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Certified Data Scientist Professional - CDSP

Machine Learning, Data Science And Intro to Deep Learning Diploma

Training Program Description
Dive into the thriving world of Machine Learning and Data Science where demand exceeds supply. The industry is wholeheartedly embracing AI, promising a continuous surge in job opportunities and salaries for skilled professionals. Secure a future-proof career by mastering data manipulation, visualization, predictive analytics, machine learning, and data science. Launch or advance your data career with hands-on experience, expert-designed projects, and invaluable support from mentors and peers. Cap it off by showcasing your applied skills with a Data Science and Machine Learning capstone project for prospective employers. Your journey to success starts now!
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CDSP Curriculum:

Duration: 180 Hours

Price Before Discount: 18,000.00 EGP 

Price After 45% Discount : 9,900.00 EGP 

• Introduction to Data Analysis, Machine Learning, Data Science.
• Introduction to AI, Computer Vision, Autonomous and NLP.
• Data Science Process Activities.
• Data Different jobs (Data Engineer – Data Analyst – Data scientist – ML engineer – MLOps Engineer) .
• Roadmap for Data Science and AI.

• Environment Setup (Anaconda).
• Virtual Environments Concept.
• Command Line.
• Conda & pip package managers.
• Jupyter Notebook.
• Why python for data science.
• Intro to python.
    o Input & Output.
    o Variables.
    o Data types.
            • Numbers & Math.
            • Boolean & Comparison & Bitwise. and Logic.
            • Strings – Strings Methods.
    o If Conditions.
    o For & While Loops.
    o Lists.
    o Tuples.
    o Sets.
    o Dictionaries.
    o List Comprehensions.
    o Dictionary Comprehensions.
• Exceptions.
• File Handling.
• Functions.
• Built-in functions &  Operators (zip, enumerate, range, …).
• Map, Filter, Reduce.
• Lambda Expressions.
• PROJECT #1 ROCK PAPER SCISSORS.
• PROJECT #2 HANG MAN.
• Modules & Packages
• Git & GitHub (Version Control).
• GitKraken.
• PROJECT #3 PY.
• Object-Oriented Programming (OOP)
    o Classes & Objects.
    o Data Hiding and Encapsulation.
    o Inheritance. 
    o PROJECT #4 LIBRARY SYSTEM USING OOP.
    o PROJECT #5 BANK SYSTEM USING OOP.

• Public datasets websites.
• Network Topologies.
• Internet and Web Servers.
• HTTP Request/Response Cycle.
• Web Services & JSON.
• Intro to HTML and CSS – Online Playlist.
• Scrapping Concept.
• Download Files.
• Beautiful Soap Library.
• PROJECT #6 WUZZUF JOBS DATA COLLECTING USING WEB SERVICES.
• PROJECT #7 DIWAN BOOKS DATA COLLECTING SYSTEM.

• Tables, Columns and Data types.
• How to design a database.
• One-To-Many & Many-To-Many Relationships.
• MySQL Workbench.
• ACTIVITY DESIGN DATABASE STRUCTURE LIKE FACEBOOK, TALABAT, YOUTUBE.
• PROJECT #8 DESIGN E-COMMERCE DATABASE.
• SQL.
• CRUD.
• Selecting data.
• Filtering data.
• Ordering data.
• Limiting data.
• Aggregate Functions.
• Joining tables.
• Grouping data.
• Dealing with date and time SQL.
• Subqueries.
• Window Functions.
• Inserting new data.
• Updating data.
• Deleting data.
• Python and MySQL.
• PROJECT #9 ECOMMERCE SYSTEM DATABASE ANALYSIS.
• PROJECT #10 LYNDA COURSES DATABASE ANALYSIS.

• Liner Algebra.
    o Vector’s operations.
    o Matrix operations.
    o Victor Norm.
    o Eigen Values, Eigen Vectors and Eigen. decomposition.
• Statistics.
    o Understanding data.
    o Central Tendency.
    o Measures of Dispersions.
    o Correlation.
    o Normal Distributions.
    o Standard Normal Distributions.
    o Sample Distribution.
    o Central Limit Theorem.
    o Confidence Interval.
    o Statistical Significance.
    o Hypothesis Testing.
    o A/B Testing.
• Probability.
• Calculus.
    o Rate of Change.
    o First order and second order derivatives.
    o Partial Derivatives.
    o Chain rule.

