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Prerequisites:

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

 

Training Program Description:

    • In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?
    • This course will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you'll learn how to use:
      • python
      • jupyter notebooks
      • pandas
      • NumPy
      • matplotlib
      • git
      • and many other tools.
    • You will learn these tools all within the context of solving compelling data science problems.
    • After completing this course, you'll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.
    • By learning these skills, you'll also become a member of a worldwide community that seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science School and AI School.

 

  • Who is this class for?
    • This course is primarily for individuals who are passionate about the field of data science 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

 

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: WRANGLE AND ANALYZE DATA
      • PROJECT 2: INVESTIGATE A DATASET
      • PROJECT 3: ANALYZE EXPERIMENT RESULTS
      • CAPSTONE PROJECT

 

program outcomes:

  • Use Python to access and analyze data from several different data sources.
  • The basic process of data science
  • Python and Jupyter notebooks
  • An applied understanding of how to manipulate and analyze uncurated datasets
  • Basic statistical analysis and machine learning methods
  • How to effectively visualize results

 

Program Duration: 6 Weeks

Program Language: English / Arabic

Location: EPSILON AI INSTITUTE | Head Office

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

I-PYTHON 3 TOPICS

  • INTRODUCTION
    • SETTING UP PYTHON
    • SYNTAX
    • FIRST PYTHON PROGRAM
    • VARIABLES & DATA TYPES
    • NUMBERS AND MATH
    • STRINGS
    • OPERATORS AND BITWISE
    • DATA STRUCTURES OF PYTHON
      • TUPLES
      • LISTS
      • SETS
      • DICTIONARIES
    • CONDITIONAL LOGIC AND CONTROL FLOW
    • FUNCTIONS AND LOOPS
    • I/O
    • WORKING WITH FILES
  • OBJECT-ORIENTED PROGRAMMING (OOP)
    • CLASSES
    • INHERITANCE
    • PYTHON GENERATORS
    • PYTHON DECORATORS
    • REGULAR EXPRESSIONS
    • EXCEPTIONS

 

II-SQL TOPICS

  • BASIC SQL
  • SQL JOINS
  • SQL AGGREGATIONS
  • SQL QUERIES

 

 III-DATA ANALYSIS TOPICS

  • THE DIFFERENCE BETWEEN DATA ANALYSIS, ML, BIG DATA, DEEP LEARNING AND DATA SCIENCE
  • ANACONDA/JUPYTER NOTEBOOK
  • NUMPY AND MATRIX OPERATIONS
  • PANDAS
  • DATA VISUALIZATION
  • GIT VERSION CONTROL
  • WEB SCRAPING FOR DATA COLLECTING
  • DATA PREPROCESSING
    • IMPORTING LIBRARIES
    • DATA ACQUISITION
    • DATA CLEANING
    • HANDLING MISSING DATA
    • CATEGORICAL DATA
    • DATA SPLITTING
    • FEATURE SCALING
    • FEATURE ENGINEERING
  • DATA WRANGLING
    • INTRO TO DATA WRANGLING
    • GATHERING DATA
    • ASSESSING DATA
    • CLEANING DATA
  • POWER BI & EXCEL

 

 IV-DATA STRUCTURES & ALGORITHMS TOPICS

  • INTRODUCTION
    • HOW TO SOLVE PROBLEMS
    • BIG O NOTATION
  • DATA STRUCTURES
    • COLLECTION DATA STRUCTURES (LISTS, ARRAYS, LINKED LISTS, QUEUES, STACK)
    • RECURSION
    • TREES
    • MAPS AND HASHING
  • BASIC ALGORITHMS
    • BINARY SEARCH
    • SORTING ALGORITHMS
    • DIVIDE & CONQUER ALGORITHMS
    • MAPS AND HASHING
    • PRACTICE PROBLEMS: RANDOMIZED BINARY SEARCH, K-SMALLEST ELEMENTS USING HEAPS, BUILD RED-BLACK TREE, BUBBLE SORT, MERGE SORT, QUICK SORT, SORTING STRINGS, LINEAR-TIME MEDIAN FINDING

 

Download Python for Data analysis and AI Specialist Certificate Brochure PDF

 

 

Course Curriculum

PYTHON 3 TOPICS
INTRODUCTION
OBJECT-ORIENTED PROGRAMMING (OOP)
SQL TOPICS
BASIC SQL
SQL JOINS
SQL AGGREGATIONS
SQL QUERIES
DATA ANALYSIS TOPICS
THE DIFFERENCE BETWEEN DATA ANALYSIS, ML, BIG DATA, DEEP LEARNING AND DATA SCIENCE
ANACONDA/JUPYTER NOTEBOOK
NUMPY AND MATRIX OPERATIONS
PANDAS
DATA VISUALIZATION
GIT VERSION CONTROL
WEB SCRAPING FOR DATA COLLECTING
DATA PREPROCESSING
DATA WRANGLING
POWER BI & EXCEL
DATA STRUCTURES & ALGORITHMS TOPICS
INTRODUCTION
DATA STRUCTURES
BASIC ALGORITHMS

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