Ahmed Ewis

• Utilized advanced querying, visualization and analytics tools to analyze and process complex data sets.
• Worked with stakeholders to develop quarterly roadmaps based on impact, effort and test coordinations.
• Identified, measured and recommended improvement strategies for KPIs across business areas.
• Set up SQL database on cloud servers to store client data for query analysis.
• Managing Customer data & Customer Analytics:
  Collect, analyze, and store massive amounts of data. We are using Machine Learning and Data Science tools to transform Big Data into a possibility to learn more about our clients to drive new revenue opportunities.
• Predictive Modeling:
  Through the power of Big Data, we are leveraging new technology for effective predictive risk modeling and better data-driven decisions the deploy the ideas to production.
  Team Regulation:
  1. Building statistical analysis & financial modeling techniques.
  2. Analyzing behavioral data & identifying growth opportunities.
  3. Using analytics & statistics to measure data quality product performance and anticipate/interpret customer behavior.
  4. Developing dashboards to visualize findings for stakeholders.
  

• Developing a platform & mobile app integration using AI technologies. The endeavor is a unique tokens (NFT's) image/video generation using GANs (Generative Adversarial Networks) integrated with Pytorch and combined with different Deep learning techniques. The work was deemed continual research based on heavy and advanced Computer vision frameworks & Natural Language Processing.
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  ‍Tasks and targets:
  ‍1. Organizing Ginormous Database Structure.
  2. Studying the movement pattern.
  3. Advanced level of search for customers.
  4. Speech and Facial recognition.
  5. Speaker identification.
  6. Video processing.
  7. Training Multi-model of text plus images.
  8. Developing codes for automation and scalability.
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• Maintained all technology documentation, providing updates and addendums when required.
• Developed new compounds and formulas based on strict customer requirements.
• Evaluated project proposals for potential environmental impact, reporting findings to both employer and appropriate regulatory agencies.
• Developed processes based on critical needs, preset efficiency standards and cost reduction measures.
• Visited customer sites to supervise new product trials and troubleshoot when issues arose.

• Helped students to recognize learning issues evidenced in one-to-one support instruction.
• Data Cleaning and data mining techniques to assist the research students.
• Developing Models with different program language (R, Python) to ensure the outcome efficiency of the research.

• Solid comprehension of data science and its ginormous theory.
• Learned several softwares, tools and techniques in Data Science.
• Training on various widely used packages in data science, starting from data entry up to Deep learning and Artificial Intelligence
• Program divided into 28 intensive data science courses, each supported with exercises and projects.
• Used Python and intriguing algorithms to analyze and solve complex business problems.

• Deep theory was grasped from Prof. Andrew.
• Comprehensive elaboration which was intriguing and essential to understand the basics of Machine learning
• Deep understand of the math behind each part in machine learning and how to deploy it to the models.
• Several techniques was explained to perform programming tasks.

Data Analytics Consulting virtual internship with below following tasks:

• Data Quality Assessment.
• Data Insights.
• Data Presentation and Visualization. Cleaned, preprocessed, managed, explored, and analyzed large amounts of sales and customers data to create customer analytics dashboard for KPMG company.
• Performed system analysis, documentation, testing, implementation and user support for platform transitions.
• Managed to perform spectacular dashboard and to obtain identical results of KPMG ideal solutions (3490 customers and KPI of 10.93M)

• Program divided in to 9 courses, devoted in theory behind data science.
• Leveraged perfectly how to use each program in data science from experienced Dr's, Prof's in this specialization.
• Created data visualization graphics, translating complex data sets into comprehensive visual representations.
• Translated benefits of machine learning technology for non-technical audiences, including cost/benefit analyses.
• Presented data science findings to peers, illustrating progress made during various tasks.
• Studied extensively in machine learning engineering and ended with massive capstone project from IBM.

Participated in numerous of Kaggle competitions for ML, AI: (a subsidiary of Google LLC, is an online community of data scientists)

• Titanic: Machine Learning from Disaster to predict survivals (67/45453 Top 1%, Applied technique: Random Forest Classifier with Decision Trees).
• House Prices - Advanced Regression Techniques: Predict sales prices and practice feature engineering (67/45453 Top 1%, Applied technique: XGBoost Regressor).
• Predict Future Sales: Final project for "How to win a data science competition" Coursera course (79/11636 Top 1%, Applied technique: XGBoost Regressor).
• Google Landmark Recognition 2020: Label famous (and not-so-famous) landmarks in images (312/736 Top 43%, Applied technique: Deep Learning with Convolutional Neural Network).
• OSIC Pulmonary Fibrosis Progression: Predict lung function decline (300/2097 Top 15%, Applied technique: Deep Learning with Convolutional Neural Network).
• VinBigData-Chest-Xray-abnormalities-detection: Automatically localize and classify thoracic abnormalities from chest radiographs (473/1275, Top 38%, Applied technique: Deep Learning with Convolutional Neural Network).
• Cassava Leaf Disease Classification: Identify the type of disease present on a Cassava Leaf image (732/3900, Top 19%, Applied technique: Deep Learning with Convolutional Neural Network).
• BirdCLEF 2021 challenge: Identifying bird calls in soundscape recordings (189/816, Top 24%, Applied technique: Deep Learning with Convolutional Neural Network).
• Rainforest-Connection-Species-Audio-Detection: (218/1143, Top 20%, Applied technique: Applied technique: Deep Learning with Convolutional Neural Network).