• EDA Process.
• Linear Algebra.
    o Vector’s operations.
    o Matrix operations.
    o Victor Norm.
• NumPy.
    o Create NumPy Array
    Indexing.
    o Arithmetic and Logic.
    o Universal Array Functions.
• Statistics.
    o Understanding data.
    o Central Tendency.
    o Measures of Dispersions.
    o Correlation.
    o Normal Distributions.
    o Standard Normal Distributions.
    o Sample Distribution.
    o Central Limit Theorem.
    o Confidence Interval.
    o Statistical Significance.
    o Hypothesis Testing.
    o A/B Testing.
• Pandas.
    o Series.
    o Data Frames.
    o Data Input & Output.
    o Useful Methods.
    o Apply function.
    o Grouping data and aggregate functions.
    o Merging, Joining and Concatenating.
    o Pivoting.
• PROJECT #11 MOVIES DATASET FROM KAGGLE.
• PROJECT #12 SHOPPING CART DATASET FROM KAGGLE.
• PROJECT #13 FIFA DATASET FROM KAGGLE.

• Plotly.
    o Distribution Plots.
    o Categorical Plots.
    o Matrix Plots.
• Dash.
    o Customize plots (colors, markers, line styles, Limits, Legends, Layouts.
    o Text and Annotations.
• PROJECT #11 MOVIES DATASET FROM KAGGLE CONT.
• PROJECT #12 SHOPPING CART DATASET FROM KAGGLE CONT.
• PROJECT #13 FIFA DATASET FROM KAGGLE CONT.

• Feature Engineering and Extraction.
    o Domain knowledge features.
    o Date and Time features.
    o String operations.
    o Web Data.
    o Geospatial features.
• Feature Transformations.
    o Data Cleaning and Cleansing.
    o Work with Duplicated data.
    o Detect and Handle Outliers.
    o Work with Missing data.
    o Work with Categorical data.
    o Deal with Imbalanced classes.
    o Split data to Train and Test Sets.
    o Feature Scaling.
    o Data Preprocessing Mind Map.
    o PROJECT #14 GOOGLE PLAY STORE.
• PROJECT #15 DATA ANALYST JOBS ANALYSIS.
• PROJECT #16 UBER ANALYSIS.
• PROJECT #17 SALES PRODUCT DATA ANALYSIS.

• DATA ANALYSIS MID – PROJECT DISCUSSION.

• Intro to Machine Learning.
• Calculus.
    o Rate of Change.
    o First order and second order derivatives.
    o Partial Derivatives.
    o Chain rule.
• Supervised Learning
    Regression.
    o Simple Linear Regression.
    o Multiple Linear Regression.
    o Other Regression Methods (polynomial).
    o Normal Equation.
    o Regularization.
    o Evaluating Model Performance.
    o PROJECT #18 USED CARS PRICES PREDICTION.
    o PROJECT #19 UBER FARES PREDICTIONS.
    o PROJECT #20 AIR FLIGHT PRICE PREDICTIONS.
• Classification.
    o Logistic Regression.
    o K-Nearest Neighbors (KNN).
    o SVM.
    o Probability.
    o Bayes Theorem.
    o Naive Bayes.
    o Decision Trees.
    o Random Forests.
    o Ensemble Methods.
    o Bagging & Boosting.
    o XGBoost.
    o Evaluating Model. Performance.
    o Feature selection.
    o PROJECT #21 AIRLINE PASSENGER SATISFACTION PROBLEM.
    o PROJECT #22 CREDIT CARD APPROVAL PROBLEM.
• Unsupervised Learning.
  o Clustering.
    o K-Means.
    o Hierarchical Clustering.
    o DBSCAN.
    o PROJECT #23 HOUSE CLUSTERING.
    o PROJECT #24 ONLINE RETAIL CLUSTERING.
    o Dimension Reduction.
    o Linear Transformations.
    o Eigen Values, Eigen Vectors and Eigen decomposition.
    o PCA.
    o PROJECT #25 MNIST DATA.
    o PROJECT #26 X-RAY DATA.
• Model Selection & Evaluation.
    Cross Validation.
    Hyperparameter Tuning
    Grid Search.
    Randomized Search.

• Streamlit as an app framework for data apps.
• Streamlit layouts and objects.
• Deployment with Streamlit.
• PROJECT #27 USED CARS PRICE PREDICTOR WEB APPLICATION DEPLOYMENT ON STREAMLIT.

• DATA SCIENCE FINAL – PROJECT DISCUSSION.

• Boost your Profile on Kaggle.
• Build up your online presence.
• Build your Resume.
• LinkedIn and Networking
• Learn how to seek a job.

