Project Based Master Workshop for Data Analytics Course Overview

Course Prerequisites

• Basic Computer Knowledge.

Target Audience for Project Based Master Workshop for Data Analytics

1. Introduction: The Project Based Master Workshop for Data Analytics offers comprehensive training with 15 real-time projects in Power Excel, Python, Power Query, and SQL, perfect for aspiring data professionals.

2. Job Roles and Target Audience:

• Data Analysts
• Data Scientists
• Business Intelligence Analysts
• Financial Analysts
• Marketing Analysts
• HR Analysts
• Sales Analysts
• IT Professionals
• Data Engineers
• Project Managers
• Business Consultants
• Statisticians
• Market Researchers
• Academic Researchers
• Data Enthusiasts
• Graduate Students in Data Science or Computer Science
• Professionals looking to transition into data analytics

Learning Objectives - What you will Learn in this Project Based Master Workshop for Data Analytics?

Introduction:

The Project Based Master Workshop for Data Analytics is a comprehensive 48-hour course designed to provide hands-on experience through 15 real-time data projects. It covers key areas in Power Excel, Python Data Analysis, Power Query, and SQL.

Learning Objectives and Outcomes:

• Power Excel:

  • Master data cleaning, transformation, and analysis.
  • Connect and manage different data sources.
  • Append and merge datasets effectively.
  • Develop dynamic and interactive dashboards.

• Python Data Analysis:

  • Understand fundamental Python programming concepts.
  • Work with Python data structures, dates, and strings.
  • Perform data extraction and cleaning using Pandas.
  • Visualize and analyze data using Matplotlib and Seaborn.

• Power Query & Analysis:

  • Conduct comprehensive data cleaning and transformations.
  • Work with multiple queries and data models.
  • Create and format detailed reports using DAX for insights.
  • Design and implement dashboards with Power BI Service.

• SQL:

  • Understand RDBMS concepts and master SQL commands (DDL, DCL, and DML).
  • Write efficient queries using filters, sorting, and grouping.
  • Execute complex joins and subqueries.
  • Build robust data