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Certified Data Scientist Professional - CDSP

Machine Learning, Data Science And Intro to Deep Learning Diploma

Diploma Details

Duration:
216 Hours
 
Price before discount
20,000.00 EGP
 
Price After 43% discount 
11,400.00 EGP
 

Training Program Description

• The demand for Machine Learning and Data science professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Machine Learning and Data science skills by employers — and the job salaries of Machine Learning and Data Science practitioners — are only bound to increase over time, as AI becomes more pervasive in society. Machine Learning and Data Science are a future-proof career.
• Build expertise in data manipulation, visualization, predictive analytics, machine learning, and data science. With the skills you learn in a program, you can launch or advance a successful data career. Start acquiring valuable skills right away, create a project portfolio to demonstrate your abilities, and get support from mentors, peers, and experts in the field 
• Gain real-world data science experience with projects designed by industry experts. Build your portfolio and advance your data science and machine learning career
• Throughout this program you will practice your Data Science and Machine Learning skills through a series of hands-on labs, assignments, and projects inspired by real world problems and data sets from the industry. You will also complete the program by preparing a Data Science and Machine Learning capstone project that will showcase your applied skills to prospective employers.
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Training Program Curriculum
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Curriculum:

216 Hours

  • MODULE I
  • Intro to Data Analysis World Using Python
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
  • MODULE II
  • Python Programming
Environment Setup (Anaconda)
Virtual Environments Concept
Command Line
Conda & pip package managers
Jupyter Notebook
Why python for data science
Intro to python
     Input & Output
     Variables
     Data types
            Numbers & Math
            Boolean & Comparison & Bitwise and Logic.
           Strings – Strings Methods.
     If Conditions
     For & While Loops
     Lists
     Tuples
     Sets
     Dictionaries
     List Comprehensions
     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)
    Classes & Objects
     Data Hiding and Encapsulation
     Inheritance 
     PROJECT #4 LIBRARY SYSTEM USING OOP
     PROJECT #5 BANK SYSTEM USING OOP
  • MODULE III
  • Data Collecting – (Web Scraping & Web Services)
  • 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
  • MODULE IV
  • Databases & MySQL

 

  • 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
  • MODULE V
  • Mathematics for Data Science
  • Liner Algebra
    • Vector’s operations
    • Matrix operations
    • Victor Norm
    • Eigen Values, Eigen Vectors and Eigen decomposition
  • Statistics
    • Understanding data
    • Central Tendency
    • Measures of Dispersions
    • Correlation
    • Normal Distributions
    • Standard Normal Distributions
    • Sample Distribution
    • Central Limit Theorem
    • Confidence Interval
    • Statistical Significance
    • Hypothesis Testing
    • A/B Testing
  • Probability
  • Calculus
    • Rate of Change
    • First order and second order derivatives
    • Partial Derivatives
    • Chain rule
  • MODULE VI
  • Exploratory Data Analysis with NumPy & Pandas
  • EDA Process
  • Linear Algebra
    • Vector’s operations
    • Matrix operations
    • Victor Norm
  • NumPy
    • Create NumPy Array
    • Indexing
    • Arithmetic and Logic
    • Universal Array Functions
  • Statistics
    • Understanding data
    • Central Tendency
    • Measures of Dispersions
    • Correlation
    • Normal Distributions
    • Standard Normal Distributions
    • Sample Distribution
    • Central Limit Theorem
    • Confidence Interval
    • Statistical Significance
    • Hypothesis Testing
    • A/B Testing
  • Pandas
    • Series
    • Data Frames
    • Data Input & Output
    • Useful Methods
    • Apply function.
    • Grouping data and aggregate functions
    • Merging, Joining and Concatenating
    • Pivoting
  • PROJECT #11 MOVIES DATASET FROM KAGGLE
  • PROJECT #12 SHOPPING CART DATASET FROM KAGGLE
  • PROJECT #13 FIFA DATASET FROM KAGGLE

 

  • MODULE VII
  • Data Visualization with Plotly & Dash
  • Plotly
    • Distribution Plots
    • Categorical Plots
    • Matrix Plots
  • Dash
    • Customize plots (colors, markers, line styles, Limits, Legends, Layouts
    • 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.
  • MODULE VIII
  • Data Preprocessing
  • Feature Engineering and Extraction
    • Domain knowledge features
    • Date and Time features
    • String operations
    • Web Data
    • Geospatial features
  • Feature Transformations
    • Data Cleaning or Cleansing
    • Work with Duplicated data
    • Detect and Handle Outliers
    • Work with Missing data
    • Work with Categorical data
    • Deal with Imbalanced classes
    • Split data to Train and Test Sets
    • Feature Scaling
    • Data Preprocessing Mind Map
    • PROJECT #14 GOOGLE PLAY STORE
  • PROJECT #15 DATA ANALYST JOBS ANALYSIS
  • PROJECT #16 UBER ANALYSIS
  • PROJECT #17 SALES PRODUCT DATA ANALYSIS
  • MODULE IX
  • Data Analysis Mid-Project

  • DATA ANALYSIS MID – PROJECT DISCUSSION

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

  • FINAL PROJECT DISCUSSION

  • MODULE XIV
  • Intro to Deep Learning

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

        • Introduction to Deep Learning
        • Deep Learning Applications
        • Google Colab
        • Perceptron
        • Artificial Neural Networks (ANN)
        • Activation functions
        • Error or Loss or Cost functions
        • Optimization algorithms
        • Backpropagation
        • Improve neural network training.
        • Deep Learning frameworks
          • 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
          • Cross Validation
          • Hyperparameter Tuning
            • Grid Search
            • Randomized Search
          • Transfer learning
          • CNN Architectures
            • Alexnet
            • VGG
            • Inception
            • 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)
  • Projects

This program is comprised of many career-oriented projects. Each project you build will be an opportunity to demonstrate what you’ve learned in the lessons. 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: 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)

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 statistic 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

o 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.
o Developers and Software Engineers
o Analytics Managers and Professionals
o 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

Our Instructors

Instructors with more than +7 years of experience in the labor market. They have sufficient experience in various fields.

Meet Our Insructors

testimonials

"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."
Mohamed Haddad
"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"
Anas Awad
Anas Awad
"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"
Tarek Ziad
Tarek Ziad

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