• This program is comprised of many career-oriented projects. Each project you build will be an opportunity to demonstrate what you’ve learned. Your completed projects will become part of a career portfolio that will demonstrate to potential employers that you have skills in data analysis and feature engineering, machine learning algorithms, and training and evaluating models.

• One of our main goals at EAII is to help you create a job-ready portfolio of completed projects. Building a project is one of the best ways to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers or colleagues. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects.

  • Project 1:  Rock paper scissors
  • Project 2:  Hung man.
  • Project 3:  Thanos
  • Project 4:  Library System using OOP.
  • Project 5:  Bank System using OOP.
  • Project 6:  Wuzzuf Jobs data collecting using web services.
  • Project 7:  Diwan Books data collecting system.
  • Project 8:  Design E-commerce Database.
  • Project 9:  Ecommerce system database analysis
  • Project 10: Lynda Courses database analysis
  • Project 11: Movies dataset from Kaggle
  • Project 12: Shopping cart dataset from Kaggle
  • Project 13: FIFA dataset from Kaggle
  • Project 14: Google Play Store
  • Project 15: Data Analyst Jobs Analysis
  • Project 16: Uber Analysis
  • Project 17: Netflix data Analysis
  • Project 18: Used Cars Prices Prediction
  • Project 19: Uber fares Predictions
  • Project 20: Air flight price Predictions
  • Project 21: Airline passenger satisfaction Problem
  • Project 22: Credit card approval Problem
  • Project 23: House clustering
  • Project 24: Online retail clustering
  • Project 25: Mnist Data
  • Project 26: X-ray Data
  • Project 27: Used Cars price predictor web application deployment on Streamlit
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CDSP + Intro To Deep Learning Curriculum:

Duration: 216 Hours

Price Before Discount: 20,000.00 EGP 

Price After 43% Discount : 11,400.00 EGP 

• Introduction to Data Analysis, Machine Learning, Data Science.
• Introduction to AI, Computer Vision, Autonomous and NLP.
• Data Science Process Activities.
• Data Different jobs (Data Engineer – Data Analyst – Data scientist – ML engineer – MLOps Engineer) .
• Roadmap for Data Science and AI.

• Environment Setup (Anaconda).
• Virtual Environments Concept.
• Command Line.
• Conda & pip package managers.
• Jupyter Notebook.
• Why python for data science.
• Intro to python.
    o Input & Output.
    o Variables.
    o Data types.
            • Numbers & Math.
            • Boolean & Comparison & Bitwise. and Logic.
            • Strings – Strings Methods.
    o If Conditions.
    o For & While Loops.
    o Lists.
    o Tuples.
    o Sets.
    o Dictionaries.
    o List Comprehensions.
    o Dictionary Comprehensions.
• Exceptions.
• File Handling.
• Functions.
• Built-in functions &  Operators (zip, enumerate, range, …).
• Map, Filter, Reduce.
• Lambda Expressions.
• PROJECT #1 ROCK PAPER SCISSORS.
• PROJECT #2 HANG MAN.
• Modules & Packages
• Git & GitHub (Version Control).
• GitKraken.
• PROJECT #3 PY.
• Object-Oriented Programming (OOP)
    o Classes & Objects.
    o Data Hiding and Encapsulation.
    o Inheritance. 
    o PROJECT #4 LIBRARY SYSTEM USING OOP.
    o PROJECT #5 BANK SYSTEM USING OOP.

• Public datasets websites.
• Network Topologies.
• Internet and Web Servers.
• HTTP Request/Response Cycle.
• Web Services & JSON.
• Intro to HTML and CSS – Online Playlist.
• Scrapping Concept.
• Download Files.
• Beautiful Soap Library.
• PROJECT #6 WUZZUF JOBS DATA COLLECTING USING WEB SERVICES.
• PROJECT #7 DIWAN BOOKS DATA COLLECTING SYSTEM.

• Tables, Columns and Data types.
• How to design a database.
• One-To-Many & Many-To-Many Relationships.
• MySQL Workbench.
• ACTIVITY DESIGN DATABASE STRUCTURE LIKE FACEBOOK, TALABAT, YOUTUBE.
• PROJECT #8 DESIGN E-COMMERCE DATABASE.
• SQL.
• CRUD.
• Selecting data.
• Filtering data.
• Ordering data.
• Limiting data.
• Aggregate Functions.
• Joining tables.
• Grouping data.
• Dealing with date and time SQL.
• Subqueries.
• Window Functions.
• Inserting new data.
• Updating data.
• Deleting data.
• Python and MySQL.
• PROJECT #9 ECOMMERCE SYSTEM DATABASE ANALYSIS.
• PROJECT #10 LYNDA COURSES DATABASE ANALYSIS.

