1
الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Statistics for Data Science Course Code: DS 611
Prerequisites: None Course Teaching Language: English
Course Level : 1 Credit Hours: ( 3 , 0 , 0 )
Course Description
This course covers the following topics: Fundamentals of probability theory and
statistical inference used in data science; Probabilistic models, random variables, useful
distributions, expectations, law of large numbers, central limit theorem; Statistical
inference; point and confidence interval estimation, hypothesis tests, linear regression.
The course will also focus on different types of quantitative research methods and
statistical techniques for analyzing data. Then, we will explore a range of statistical
techniques and methods using the open-source statistics language, R. The course will
also cover topics in quantitative techniques that include: descriptive and inferential
statistics, sampling, experimental design, parametric and non-parametric tests of
difference, ordinary least squares regression, and logistic regression. Recent correlated
software packages should be used through labs.
Course Objectives
The student should be able to:
1. Demonstrate understanding of the value of statistics and testing problems.
2. Demonstrate understanding of quantitative research methods and statistical
inferences for analyzing data.
3. Use statistical algorithms for solving binomial and exponential methods.
4. Apply statistical programming tools to solve real-world problems.
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Demonstrate understanding of multiple testing problem,
probabilistic models, random variables, and useful distributions. Knowledge
Analyze data using different types of quantitative research
methods and statistical inferences. Cognitive Skills
Demonstrate knowledge of and ability to analyze statistical data
in a professional standards and professional behavior
Interpersonal Skills &
Responsibility
Communicate technical knowledge, including ideas, data analysis,
findings, or decision justification in written formats in a manner
appropriate to the audience.
Communication,
Information
Technology, Numerical
Course Content
List of topics Number of weeks
Teaching / contact hours
Fundamentals of probability theory and statistical inference used in data science
1 3
Probabilistic models 1 3 Random variables and useful distributions 1 3 Expectations and law of large numbers 1 3 Central limit theorem 1 3 Statistical inference; point and confidence interval estimation
2 6
Hypothesis tests, linear regression 2 6 Statistical techniques and methods using the open-source statistics language, R language
2 6
Descriptive and inferential statistics 1 3 Parametric and non-parametric tests of difference 1 3 Ordinary least squares regression 1 3 Logistic regression 1 3
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Supportive Books & References
Book Title Author Publisher Publication
Year Practical Statistics for Data Scientists: 50 Essential Concepts ISBN-13: 978-1491952962
Peter Bruce
and Andrew Bruce O'Reilly Media 2017
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data ISBN-13: 978-1491910399
Hadley Wickham and Garrett Grolemund O'Reilly Media 2017
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Programming for Data
Science Course Code: DS 612
Prerequisites: None Course Teaching Language: English
Course Level : 1 Credit Hours: 3
Course Description
This course presents the programming for data science, using the Python
programming language. The course covers the data science process, from
collecting data, pre-processing it (cleaning/correcting it), performing
exploratory data analyses, visualizing data, and sharing analysis results.
Course Objectives:
1. To learn the different aspects of programming for data science.
2. To gain an appreciation for the end-to-end process of obtaining
data, processing it, through to presenting results.
3. By the end of the course to be able to build own data simple data
processing pipeline.
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Knowledge
Demonstrate an understanding of the process for
extracting knowledge from data.
Demonstrate an understanding of the broad range of
methods, which are based on statistics and computer
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
and used in data science.
Cognitive Skills Apply the statistical and computational techniques in
data management, analysis and problem solving.
Communication,
Information
Technology, Numerical
Evaluate the current tools and techniques used for
data science.
