Course description

Today, static reports and spreadsheets are no longer sufficient to meet modern business demands. 80% of companies worldwide are turning to Business Intelligence (BI) tools for data analysis and real-time decision-making with Power BI leading the way.

Based on this need, the Data Analysis with Power BI course has been designed as more than just a technical training  it's a complete learning experience that takes you from beginner to professional.
The journey starts with learning how to import data from multiple sources, then cleaning and analyzing it accurately using Power BI's advanced tools, and finally creating interactive dashboards and dynamic reports that support decision-makers with timely insights.
This course is not just a gateway to understanding data but a crucial first step toward a successful career in data analysis and business intelligence.

 General Objectives of the Data Analysis with Power BI Course

This course aims to equip you with essential and practical skills to understand data and transform it into actionable insights. 

objectives include:

•  Gaining a comprehensive understanding of Business Intelligence using Power BI and converting data into insights.

•  Importing data from multiple sources such as Excel, databases, and the web.

•  Cleaning and merging data efficiently using Power Query.

•  Creating relationships between tables to build a connected data model for advanced analysis.

•  Analyzing data using interactive charts to discover trends and patterns.

•  Designing tree charts and horizontal data analysis for displaying relationships and hierarchy.

•  Building dynamic reports with filters and interactive elements to navigate the data.

•  Creating professional dashboards that simplify data and support decision-making.

• Applying skills through real-world projects simulating actual work challenges.
  
  

Who Can Benefit from Data Analysis with Power BI Course

This course is for anyone looking to improve their decision-making by understanding and analyzing data professionally, including:

• Students and graduates in technology, statistics, and business fields

• Professionals in marketing, sales, and finance roles Individuals interested in data analysis and business intelligence

•  Entrepreneurs and small business owners

•  Anyone seeking to develop Power BI skills

•  Professionals required to prepare reports and dashboards.

  Main Course Modules

  This course focuses on key areas that build a solid foundation in data analysis, including:

  1. Importing Data from Excel and Creating Charts:
    Learn how to import data from sources like Excel and transform it into interactive visuals that help identify trends and patterns.
  2. Creating Reports and Adding Filters:
    Learn how to design dynamic reports that allow flexible data navigation using filters and interactive elements.
  3. Horizontal Data Analysis and Tree Charts:
    Understand how to handle horizontally structured data and visualize it clearly using tree charts for hierarchical insights.

  4. Merging Multiple Tables using Power Query:
    Master Power Query for cleaning and merging data from multiple sources into a single organized table ready for analysis.
  5. Creating Relationships Between Tables:
    Build relationships between tables to form a unified data model that supports multi-angle analysis without manual merging.
  6. Designing Dashboards and Dynamic Reports:
    Create interactive dashboards with multiple charts and visuals to transform raw data into actionable insights for strategic decisions.
  

  Frequently Asked Questions

  What is Data Analysis, and how does it support data-driven decision making?

  Data Analysis is the process of inspecting, cleaning, transforming, and modeling data to extract data insights. It helps organizations make data-driven decisions by identifying patterns, trends, and relationships that guide strategy and problem-solving.

  
  

  What is EDA (Exploratory Data Analysis), and why is it important?

  EDA is the process of exploring datasets using statistical analysis and data visualization tools to understand patterns, distributions, and anomalies before applying machine learning models. It ensures that the data is suitable for analysis and guides the selection of methods.

  How are the Pandas Library and the NumPy Library used in Python for Data Science?

  Pandas: Provides data structures like DataFrames and Series for efficient data manipulation, cleaning, and analysis.

  NumPy: Supports fast mathematical computations with arrays and matrices, making it essential for data processing and numerical operations.

  How does SQL complement Python for Data Science?

  SQL is used to query and retrieve data from relational databases.Combined with Python’s analytical libraries, it enables analysts to perform data processing, transformation, and analysis on large datasets efficiently.

  What is the importance of Data Transformation in data analysis?

  Data Transformation converts raw data into a structured format suitable for statistical analysis and machine learning. This includes normalization, scaling, encoding categorical data, and feature engineering.
  

  Key Terms and Topics in Data Analysis with Power BI

  Here are the essential terms you'll encounter throughout the course:

  • Importing Data from Excel

  •  Creating Simple Charts

  •  Building Reports in Power BI

  •  Adding Filters to Data

  •  Working with Horizontal Data

  •  Creating Tree Charts

  •  Combining Multiple Tables using Power Query

  •  Data Transformation and Cleaning

  •  Creating Relationships Between Tables

  •  Building Data Models.

    
    

What will i learn?

• Master Power BI for importing, cleaning, and analyzing data
• Transform raw data into actionable insights
• Design professional, interactive reports and dashboards
• Acquire skills in identifying trends and analyzing patterns
• Support decision-making using clear data visualizations
• Develop hands-on experience through realistic projects and scenarios
• Build confidence in handling data in various professional environments

Frequently asked question