• Liner Algebra.
    o Vector’s operations.
    o Matrix operations.
    o Victor Norm.
    o Eigen Values, Eigen Vectors and Eigen. decomposition.
• Statistics.
    o Understanding data.
    o Central Tendency.
    o Measures of Dispersions.
    o Correlation.
    o Normal Distributions.
    o Standard Normal Distributions.
    o Sample Distribution.
    o Central Limit Theorem.
    o Confidence Interval.
    o Statistical Significance.
    o Hypothesis Testing.
    o A/B Testing.
• Probability.
• Calculus.
    o Rate of Change.
    o First order and second order derivatives.
    o Partial Derivatives.
    o Chain rule.

• EDA Process.
• Linear Algebra.
    o Vector’s operations.
    o Matrix operations.
    o Victor Norm.
• NumPy.
    o Create NumPy Array
    Indexing.
    o Arithmetic and Logic.
    o Universal Array Functions.
• Statistics.
    o Understanding data.
    o Central Tendency.
    o Measures of Dispersions.
    o Correlation.
    o Normal Distributions.
    o Standard Normal Distributions.
    o Sample Distribution.
    o Central Limit Theorem
    o Confidence Interval.
    o Statistical Significance.
    o Hypothesis Testing.
    o A/B Testing.
• Pandas.
    o Series.
    o Data Frames.
    o Data Input & Output.
    o Useful Methods.
    o Apply function.
    o Grouping data and aggregate functions.
    o Merging, Joining and Concatenating.
    o Pivoting.
• PROJECT #11 MOVIES DATASET FROM KAGGLE.
• PROJECT #12 SHOPPING CART DATASET FROM KAGGLE.
• PROJECT #13 FIFA DATASET FROM KAGGLE.

• Plotly.
    o Distribution Plots.
    o Categorical Plots.
    o Matrix Plots.
• Dash.
    o Customize plots (colors, markers, line styles, Limits, Legends, Layouts.
    o Text and Annotations.
• PROJECT #11 MOVIES DATASET FROM KAGGLE CONT.
• PROJECT #12 SHOPPING CART DATASET FROM KAGGLE CONT.
• PROJECT #13 FIFA DATASET FROM KAGGLE CONT.

• Feature Engineering and Extraction.
    o Domain knowledge features.
    o Date and Time features.
    o String operations.
    o Web Data.
    o Geospatial features.
• Feature Transformations.
    o Data Cleaning and Cleansing.
    o Work with Duplicated data.
    o Detect and Handle Outliers.
    o Work with Missing data.
    o Work with Categorical data.
    o Deal with Imbalanced classes.
    o Split data to Train and Test Sets.
    o Feature Scaling.
    o Data Preprocessing Mind Map.
    o PROJECT #14 GOOGLE PLAY STORE.
• PROJECT #15 DATA ANALYST JOBS ANALYSIS.
• PROJECT #16 UBER ANALYSIS.
• PROJECT #17 SALES PRODUCT DATA ANALYSIS.

• DATA ANALYSIS MID – PROJECT DISCUSSION.

• Intro to Machine Learning.
• Calculus.
    o Rate of Change.
    o First order and second order derivatives.
    o Partial Derivatives.
    o Chain rule.
• Supervised Learning
    Regression.
    o Simple Linear Regression.
    o Multiple Linear Regression.
    o Other Regression Methods (polynomial).
    o Normal Equation.
    o Regularization.
    o Evaluating Model Performance.
    o PROJECT #18 USED CARS PRICES PREDICTION.
    o PROJECT #19 UBER FARES PREDICTIONS.
    o PROJECT #20 AIR FLIGHT PRICE PREDICTIONS.
• Classification.
    o Logistic Regression.
    o K-Nearest Neighbors (KNN).
    o SVM.
    o Probability.
    o Bayes Theorem.
    o Naive Bayes.
    o Decision Trees.
    o Random Forests.
    o Ensemble Methods.
    o Bagging & Boosting.
    o XGBoost.
    o Evaluating Model. Performance.
    o Feature selection.
    o PROJECT #21 AIRLINE PASSENGER SATISFACTION PROBLEM.
    o PROJECT #22 CREDIT CARD APPROVAL PROBLEM.
• Unsupervised Learning.
  o Clustering.
    o K-Means.
    o Hierarchical Clustering.
    o DBSCAN.
    o PROJECT #23 HOUSE CLUSTERING.
    o PROJECT #24 ONLINE RETAIL CLUSTERING.
    o Dimension Reduction.
    o Linear Transformations.
    o Eigen Values, Eigen Vectors and Eigen decomposition.
    o PCA.
    o PROJECT #25 MNIST DATA.
    o PROJECT #26 X-RAY DATA.
• Apriori Algorithm.
    o PROJECT #27 MARKET BASKET ANALYSIS.
• Model Selection & Evaluation.
    Cross Validation.
    Hyperparameter Tuning
    Grid Search.
    Randomized Search.

