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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 employers.

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

 

  • 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:  py
  • Project 2:  Library System using OOP.
  • Project 3:  Weather Logs data collecting system.
  • Project 4:  Employee’s data collecting using web services.
  • Project 5:  Design Database systems like Facebook, Souq, YouTube
  • Project 6:  Weather Logs data collecting system using database
  • Project 7:  Analyze SF Salaries dataset from Kaggle.
  • Project 8:  Analyze Ecommerce Purchase dataset from Kaggle.
  • Project 9:  Titanic Analysis Project
  • Project 10: 911 calls dataset from Kaggle analysis
  • Project 11: Preprocess Loan data
  • Project 12: Ecommerce Expenses Prediction
  • Project 13: Kaggle Bike Demand Predictions
  • Project 14: Kaggle Black Friday Purchase Predictions
  • Project 15: Predict Loan Approval Problem
  • Project 16: Advertising Problem
  • Project 17: Sentiment Analysis Problem
  • Project 18: Mall Problem
  • Project 19: University Problem
  • Project 20: Bike demand 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 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
  • Introduction to Deep Learning
  • Introduction To Data Analysis using Excel – Power BI – Tableau
  • Introduction to Cloud Computing Using Amazon AWS
  • Introduction To Apache Spark and Big Data

 

Program Duration: 25 Weeks

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 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
  • Review Diploma Content
  • Roadmap for AI
  • Introduction to Computer science
  • Environment Setup (Anaconda)
  • Command Line
  • Conda & pip package managers
  • Jupyter Notebook
  • Intro to python
  • Input & Output
  • Variables
  • Data types
    • Numbers & Math
    • Boolean & Comparison and Logic
    • Strings
    • Lists
    • Tuples
    • Sets
    • Dictionaries
  • File Handling
  • If Conditions
  • For Loops
  • Built-in functions & Operators (zip, enumerate, range, …)
  • List Comprehensions
  • Functions
  • Lambda Expressions
  • Map, Filter, Reduce
  • Modules & Packages
  • Git & GitHub
  • Project #1 (Thanos.py)
  • Object-Oriented Programming (OOP)
    • Classes & Objects
    • Data Hiding and Encapsulation
    • Inheritance
    • Exceptions
    • Project #2 (Library System using OOP)

 

 

2. Web Scraping & Web Services

  • Network Topologies
  • Internet and Web Servers
  • HTTP Request/Response Cycle
  • HTML
  • CSS
  • Scrapping Concept
  • Beautiful Soap Library
  • Web Services & JSON
  • Project #3 (Weather Logs data collecting system)
  • Project #4 (Employee’s data collecting using web services)

 

3. Databases & SQL

  • RDBMS
  • Tables, Columns, and Datatypes
  • How to design a database.
  • One-To-Many & Many-To-Many Relationships.
  • Project #5 (Design Database systems like Facebook, Souq, YouTube)
  • SQL
  • CRUD
  • Selecting data
  • Filtering data
  • Ordering data
  • Limiting data
  • Aggregate Functions
  • Joining tables
  • Grouping data
  • Subqueries
  • Inserting new data
  • Updating data
  • Deleting data
  • Python and SQLite
  • DB Browser for SQLite
  • Project #6 (Weather Logs data collecting system using a database)

 

4. Exploratory Data Analysis with NumPy & Pandas

  • Linear Algebra
    • Vector’s operations
    • Matrix operations
  • Statistics
    • Central Tendency
    • Measures of Dispersions
    • Data Visualization
    • Probability Density Function and Distributions
    • Normal Distributions
    • Standard Normal Distributions
    • Correlation and covariance
    • Sample Distribution
    • Central Limit Theorem
    • Confidence Interval
    • Statistical Significance
    • Hypothesis Testing
  • NumPy
    • Create Numpy Array
    • Indexing
    • Arithmetic and Logic
    • Universal Array Functions
  • Pandas
    • Series
    • Data Frames
    • Data Input & Output
    • Useful Methods
    • Apply function
    • Grouping data and aggregate functions
    • Merging, Joining, and Concatenating
    • Pivoting
  • Project #7 (Analyze SF Salaries dataset from Kaggle)
  • Project #8 (Analyze Ecommerce Purchase dataset from Kaggle)