Course Content
List of topics Number of weeks Teaching / contact hours
Introduction to Data
Science 1 3
Python primer 2 6
Playing with Pandas 2 6
Data visualization 2 6
Big Data 2 6
Getting data from public
sources 2 6
Machine Learning 2 6
Sharing analyses 2 6
Course Supportive Books & References
Book Title Author Publisher Publication Year Doing Data Science: Straight Talk from the Frontline
Cathy O'Neil, Rachel Schutt
O'Reilly Media 2013
Python 3 Object-Oriented Programming
Dusty Phillips Packt
Publishing 2018
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Data Visualization Course Code: DS 613
Prerequisites: None Course Teaching Language: English
Course Level : 1 Credit Hours: ( 3 , 0 , 0 )
Course Description
Data Visualization enhances exploratory analysis as well as efficient communication of
data results. This course focuses on the design of visual representations of data in
order to discover patterns, answer questions, convey findings, drive decisions, and
provide persuasive evidence. The goal is to give you the practical knowledge you need
to create effective tools for both exploring and explaining your data. Exercises
throughout the course provide a hands-on experience using relevant programming
libraries and software tools to apply research and design concepts learned. Recent
correlated software packages should be used through labs.
Course Objectives
The student should be able to:
1. Demonstrate understanding of the value of visualized data.
2. Demonstrate understanding of analysis and efficient communication of data
results.
3. Use visual representations of data to discover patterns and answer questions.
4. Apply practical knowledge to create effective tools for both exploring and
explaining data.
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Course Content
List of topics Number of weeks Teaching / contact
hours
A language – learning bridge between Python and Java Script
1 3
Reading and writing data with Python 2 6 Webdev 101 Tool 1 3 Getting data off the web with Python 2 6 Heavyweight Scraping with Scrapy 2 6 Introduction to NumPy 1 3 Introduction to Pandas 1 3 Cleaning data with Pandas 1 3 Visualizing data with Matplotlib 2 6 Exploring data with Pandas 1 3
Delivering the Data 1 3
Demonstrate understanding of visualized data values
and design of visual representations. Knowledge
Analyze data to discover patterns and derive decisions
to support data – driven decision making. Cognitive Skills
Demonstrate knowledge to explore and explain data in
a professional standards and professional behavior.
Interpersonal Skills &
Responsibility
Communicate technical knowledge, including ideas,
data analysis, findings, or decision justification in
written formats in a manner appropriate to the
audience.
Communication,
Information Technology,
Numerical
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Supportive Books & References
Book Title Author Publisher Publication
Year
Data Visualization with Python
and JavaScript: Scrape, Clean,
Explore & Transform Your Data
1st Edition
ISBN-13: 978-1491920510
Kyran Dale O'Reilly Media 2016
Data Visualization: A Practical
Introduction 1st Edition
ISBN-13: 978-0691181622
Kieran Healy
Princeton
University
Press
2018
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Applied Machine Learning Course Code: DS 614
Prerequisites: DS 612 Course Teaching Language: English
Course Level : 2 Credit Hours: ( 3 , 0 , 0 )
Course Description
Machine learning is a type of artificial intelligence (AI) that provides computers with
the ability to learn without being explicitly programmed. This area is also concerned
with issues both theoretical and practical. This course provides a broad and rigorous
introduction to machine learning. In this course, we will present algorithms and
approaches in such a way that grounds them in larger systems as you learn about a
variety of topics, including:
supervised learning
unsupervised learning
Reinforcement learning
This course covers bias-variance trade-off; training versus test error; overfitting; cross-validation; subset selection methods; regularized approaches for linear regression, non-parametric regression: trees, bagging, random forests; generalized additive models; support vector machines; k-means and hierarchical clustering; reinforcement learning, and principal components analysis for dimensionality reduction.
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Objectives
The student should be able to:
1. Provide a broad survey of approaches and techniques in machine learning
2. Develop a deeper understanding of several major topics in machine learning
3. Develop the design and programming skills that will help you to build intelligent,
adaptive artifacts
4. Develop the basic skills necessary to pursue research in machine learning.
5. Apply machine learning tools to solve real-world problems.
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Knowledge Demonstrate understanding of classical machine
learning algorithm.
Cognitive Skills Apply the statistical and computational techniques in
data science to capture key patterns.
Interpersonal Skills &
Responsibility
Show an ability to practice in a professional standards
and professional behavior.
Demonstrate ability to decide what techniques are
appropriate for a given question, and to make trade-offs
between model complexity in a professional standards
and professional behavior.
Communication,
Information Technology,
Numerical
Communicate technical knowledge, including ideas,
data analysis, findings, or decision justification in
written formats in a manner appropriate to the
audience.