• Streamlit as an app framework for data apps.
• Streamlit layouts and objects.
• Deployment with Streamlit.
• PROJECT #28 USED CARS PRICE PREDICTOR WEB APPLICATION DEPLOYMENT ON STREAMLIT.

• DATA SCIENCE FINAL – PROJECT DISCUSSION.

• Boost your Profile on Kaggle.
• Build up your online presence.
• Build your Resume.
• LinkedIn and Networking
• Learn how to seek a job.

(1) Intro to Artificial neural networks (ANNs).

• Introduction to Deep Learning.
• Deep Learning Applications.
• Google Colab.
• Perceptron.
• Artificial Neural Networks (ANN).
    o Activation functions.
    o Error or Loss or Cost functions.
    o Optimization algorithms
    Backpropagation.
    o Improve neural network training.
    o Deep Learning frameworks
    o PROJECT #29 MNIST DATA.

(2) Intro to Convolutional neural networks (CNNs) and Computer Vision.

• Drawbacks of ANN when dealing with images.
• Convolutions.
• Pooling.
• Data Augmentation.
• Batch Normalization.
• Image Classification.
• PROJECT #30 COVID-19 MASK OR NOT.
• Model Selection & Evaluation.
    o Cross Validation.
    o Hyperparameter Tuning.
    o Grid Search.
• Randomized Search.
• Transfer learning.
• CNN Architectures.
    o Alexnet.
    o VGG.
    o Inception.
  o PROJECT #31 EMOTION RECOGNITION.

(3) Intro to NLP

• Introduction to NLP and Text Preprocessing.
• Introduction to NLP and its Applications.
• Text Preprocessing: tokenization, stemming, and lemmatization.
• Exploratory Data Analysis: word frequency distributions and word clouds.
• Text Representation: bag-of-words.
• Text Classification Methods.
• PROJECT #32 TEXT CLASSIFICATION (IMDB DATASET).
• Information Retrieval: keyword-based search and semantic search.
• Text Summarization: extractive summarization.
• Sentiment Analysis.
• PROJECT #33 TEXT SUMMARIZATION (WIKIPEDIA ARTICLES DATASET).

• This program is comprised of many career-oriented projects. Each project you build will be an opportunity to demonstrate what you’ve learned. Your completed projects will become part of a career portfolio that will demonstrate to potential employers that you have skills in data analysis and feature engineering, machine learning algorithms, and training and evaluating models.

 

• One of our main goals at EAII is to help you create a job-ready portfolio of completed projects. Building a project is one of the best ways to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers or colleagues. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects.

 

  • Project 1:  Rock paper scissors
  • Project 2:  Hung man.
  • Project 3:  Thanos
  • Project 4:  Library System using OOP.
  • Project 5:  Bank System using OOP.
  • Project 6:  Wuzzuf Jobs data collecting using web services.
  • Project 7:  Diwan Books data collecting system.
  • Project 8:  Design E-commerce Database.
  • Project 9:  Ecommerce system database analysis
  • Project 10: Lynda Courses database analysis
  • Project 11: Movies dataset from Kaggle
  • Project 12: Shopping cart dataset from Kaggle
  • Project 13: FIFA dataset from Kaggle
  • Project 14: Google Play Store
  • Project 15: Data Analyst Jobs Analysis
  • Project 16: Uber Analysis
  • Project 17: Netflix data Analysis
  • Project 18: Used Cars Prices Prediction
  • Project 19: Uber fares Predictions
  • Project 20: Air flight price Predictions
  • Project 21: Airline passenger satisfaction Problem
  • Project 22: Credit card approval Problem
  • Project 23: House clustering
  • Project 24: Online retail clustering
  • Project 25: Mnist Data – Machine learning
  • Project 26: X-ray Data
  • Project 27: Market Basket Analysis
  • Project 28: Used Cars price predictor web application deployment on Streamlit
  • Project 29: Mnist Data – Deep Learning
  • Project 30: COVID-19 Mask or Not
  • Project 31: Emotion Recognition
  • Project 32: Text Classification (IMDB Dataset)
  • Project 33: Text Summarization (Wikipedia articles dataset)
  • Project 34: Create Professional Dashboard using Power BI
  • Capstone Project
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Certificate

