5. Evaluate model performance and communicate results clearly to business stakeholders

  6. Assess the ethical, legal, and societal implications of AI

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The topics covered in this class are :

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  • Exploratory data analysis

  • Linear Regression

  • Classification and Logistic Regression

  • Decision Tree and Ensemble Learning

  • Neural Network and Deep Learning

  • Clustering 

  • Autoencoders

  • Unsupervised Learning

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