Evaluate the current tools and techniques used for data
science.
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Content
List of topics Number of
weeks
Teaching /
contact hours
Pattern Recognition Introduction Pattern recognition definition Types of Learning Supervised learning unsupervised learning Complete ML Example
1 3
Python for Machine Learning : Scikit-learn for working with classical ML
algorithms Pandas for data extraction and preparation Matplotlib for data visualization
1 3
K-Nearest Neighbour Classifier Describe a data set as points in a high dimensional
space. Compute distances between points in high
dimensional space. Implement a K-nearest neighbor model of
learning. Draw decision boundaries. First “machine learning” algorithm K-nearest neighbor for classification & Regression Handwriting recognition sample
1 3
Neural Network Decision Stump Single-layer neural networks How Neuron work Perceptron Learning Rule Gradient decent/Backpropagation Multi-layer neural networks.
2 6
Decision Trees What is a Decision Tree Sample Decision Trees How to Construct a Decision Tree
1 3
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الجامعة وكالة
العلمي والبحث العليا للدراسات
ID3 algorithm Problems with Decision Trees Naïve Bayes Classifier Probability Basics Probabilistic Classification Naïve Bayes Principle and Algorithms Example: Play Tennis
1 3
Margin-Based Classification Support Vector Machines
1 3
Unsupervised learning - Clustering & K-Mean Unsupervised learning Clustering & Types of clustering K-Means clustering Simple K-Means example Weaknesses of K-Mean Clustering Applications of K-Mean Hierarchical clustering Agglomerative Clustering Common Distance measures inter-Cluster Similarity
2 6
Ensemble, Bagging, Boosting, Stacking Ensemble, Bagging, Random Forest Boosting, AdaBoost Stacking
1 3
Features Engineering Feature selection Features generation Dimensionality reduction
1 3
Dataset management Dataset splitting Cross validation Validation & Verification Underfitting and Overfitting
1 3
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الجامعة وكالة
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Time Series recognition Hidden Markov Model
1 3
Reinforcement Learning MDP / Value and Policy Iteration Reinforcement Learning
1 3
Course Supportive Books & References
Book Title Author Publisher Publication
Year Pattern Recognition and Machine Learning (Information Science and Statistics),:
Christopher M. Bishop Springer
2016
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Kevin P. Murphy Francis Bach 2012
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Big Data Analytics Course Code: DS 615
Prerequisites: Statistics for Data
Sciences (DS 611) Course Teaching Language: English
Course Level : 2 Credit Hours: 3
Course Description
Big Data requires the storage, organization, and processing of data at a scale and
efficiency that go well beyond the capabilities of conventional information
technologies. In this course, we will study the state of the art in big data
management: we will learn about algorithms, techniques and tools needed to
support big data processing. In addition, we will examine real applications that
require massive data analysis and how they can be implemented on Big Data
platforms.
Course Objectives
1. Treat the Big Data storage, processing, analysis, visualization, and
application issues.
2. Get insight on what tools, algorithms, and platforms to use on
which types of real world use cases.
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Knowledge Demonstrate an understanding of the broad range of methods, which are based on statistics and computer and used in data science.
Cognitive Skills Apply the statistical and computational techniques in data management, analysis and problem solving.
Interpersonal Skills &
Responsibility Show an ability to practice in a professional standards and professional behavior.
Communication,
Information
Technology, Numerical
Evaluate the current tools and techniques used for data science.
