Python & Statistics For Data Science & Artificial Intelligence

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Start DateTypeDaysTimeAvailable Seats
20-Apr-2024Interactive Online TrainingFriday5:00 PM-9:00 PMLimited

This diploma is the ideal scientific and practical choice for your excellence in data science.

This diploma is a combination of technical skills from both business sciences and technology to build a career future in one of the most important fields worldwide; where the most of global and innovative companies are utilizing data science to help making data-driven decisions after developing statistical models and algorithms

Diploma Objectives

  • Introduction to Data Career & Data Competencies
  • Databases Concepts & Relational Database
  • Statistics for Data Science
  • Data Analytics Using Python
  • Data-Driven Decision-Making Using Power BI

Why Study Data Science?

  • Become an Expert Data Scientist.
  • Opening the horizon for exciting and ever renewed career future worldwide in the field of data analysis and data science, which is characterized by a high salary for its employees.
  • Excellence in business by mastering technological techniques to explore and analyze data to reach data-driven decisions and aid in predicting and forecasting.
  • Proficiency in data analysis using the most versatile and adaptable programming language: Python.
  • Mastering the merit of making data-driven decisions.

For Whom is Data Science?

  • For business professionals who have a passion for acquiring distinguished and distinct competencies in the field of data analysis and become able to make data-driven decisions.
  • For those who wish to excel in the field of data analysis, data-driven decision and get advanced to the stage of anticipation and prediction using the development of statistical models and algorithms using Python programming language.

Upon The Completing The Diploma, You Will:

  • Module 1
    • Intro to Data Career and Road Map
    • Learn about the Competencies for Data Sciences
    • Differentiate your Skill as Data Scientist
  • Module 2
    • Understanding Databases Concepts & Relational Database Management
    • Creating Database Objects
    • Working with T-SQL Querying
  • Module 3
    • Learn the important topics in statistics for Data Science & Data Analysis
    • Understanding The Concept Of
      • Mode, Median and Mean
      • Variance and Standard deviation
      • Probability
      • Descriptive statistics
      • Linear Algebra
  • Module 4
    • Learn Python basics for Data Analysis
    • Working with Anaconda and Jupyter Notebook
    • Working with Panda – Data Analysis Library
    • Working With NumPy – Data Analysis Library
    • Visualization your Data Using Python
    • Using Python for web Scraping


Full knowledge of Data Analysis skills using Microsoft Excel and business intelligence using Microsoft Power BI.


Besides rounds that are conducted in IMP head office in Egypt; IMP courses are also delivered LIVE with fully interactive sessions allowing for a highly engaging Q & A and assuring that you get the best ever learning experience.

Terms and Conditions

Certification of Completion

To have successfully completed; a trainee should:

  • have an attendance rate of not less than 80% (or such higher attendance requirement as prescribed for the course);
  • Pass the course practical assessments and projects in at least 75% of the total number of assignments as required in each training course.

Module 1: Introduction To Data Career & Data Competencies

Differentiate your Skill as Data Scientist

Module 2 : Databases Concepts & Relational Database

Introduction to Databases Concepts & Relational Database Management

Introduction to Microsoft SQL

Core Database Concepts

Data Storage

Module 3 : Statistics for Data Science

Basic Statistics: Cases, Variables, Types of Variables

Basic Statistics : Matrix and Frequency Table

Basic Statistics : Graphs and Shapes of Distributions

Basic Statistics : Mode, Median and Mean

Basic Statistics : Range, Interquartile Range and Box Plot

Basic Statistics : Variance and Standard deviation

Basic Statistics : Basics of Regression Probability

Probability : Elementary Probability

Probability : Random Variables and Probability Distributions

Probability : Normal Distribution, Binomial Distribution & Poisson Distribution

Probability : Descriptive statistics

Probability : Population vs samples

Probability : Measures of Central Tendency & Variability

Probability : Detection of Outliers

Probability : Inferential Statistics

Probability : Observational Studies and Experiments

Probability : Sample and Population

Probability : Population Distribution, Sample Distribution and Sampling Distribution

Probability : Central Limit Theorem

Probability : Point Estimates

Probability : Confidence Intervals

Probability : Introduction to Hypothesis Testing

Module 4 : Data Analytics Using Python

Introduction to Data Analytics with Python

Python Basics

Python Basics : Basic Syntax

Python Basics : Data Types

Python Basics : Operators

Python Basics : Control flow statements

Python Basics : Decisions

Python Basics : Loops

Python Basics : Functions

Data Structures

Data Structures : List and tuples

Data Structures : Sets

Data Structures : Dictionaries

Data Structures: Strings

Files and Databases

Files and Databases : Reading from Files

Files and Databases : Writing into files

Files and Databases : Database connections

Anaconda and Jupyter Notebook

Anaconda and Jupyter :Notebook Introduction to Anaconda Distribution

Introduction to the Jupyter Notebook : Introduction to the Jupyter Notebook

Anaconda and Jupyter Notebook : Introduction to Regex Regular Expression

Working with Matplotlib

Working with NumPy

Working with Pandas

Visualization and plotting

Web Scraping with Python

Module 5 : Data-Driven Decision-Making Using Power BI

Advanced Data Transformation

Advanced Data Modeling

Working with DAX in depth

Working with M language in depth

Working With Visuals in depth

Artificial Intelligence Visuals

Level: Beginner & Advanced
60 Hours
Course Type: Online Interactive
Language: Arabic
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