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Data Analysis Diploma

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Data Analysis Diploma

اتعلم تحاليل البيانات بطريقه افضل

1-Introduction

  • What is data analysis
  • Types of data
  • Data pipeline
  • Important data tools

2-Data Analysis using Python

✨Project: Python problems

  • Introduction to Python
  • NumPy
  • Data structures overview
  • Pandas
  • Data collection techniques
  • Matplotlib
  • Web scrapping & Scrapping tools
  • Seaborn
  • Plotly
  • Data cleaning
  • Handling different data formats
  • EDA
  • Automation using code

3-Statistics

✨Project: Analyzing Real Data with Statistics.

  • Introduction to statistics
  • Descriptive vs. Inferential statistics
  • Probability
  • Hypothesis testing
  • Statistical test

4-Database

✨Project: Applying all learned techniques to a comprehensive project - Preparing Data for Analysis - Developing and Evaluating Models - Presenting Insights and Solutions.

  • Introduction to database
  • Data Modeling
  • Types of databases
  • ERD
  • How to choose the best database for your data
  • SQL vs NoSQL database
  • SQL for data analysis
  • Mapping

5-Excel

  • Work with Excel Sheets
  • Create graphs using Excel
  • Writing Excel formulas
  • Create Excel dashboards

6-Power BI

✨Project: End-to-End Data Projects - Report Generation - Story Telling

  • Introduction to data analysis tools
  • Power BI vs Tableau
  • Create Power BI dashboard
  • Integrate Power BI with different data sources
  • Introduction to Data Engineering

7-Power Query

  • Introduction to power query
  • Preprocessing data
  • Why using power query
  • Apply data transformations
  • Power query interface
  • Introduction to M language
  • Supported data sources
  • Best practices
  • Cleaning data

8-Data Warehouse & Cloud

  • What is a Data Warehouse?
  • General Cloud Concepts : IaaS, PaaS, SaaS
  • ETL (Extract, Transform, Load)
  • Main Cloud Providers: AWS, Azure, Google Cloud (GCP)
  • ELT (Extract, Load, Transform)
  • Data Lake vs Data Warehouse vs Data Marts

9-Final Project

  • Apply your full data analysis pipeline from data collection, cleaning, exploration, and visualization to generating actionable insights on a real-world dataset.

Course Outcome & Expectations نتائج الدبلومة والتوقعات

  • أكثر من 6 مشاريع.
  • استخلاص رؤى من جميع أنواع البيانات.
Conclude Insights from all kinds of Data
  • إجراء جميع عمليات المعالجة المسبقة للبيانات اللازمة لأي مشكلة.
Perform all Data preprocessing required for a problem.
  • إنشاء مجموعات بيانات بناءً على المشكلة. وقواعد البيانات.
Generate Datasets based on the problem
  • اختيار المنهجية الأنسب لحل أي مشكلة تحسين.
Choose the best methodology to solve any optimization problem
  • إتقان الأدوات الرئيسية لعلم البيانات.
Mastering main tools of Data Science
  • ضبط النماذج وتخصيص هياكلها. Fine tune models and customize architectures
  • مقابلات تجريبية + تدريب مهني (Mock Interviews + Career Coaching)