There is a shortage of qualified Data Scientists in the workforce, and individuals with these skills are in high demand. Build skills in programming, data wrangling, machine learning, experiment design, and data visualization, and launch a career in data science.

Prerequisites:

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

Training Program Description:

• 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.

• 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 future-proof careers.

• 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

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 anvirtual d 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

• Building a project is one of the best ways both to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers. 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: Sales product data Analysis
• Project 18: Ecommerce System Data Analysis
• Project 19: Netflix data Analysis
• Project 20: Used Cars Prices Prediction
• Project 21: Uber fares Predictions
• Project 22: Air flight price Predictions
• Project 23: Churn Problem
• Project 24: Airline passenger satisfaction Problem
• Project 25: Credit card approval Problem
• Project 26: House clustering
• Project 27: Online retail clustering
• Project 28: Mnist Data
• Project 29: X-ray Data
• Project 30: Market Basket Analysis
• Project 31: Used Cars price predictor web application deployment on Heroku
• Capstone Project

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 analysis 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 as 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 recommendations.
• 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.
• The 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

Program Duration: 204 Hours

Program Language: English / Arabic

Location: EPSILON AI INSTITUTE | Head Office / Virtual Online Live Classroom

Participants will be granted a completion certificate from Epsilon AI Institute, USA if they attend a minimum of 80 percent of the direct contact hours of the Program and after fulfilling program requirements (passing both the Final Exam and Project to obtain the Certificate)

CURRICULUM

Training Program Curriculum

1. Intro to Programming & AI World

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

2. 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 HANGMAN
• Modules & Packages
• Git & GitHub (Version Control)
• GitKraken
• PROJECT #3 Thanos.PY
• Object-Oriented Programming (OOP)
  • Classes & Objects
  • Data Hiding and Encapsulation
  • Inheritance
  • PROJECT #4 LIBRARY SYSTEM USING OOP
  • PROJECT #5 BANK SYSTEM USING OOP

3. 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

4.Databases & MySQL

• RDBMS
• Tables, Columns, and Data types
• How to design a database.
• One-To-Many & Many-To-Many Relationships.
• MySQL Workbench
• ACTIVITY DESIGN DATABASE STRUCTURES LIKE FACEBOOK, TALABAT, YOUTUBE
• PROJECT #8 DESIGN AN E-COMMERCE DATABASE
• SQL
• CRUD
• Selecting data
• Filtering data
• Ordering data
• Limiting data
• Aggregate Functions
• Joining tables
• Grouping data
• Dealing with the 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

5. 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

6. 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.

7. 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
• PROJECT #18 ECOMMERCE SYSTEM DATA ANALYSIS
• PROJECT #19 NETFLIX DATA ANALYSIS

8. Data Analysis Final Project

• DATA ANALYSIS FINAL PROJECT DISCUSSION

9. 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 #20 USED CARS PRICES PREDICTION
    • PROJECT #21 UBER FARES PREDICTIONS
    • PROJECT #22 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
      • PROJECT #23 CHURN PROBLEM
  • Feature selection
    • • PROJECT #24 AIRLINE PASSENGER SATISFACTION PROBLEM
      • PROJECT #25 CREDIT CARD APPROVAL PROBLEM
• Unsupervised Learning
  • Clustering
    • K-Means
    • Hierarchical Clustering
    • DBSCAN
    • PROJECT #26 HOUSE CLUSTERING
    • PROJECT #27 ONLINE RETAIL CLUSTERING
  • Dimension Reduction
    • Linear Transformations
    • Eigen Values, Eigen Vectors and Eigen decomposition
    • PCA
    • PROJECT #28 MNIST DATA
    • PROJECT #29 X-RAY DATA
  • Apriori Algorithm
    • PROJECT #30 MARKET BASKET ANALYSIS
  • Model Selection & Evaluation
    • Cross Validation
    • Hyperparameter Tuning
      • Grid Search
      • Randomized Search

10. Software Engineering & Model Deployment

• Streamlit as an app framework for data apps.
• Streamlit layouts and objects
• Deployment with Streamlit
• PROJECT #31 USED CARS PRICE PREDICTOR WEB APPLICATION DEPLOYMENT
  ON STREAMLIT

11. CAPSTONE PROJECT

• FINAL PROJECT DISCUSSION

12. Advance your Career

• Boost your Profile on Kaggle
• Build up your online presence
  • Medium Blog
  • YouTube Channel
  • Contribute to Open-Source Community on GitHub
• Build your Resume
• LinkedIn and Networking
• Learn how to seek a job

Download Certified Data Scientist Professional – CDSP Brochure PDF