 

5. Data Visualization with Matplotlib & Seaborn

  • Data Visualization
    • What is Data Visualization
    • Matplotlib and Seaborn
    • Distribution Plots
    • Categorical Plots
    • Matrix Plots
  • Project #9 (Titanic Analysis Project)
  • Project #10 (911 calls dataset from Kaggle analysis)

 

6. Data Preprocessing

  • Feature Engineering and Extraction
    • Domain knowledge features
    • Date and Time features
    • String operations
    • Web Data
    • Geospatial features
    • Work with Text
  • Feature Transformations
    • Data Cleaning or Cleansing
    • Work with Missing data
    • Work with Categorical data
    • Detect and Handle Outliers
    • Deal with Imbalanced classes
    • Split data to Train and Test Sets
    • Feature Scaling
    • Project #11 (Preprocess Loan data)

 

7. Data Analysis Final Project

  • Data Analysis Final Project Discussion

 

8. Machine Learning

  • Supervised Learning
    • Regression
      • Simple Linear Regression
      • Multiple Linear Regression
      • Other Regression Methods
      • Evaluating Model Performance
      • Project #12 (Ecommerce Expenses Prediction)
      • Project #13 (Kaggle Bike Demand Predictions)
      • Project #14 (Kaggle Black Friday Purchase Predictions)
    • Classification
      • Logistic Regression
      • K-Nearest Neighbors (KNN)
      • Naive Bayes
      • Decision Trees
      • SVM
      • Ensemble Methods
      • Bagging & Boosting
      • Random Forests
      • XGBoost
      • Evaluating Model Performance
      • Project #15 (Predict Loan Approval Problem)
      • Project #16 (Advertising Problem)
      • Project #17 (Sentiment Analysis Problem)
      • Unsupervised Learning
          • Clustering
            • K-Means
            • Hierarchical Clustering
            • Project #18 (Mall Problem)
            • Project #19 (University Problem)
          • Dimension Reduction
            • PCA
          • Model Selection & Evaluation
            • Cross-Validation
            • Hyperparameter Tuning
              • Grid Search
              • Randomized Search

 

9. Software Engineering & Model Deployment

  • What is Internet and Web Servers
  • HTTP Request/Response Cycle
  • HTML
  • CSS
  • Python as a backend language
  • Flask
  • Work with requests
  • Work with templates
  • Integrate machine learning model.
  • Deploy the app to Heroku.
  • Project #20 (Bike demand predictor web application deployment on Heroku)

 

10. 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.
  • Job Interview

 

11. CAPSTONE PROJECT

  • Final Project Discussion

 

12. INTRO TO DEEP LEARNING

  • What is Deep Learning
  • How Neuron works
  • What is Artificial Neural Network
  • How to train ANN
  • Apply the ANN on Bikes problem & Loan data problem from CDSP Diploma

 

13. INTRO TO DATA ANALYSIS USING EXCEL / POWER BI / TABLEAU

  • EXCEL
    • Create a pivot table and pivot chart
  • Power BI
    • Connecting to the data source
    • Edit Query with power query
    • Add Visualize to your report
    • Format visual
    • Visual analytics
    • Filter visuals
  • Tableau
    • Data Connections
    • Create visuals
    • Create dashboard

 

14. Cloud computing (AWS)

    • Intro to Cloud data warehouse architecture.
    • Intro to Cloud computing using AWS.
    • Starting EC2 machine demo on AWS

 

15. Apache Spark and Big data

    • Intro to Big-Data concepts.
    • Intro to Big-Data architecture.
    • Intro to Spark architecture.
    • ETL job demo with Apache Spark

 

 

 

 

 

 

 

 

 

Course Curriculum

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