Course Content
List of topics Number of weeks Teaching /
contact hours
Background - Course Overview; The evolution of Data Management and introduction to Big Data - Introduction to Databases, Relational Model and SQL
2 6
Big Data Foundations and Infrastructure (3 weeks) - Introduction to Map Reduce - Algorithm Design for MapReduce: Relational Operations - MapReduce Algorithm Design Patterns; Parallel Databases vs MapReduce
4 12
Transparency and Reproducibility - Data Exploration and Reproducibility
2 6
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Big Data Algorithms, Mining Techniques, and Visualization (6 weeks) - Finding similar items - Association Rules - Visualization and Spatio-Temporal Data - Parallel Databases - Graph Analysis
7 21
Course Supportive Books & References
Book Title Author Publisher Publication Year Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
Cambridge University Press
2014
Data-Intensive Text Processing with MapReduce
Jimmy Lin, Chris Dyer
Morgan and Claypool Publishers
2010
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Statistical Methods for
Discrete Response and Time Series
Course Code: DS 621
Prerequisites: None Course Teaching Language: English
Course Level : ----- Credit Hours: ( 3 , 0 , 1 )
Course Description
Classical linear regression and time series models are workhorses of modern statistics,
with applications in nearly all areas of data science. This course takes a more advanced
look at both classical linear and linear regression models, including techniques for
studying causality, and introduces the fundamental techniques of time series
modeling. Mathematical formulation of statistical models, assumptions underlying
these models, the consequence when one or more of these assumptions are violated,
and the potential remedies when assumptions are violated are emphasized
throughout. Major topics include classical linear regression modeling, casual inference,
identification strategies, and a class of time series models that are popular among
industry professionals. The course emphasizes formulating, choosing, applying, and
implementing statistical techniques to capture key patterns exhibited in data. All of the
techniques introduced in this course come with real-world examples and R code that is
explained in weekly sessions. Students who successfully complete this course will be
able to decide what techniques are appropriate for a given question, and to make
trade-offs between model complexity, ease of interpreting results, and timing
implementation in real-world applications. As concepts in probability theory and
mathematical statistics are used extensively; students should feel comfortable with the
definition, manipulation, and application of these concepts in mathematical notations.
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Recent correlated software packages should be used through labs.
Course Objectives
The student should be able to:
1. Demonstrate understanding of classical linear regression and time series models.
2. Demonstrate understanding of causality techniques and mathematical
formulation of statistical models.
3. Use statistical techniques for formulating, choosing, applying, and implementing
data.
4. Apply statistical programming tools to solve real-world problems.
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Demonstrate understanding of classical linear regression and
time series models. Knowledge
Analyze large data to study causality and introduce fundamental
techniques of time series modeling.
Apply the statistical and computational techniques in data
science to capture key patterns.
Cognitive Skills
Demonstrate knowledge of and ability to decide what techniques
are appropriate for a given question, and to make trade-offs
between model complexity in a professional standards and
professional behavior.
Interpersonal Skills &
Responsibility
Communicate technical knowledge, including ideas, data
analysis, findings, or decision justification in written formats in a
manner appropriate to the audience.
Communication,
Information
Technology, Numerical
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Content
List of topics Number of weeks Teaching / contact
hours
Basic Regression Models 1 3 Discrete hazard functions and parametric regression models
1 3
Discrete and continuous hazards 1 3 Characteristics of Time Series: Nature of time series, time series statistical models
1 3
Measures of Dependence 1 3 Stationary Time Series 1 3 Estimation of Correlation 1 3 Classical Regression in the Time series context
1 3
Exploratory data analysis 1 3 Smoothing in the time series context 1 3 Autoregressive moving average models 1 3 Difference equations 1 3 Forecasting 1 3 Estimation 1 3 Cyclical behavior and periodicity 1 3
Course Supportive Books & References
Book Title Author Publisher Publication
Year Time Series Analysis and Its Applications: With R Examples ISBN-13: 978-3319524511
Robert H. Shumway David S. Stoffer
Springer
2017
Modeling Discrete Time-to-Event Data ASIN: B01H3AIL2I
Gerhard Tutz Matthias Schmid
Springer
2016
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Data systems Course Code: DS 622
Prerequisites: Course Teaching Language: English Course Level : Credit Hours: 3
Course Description
This course will be a comprehensive introduction to modern data systems. The
primary focus of the course will be on modern trends that are shaping the data
management industry right now such as column-store and hybrid systems,
shared nothing architectures, cache conscious algorithms, hardware/software
co-design, main memory systems, adaptive indexing, stream processing,
scientific data management, and key-value stores. We will also study the history
of data systems, traditional and seminal concepts and ideas such as the relational
model, row-store database systems, optimization, indexing, concurrency control,
recovery and SQL; In this way, we will discuss both how data systems evolved
over the years and why, as well as how these concepts apply today and how data
systems might evolve in the future.
