Data Science Diploma

Home / Course / Data Science Diploma

DIPLOMA OVERVIEW

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 DIPLOMA ?

  • 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 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
  • Module 5
    • Advance your Skill in Data Transformation & Data Modeling
    • Using Statistical& Math DAX
    • Develop Power BI Visual
    • Connect to SQL Or Any Database
    • Design & Implement Enterprise Dashboard

PREREQUISITES: 

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

ROUNDS

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.

Start DateTypeDaysTimeAvailable Seats
30-Dec-2022Interactive Online TrainingFriday1:00 PM-5:00 PMLimited
28-Jan-2023Classroom onlySaturday6:00 PM-10:00 PMLimited

Terms and Conditions

CERTIFICATE 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: advanced
60 Hours