Data Analyst | Data Scientist

• uOttawa: End-to-End Speech Translation of Contemporary Egyptian Arabic Dialect

• US Federal Campaign Finance Data 1990–2016 Analysis Project | In my previous role, I was responsible for designing, implementing, and enhancing a Spark application to address specific inquiries using a given dataset. These inquiries included analyzing the evolution of individual contributions for each course over time, examining the impact of presidential election years versus midterm election years on contributions, and investigating how the industry or ideology of the donor influences individual contributions. Through my work, I aimed to provide valuable insights and facilitate a deeper understanding of these topics.

• Data Modeling with Postgres | Analyzing the songs of Sparkify by applying data modelling with Postgres and building an ETL pipeline using Python.

• Project_Data_Modeling_with_Apache_Cassandra | Analyzing the songs of Sparkify by applying data modelling with Apache Cassandra and building an ETL pipeline that transfers data from a set of CSV files within a directory to create a streamlined CSV file to model and insert data into Apache Cassandra tables.

• A prediction of customer churn rate in the telecom industry and determination of the associations between items purchased.

• Build RDBMS using SQL Server and Data Warehousing using SQL Server and R Language in Market-Basket-Analysis (MBA)

• Project Overview: Introduction to NoSQL Databases | This project moves data from external sources into various databases, moves data from one type of database to another, and runs basic queries on various databases.

• ETL and Machine Learning - Project | In this Project, We imported data from an external dataset and create a DataFrame. We saved the data to a Parquet file and follow the steps to train the module using with Apache Spark.

• MapReduce versus Hadoop MapReduce

• Data Wrangling with DataFrames & Spark SQL

• Hospitals Analysis project | We used confirmed, recovered, and death datasets for all hospitals in all countries. By using the PowerBI tool, we explored useful statistical information to know the highest rate of confirmed, recovered, and dead countries in hospitals.
• Pharmaceutical Sales Analysis project| We analyzed a global pharmaceutical manufacturing company's raw sales data and gained meaningful insights. By using the PowerBI tool, we built a data model where dimensions and facts are separated by the provided data is in a single table format, and then they are linked together by logical relationships to form a star schema. So we extracted useful information by analyzing pharmaceutical sales, distributor and customer information, and sales team performance.
• COVID 2019 Analaysis (Top 10 Asian Countries) project| We analyzed the COVID 2019 dataset by using the Power BI tool, so we extracted the top 10 Asian countries for the highest cases and death rates, as well as the median age and total deaths by country.
• Health Care Analysis project| We analyzed the Health Care dataset by using the Power BI tool, so we extracted useful information such as Day Wise Schedule Appointment, Call Reason, Location Wise Cases Counts, Covid Test, and others.
• Medicare Inpatient 2017 Charges project| We analyzed the Medicare Inpatient 2017 dataset by using the Power BI tool, so we extracted useful information such as Average Total Payments and Average Medicare Payments by Provider Name, Average Total Payments by Provider State, and others.
• Adventure Works Cycles' analysis project | this project used Power BI to extract insights from Adventure Works CSV files, which can be found at this link: click here.
• Exploratory Data Analysis - Retail | Find out the weak areas where you can work to make more profit.
• Using MongoDB and Cassandra in analysis for both the movies' database and the Sales Manager database

• Network Intrusion Detection using Deep Learning | Detect anomalies using Deep learning Using tuning many different hyper parameters like (learning rate, hidden layer, activation function, batch size) and do grid search to select the best hyper parameters and do dimensionality reduction and feature selection then plot Tsne.
• Machine Learning Bagging and Boosting Models
• Machine Learning Clustering Models
• Machine Learning Classification Models
• Build One-vs-Rest and One-vs-One strategies Based-On the Most Important Features
• Machine learning approach to identify fake tasks
• Time Series Anomaly Detection
• A Fake Task Filtering Security Layer using CGAN (Conditional GAN) | Data generation using GANs, and use the discriminator as a classifier to detect suspicious and fake data.
• Detecting malicious Website Using its URL | We use machine learning and deep learning models such as random forest, extra tree, Adaboost, Gaussian Naive Bayes, and CNN-BiLSTM on the dataset, which contains 651,191 URLs.
• Predicting data exfiltration via DNS
• Prediction using Supervised ML | Predict the percentage of an student based on the no. of study hours.
• Prediction using Unsupervised ML | From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually.

• Gutenberg Book Classification (Classification-Is-All-You-Need) | Predict the author or the name of the book given some text, using classification and clustering techniques.
• Gutenberg Book Clustering (Clustering-Is-All-You-Need)
• ChatBot For Movie Recommendation system
• Manipulating the data ("Gutenberg's couple of digital books") and serializing them.
• Speech Recognition for Arabic Words | Predict Arabic words by wav files.

• Sports Products Multi-Class Classification | Using ResNet, VGG, and AlexNet Models to classify the different types of sports or their equipment.
• Glaucoma Detection Using Fudous Images And Convolution Neural Network
• Applying Machine Learning Models To Image Classification (the Cifar10 dataset)
• Applying Deep Learning Models to Image Classification (Caltech-UCSD Birds-200-2011)
• Applying Regularization and Data Augmentation on CNN Model to Image Classification (Leaf counting dataset)

• Atari Games using Q-Learning | Create brute-force function and Q learning function to get the average timestamps for going the target in some environments.

Learn SQL with simple, interactive exercises Click here to see the solutions

• Spaceship_Titanic | Predict which passengers are transported to an alternate dimension.
• ELG7186 - Assignment 2

• Hands-on Lab Monitoring in Action with Prometheus | In this lab, you will become familiar with using Prometheus to monitor sample servers simulated with node exporter.
• Hands-on Lab Monitoring in Action with Grafana | In this lab you will learn to use Grafana as a visualization tool and dashboard for Prometheus.

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