Course Objectives
1. Learn state-of-the-art research and industry trends in big data systems. 2. Understand the tradeoffs in designing and implementing modern big data
systems. 3. Be able to make design decisions in big data driven scenarios.
This course covers main NoSQL data management systems topics such as key-
value stores, graph databases, and document databases.
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Knowledge
Demonstrate an understanding used to create, manipulate
and optimize database as well as of splitting data across
machines via sharding.
Cognitive Skills
Apply methods to analyze large data sets using
aggregation techniques to support data-driven decision
making.
Develop software and tools to create large scale database
application that are effective for analysis.
Interpersonal Skills &
Responsibility
Show an ability to work effectively as an individual and as
a member of a team to accomplish a goal.
Communication,
Information
Technology, Numerical
Evaluate the current tools and techniques used for data systems.
Course Content
Topic Number of
Weeks Contact Hours
row-store database systems, optimization, indexing, concurrency control, recovery
2 6
Introduction to NOSQL database systems 1 3 NOSQL Creating , Inserting, Updating and deleting Documents (Chodorow Chapter 3)
2 6
NOSQL Querying (Chodorow Chapter 4) 2 6 NOSQL Indexing (Chodorow Chapter 5) 1 3 NOSQL Aggregation (Chodorow Chapter 7) 1 3 Sharding ( Chodorow chapter 13,14,15 ) 2 6 Manipulating Graph database 2 6 Key- value database 2 6
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الجامعة وكالة
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Course Supportive Books & References
Book Title Author Publisher Publication
Year
Fundamentals of Data
base Systems
Elmasri &
Navathe Pearson 5102
NoSQL for Mere Mortals Dan Sullivan Pearson
Education 2015
MongoDB: The
Definitive Guide Chodorow, K.
Pearson
Education 5102
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Deep Learning Course Code: DS 623
Prerequisites: DS 614 Course Teaching Language: English
Course Level : Postgraduate Credit Hours: ( 3 , 0 , 0 )
Course Description
This is an advanced course on machine learning, focusing on recent advances in deep
learning with neural networks, such as CNN, RNN, Deep RL networks. The course will
concentrate cover computer vision and natural language processing (NLP)
applications. Recent statistical techniques based on neural networks have achieved a
remarkable progress in these fields, leading to a great deal of commercial and
academic interest. The course will introduce the mathematical definitions of the
relevant machine learning models and derive their associated optimization
algorithms. It will cover a range of applications of neural networks in natural language
processing, including analyzing latent dimensions in text, translating between
languages, and answering questions.
Course Objectives
The student should be able to:
1. Understand the definition of a range of advanced machine learning models.
2. Understand advanced machine learning implementations mechanisms and
sequence embedding models and how these modular components can be
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
combined to build state-of-the-art systems.
3. Have an understanding of how to choose a model to describe a particular type of
data.
4. Know how to evaluate a learned model in practice.
5. Understand the mathematics necessary for constructing novel machine learning
solutions.
6. Be able to design and implement various machine learning algorithms in a range
of real-world applications.
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Knowledge
Demonstrate an understanding of the broad range of advanced machine learning algorithm, which are based on statistics and computer and used in data science.
Cognitive Skills
Apply the deep learning models and computational
techniques in data science to capture key patterns in
different real world application.
Interpersonal Skills &
Responsibility
Demonstrate ability to decide what techniques are
appropriate for a given question, and to make trade-
offs between model complexity in a professional
standards and professional behavior.
Communication,
Information Technology,
Numerical
Communicate technical knowledge, including ideas,
data analysis, findings, or decision justification in
written formats in a manner appropriate to the
audience.
Evaluate the current tools and techniques used for
data science.