Upon successful completion of the Program, participants will receive a verified digital certificate from EPSILON AI, Delaware, USA. if they attend a minimum of 85 percent of the direct contact hours of the Program and after fulfilling program requirements 
(passing both Final Exam and Project to obtain the Certificate)
  • Prerequisites:

• Basic skills with at least one programming language are desirable – Optional
• Familiar with the basic math and statistics concepts – Optional

  • Program Outcomes:

• Build predictive models using a variety of unsupervised and supervised machine learning techniques.
• Perform feature engineering to improve the performance of machine learning models.
• Optimize, tune, and improve algorithms according to specific metrics like accuracy and speed.
• Compare the performances of learned models using suitable metrics.
• analyze, design and document a system component using appropriate data analytical techniques and models.
• demonstrate an understanding of fundamental principles of data analytics systems and technologies.
• Able to use standard techniques of mathematics, probability, and statistics to address problems typical of a career in data science.
• Apply appropriate modeling techniques to conduct quantitative analyses of complex big data sets.
• Use statistical software packages such Python to solve data science problems.
• Communicate results effectively to stakeholders.
• Use principles of statistics and probability to design and execute A/B tests and recommendation.
• Deploy machine learning models into the cloud.
• Send and receive requests from deployed machine learning models.
• Build reproducible machine learning pipelines.
• Create continuous and automated integrations to deploy your models.
• Build machine learning model APIs.
• Design testable, version controlled and reproducible production code for model deployment.
• Perform feature engineering to improve the performance of machine learning models.
• Transition from the Very Basics to a Point Where You Can Effortlessly Work with Large SQL Queries
• Web Scraping using Python, scrape data and store it locally or globally to access the data sets whenever needed.
• Boost your Profile.
• identifying opportunities for data science across many functional areas of the business

  • Who Should Attend:

• This Program is primarily for individuals who are passionate about the field of data science, Machine Learning, and data analysts and who are aspiring to apply machine learning in their business, industry, or research.
• Developers and Software Engineers
• Analytics Managers and Professionals
• Statisticians with an interest in Machine

  • Payment Terms And Methods:

Payment must be made prior to Program commencement at Epsilon AI Institute, HQs
• In-Person
       o In Cash to our address:
            • Elserag shopping mall, Residential Building 1, Entrance 1, Floor 11
            • Alfouad administrative Tower, Building 22, Floor 2, Anas ebn malek str., Shehab Str., Mohandessin, Cairo, Egypt

      o By cheque – Payable to: Epsilon ابسلون للتدريب
      o Credit Card
• Bank transfer to our ACC in (Excluding Bank Transfer Fees):
      o QNB ALAHLI Acc /20318280579-69 EGP Branch code / 00078
• Vodafone Cash to 01011933233
• Credit Card online
• Cash Collection from Client’s Premises
• Masary/Aman Service
• Fawry Service
• Wallet Transfer
• Banks Credit Card Installments (up to 36 months)
• VALU Installments (up to 36 months)
• Credit Card Bank installments

Learn with AI & Data Science Industry leaders and Knowledgable instructors.

Instructors with more than +7 years of experience in the labor market.
Meet Our Insructors
"Our business has data at its core. In a competitive marketplace, we have a growing need for employees trained in the critical field of data science with strong analytical skills. The CDSP Certificate provides an excellent opportunity for learners to have rigorous training in statistics, data analysis and machine learning and to develop the skills necessary to be competitive in a world increasingly shaped by data."
Ahmed Adel
Ahmed AdelMogassam CEO
"As always, Epsilon AI is fantastic. I loved the CDSP Training Program. It is still a very good course which covers almost everything. Thank you Epsilon AI team, You all are simply the best. Anas Awad"
Ahmed Osama
Ahmed OsamaManager @ Vodafone
"Amazing. It explains the necessary fundamentals in a clear and intuitive way that other courses do not teach, while also achieving a good level of depth. Amazing for consolidating knowledge"
Anas Awad
Anas AwadData Scientist @ orange
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