25
الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Content
List of topics Number of weeks Teaching / contact
hours
Introduction to Deep learning - Deep Learning Models
1 3
Deep Learning development tool: Introduction to Theano, TensorFlow, and
Keras libraries. Introduction Machine Learning With
Weka Seaborn for data visualization library
2 6
Deep Learning for Computer vision Convolutional Neural Networks Improve Model Performance With Image
Augmentation. Object detection Object tracking and action recognition Famous computer vision deep learning
architecture such as AlexNet Reduce Overfitting With Dropout
Regularization Transfer Learning
3 9
Natural Language Processing Intro and text classification
2 3
Time Series Prediction Time series problem RNN Networks LSTM Networks, GRU Networks
2 6
NLP & Deep Learning Vector Space Models of Semantics Word-2-Vec Sequance-2-Sequance Sequence to sequence tasks Dialog systems
2 6
Deep Reinforcement Learning 1 3
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الجامعة وكالة
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Generative adversarial network 1 3 State of the art XGBoost 1 3
Course Supportive Books & References
Book Title Author Publisher Publication
Year
Neural Networks and Deep
Learning: A Textbook Charu C. Aggarwal 2018
Keras 2.x Projects: 9 projects
demonstrating faster
experimentation of neural network
and deep learning applications
using Keras
Giuseppe Ciaburro 2018
Advanced Deep Learning with
Keras: Apply deep learning
techniques, autoencoders, GANs,
variational autoencoders, deep
reinforcement learning, policy
gradients, and more
Rowel Atienza 2018
27
الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Generalized Linear Models Course Code: DS624
Prerequisites: None Course Teaching Language: English
Course Level : ----- Credit Hours: ( 3 , 0 , 0 )
Course Description
The course focus on analyzing linear and non-linear effects of continuous and
categorical predictor variables on a discrete or continuous dependent variable using
Generalized linear models. The structural form of the model describes the patterns of
interactions and associations. The model parameters provide measures of strength of
associations. In models, the focus is on estimating the model parameters. The basic
inference tools (e.g., point estimation, hypothesis testing, and confidence intervals)
will be applied to these parameters.
Course Objectives
Upon successful completion of the course students should:
1. Understand statistical concepts for building generalized linear models.
2. Be able to apply the concepts of mathematics and statistics in estimating the model
parameters.
3. Use multiple regression, analysis of variance and analysis of covariance for
quantitative responses.
4. Use logistic regression and probit models for binary data
5. Apply statistical programming tools to solve real-world problems.
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Demonstrate understanding of simple and multiple
regression as well as generalized linear models. Knowledge
Apply the most common generalized linear models in
statistical data analysis of the all areas of application. Cognitive Skills
Demonstrate knowledge of and ability to determine
which model is appropriate for a given application area.
Interpersonal Skills &
Responsibility
have the ability to present and discuss, orally and in
writing, the results of studies based on generalized linear
models
Communication,
Information Technology,
Numerical
Course Content
List of topics Number of weeks Teaching /
contact hours
Introduction Simple Regression Model
2 6
Bivariate Regression 1 3
On way ANOVA 1 3
ANOVA and the Bivariate Regression Approach
1 3
Multiple Regression Model 1 3
Multiple Regression Model when predictors interact
1 3
Two way ANOVA 1 3
Logistic regression models 1 3
Analysis of Covariance: Continuous and Categorical Predictors
1 3
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الجامعة وكالة
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Repeated Measures 1 3
Multiple Repeated Measures 1 3
Mixed Between and within Designs 1 3
Poison Regression 1 3
Log-Linear Models 1 3
Course Supportive Books & References
Book Title Author Publisher Publication
Year An Introduction to Generalized Linear Models, ISBN: 9781351726221.
Annette J. Dobson, Adrian G. Barnett
CRC Press
April 2018
Regression, ANOVA, and the General Linear Model, ISBN: 9781483310336 Peter W. Vik
SAGE Publications,
Inc
2013
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Artificial Intelligence Course Code: DS 625
Prerequisites: None Course Teaching Language: English
Course Level : ----- Credit Hours: ( 3 , 0 , 1 )
Course Description
Artificial Intelligence (AI) is an exciting field that has enabled a wide range of cutting-
edge technology, from driverless cars to grandmaster-beating Go programs. The goal
of this course is to introduce the ideas and techniques underlying the design of
intelligent computer systems. Topics covered in this course are broadly being divided
into 1) planning and search algorithms, 2) probabilistic reasoning and representations,
and 3) machine learning. Within each area, the course will also present practical AI
algorithms being used in the wild and, in some cases, explore the relationship to state-
of-the-art techniques in robotics, computer vision, and related areas. Recent
Correlated software packages should be used through labs.
Course Objectives
The student should be able to:
1. Demonstrate understanding of major artificial intelligence techniques.
2. Demonstrate understanding of planning and searching algorithms.
3. Use a set of probabilistic reasoning and representation algorithms.
4. Apply technical knowledge and techniques for problem solving and reliability
of results.
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Demonstrate understanding of understanding of major artificial
intelligence techniques. Knowledge
Analyze large data sets for planning and searching algorithms. Cognitive Skills
Demonstrate knowledge to apply Artificial Intelligence
algorithms in a professional standard.
Interpersonal Skills
& Responsibility
Communicate technical knowledge, including ideas, data
analysis, findings, or decision justification in written formats in
a manner appropriate to the audience.
Communication,
Information
Technology,
Numerical
Course Content
List of topics Number of weeks Teaching / contact
hours Intelligent Agents 1 3
Solving problems by searching 2 6
Beyond classical search 2 6
Advanced search 1 3
Logical agents 1 3
Constraint satisfaction problems 1 3
First order logic 1 3
Inference in First order logic 1 3
Classical planning 1 3
Knowledge representation 1 3
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Quantifying uncertainty 1 3
Natural language processing 2 6
Course Supportive Books & References
Book Title Author Publisher Publication
Year
Artificial Intelligence: A modern
approach 3rd Edition
ISBN-13: 978-9332543515
Stuart Russell Pearson
Education India 2015
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Course Name: Data Engineering Course Code: DS 626
Prerequisites: None Course Teaching Language: English
Course Level : ----- Credit Hours: ( 3 , 0 , 0)
Course Description
The course will focus on the analysis of messy, real life data to perform predictions
using statistical and machine learning methods. Material covered will integrate the five
key facets of an investigation using data: (1) data collection - data wrangling, cleaning,
and sampling to get a suitable data set; (2) data management - accessing data quickly
and reliably; (3) exploratory data analysis – generating hypotheses and building
intuition; (4) prediction or statistical learning; and (5) communication – summarizing
results through visualization, stories, and interpretable summaries.
Course Objectives
The student should be able to:
1. Demonstrate understanding of storing, managing and processing of datasets.
2. Demonstrate understanding of analyzing large datasets for data storage,
retrieval, and processing systems.
3. Use a set of building blocks to construct a complete architecture for storing
and processing data.
Summary of Course Description
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الجامعة وكالة
العلمي والبحث العليا للدراسات
4. Apply technical knowledge and architectures for problem solving and
reliability of results.
Learning outcomes: (comprehension, knowledge, intellectual & scientific skills) By completion of this course students are expected to be able to:
Demonstrate understanding of data collection - data
wrangling, cleaning, and sampling.
Knowledge
Analyze large data sets to support decision-making processes. Cognitive Skills
Demonstrate knowledge to manage data quickly and reliably
in a professional standard.
Interpersonal
Skills &
Responsibility
Communicate technical knowledge, including ideas, data
analysis, findings, or decision justification in written formats
in a manner appropriate to the audience.
Communication,
Information
Technology,
Numerical
Course Content
List of topics Number of weeks Teaching /
contact hours
Statistical Learning and Regression 1 3
Curse of Dimensionality and Parametric Models and Assessing Model Accuracy and Bias-Variance Trade-off
1 3
Classification Problems and K-Nearest Neighbors 1 3
Introduction to R 1 3
Linear Regression 1 3
Classification 2 6
Resampling Methods 1 3
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الجامعة وكالة
العلمي والبحث العليا للدراسات
Linear Model Selection and Regularization 2 6
Moving Beyond Linearity 1 3
Tree-Based Methods 1 3
Support Vector Machines 2 6
Unsupervised Learning 1 3
Course Supportive Books & References
Book Title Author Publisher Publication
Year An Introduction to Statistical Learning: with Applications in R 1st Edition ISBN-13: 978-1461471370
Gareth James, Daniela Witten, Trevor Hastie,
Robert Tibshirani
Springer 2016