Data Analysis Boot Camp Using Excel and Power BI For Associates


Program Overview

Data analytics is a rapidly growing field with high job demand across a variety of industries. With the increasing reliance on data-driven decision-making, companies are seeking professionals who can analyze complex data sets and provide insights to improve business operations. 

Data-related jobs have seen a significant increase in demand over the years, and this trend is expected to continue in the coming years. According to the World Economic Forum, it is estimated that there will be a growth of 65% in data-related jobs by the year 2025.


What will you learn in this track? 


  • The data analysis project phases and the deliverables from each milestone. 

  • Data Cleaning and transformations using Excel classical model and the Power Query advanced model.

  • The Basics of Data Visualization using Excel charts.

  • Running An exploratory Data Analysis (EDA) using the statistical functions and charts in Excel. 

  • The basics of Dimensional Data Modeling in Advanced Data Model of Excel. 

  • The art of Data storytelling. 

  • An introduction to writing DAX formulas in the Advanced Excel Data Model. 

Course Outline
Who Should Attend ?
Course Prerequisites
Learning Methodology
Course Duration
Course Outline

Program Agenda

The following proposed agenda is subject to changes by the client for the ultimate benefit of improving the training ROI of delegates and the client organization. The schedule duration can also be lengthened if the client wishes to enable delegates to achieve an even deeper understanding.

Sessions Outline:

Session 1:

    1. Data literacy concepts and principles. 
    2.  Review the basic of Excel Data ranges versus Data Tables in Excel.
    3.  The basics of Dimensional analysis in Excel.
    4.   Charts and visualization in Excel.
    5.    Perform an initial Exploratory Data Analysis (EDA) in Excel.       

Session 2:

    1. The principles of Data Cleaning with Excel. 
    2. Dealing with Time and Dates using Time Intelligence Feature in Excel. 
    3. Limitations of Excel Classical Model. 
    4. Introduction to Power Query 
    5. Ingesting Excel files into a Power Query. 
    6. Ingesting CSV files into Power Query. 
    7. Reading dataset from a folder using Power Query. 

Session 3:

    1. Data Cleaning and transformation using Power Query.  
    2. Appending multiple datasets into a single one using Power Query. 
    3. Merging Multiple datasets into a single one using Power Query.
    4. Performing Advanced data combination using Power Query. 
Who Should Attend ?

Who Should Attend?

The course is suitable for:

  • Entry and mid-level staff working in the Sales, Marketing, Finance, and HR departments. 
  • Process improvement engineers. 
  • Quality Assurance professionals. 
  • entry-level persons who are determined to pursue data science related jobs.
Course Prerequisites

Course Prerequisites

  • Basic Knowledge of Computers. 
  • Basic Algebra and statistics. 
  • Basics of Microsoft Excel. 
  • A device with a processor Core i5 or higher with 8 GB of RAM at least.
Learning Methodology

Learning Methodology

The learning methodology is highly interactive, consisting of a balanced mix of short presentations,  the Course Director’s extensive experience in this specialized field, handouts containing key articles and topic support, dialogue about leading practice, delegates’ own ideas, experiences and Q&A, sharing of knowledge, discussions, feedback, scenarios, case studies, practical examples, individual and group exercises designed to assist the participants to gain a working knowledge of the topics taught and to embed the skills.

Course Duration

Course Duration: 

18 Hours

Session 4:

    1. Introduction to the Calculated field using DAX. 
    2. Introduction to measures using DAX.
    3. Building a Calendar Dimension Table using DAX. 
    4. DAX String Functions. 
    5. DAX Numeric Functions. 
    6. DAX Date/Time Functions.

Session 5:

    1. Data Modeling in DAX. 
    2. Performance Optimization in DAX. 
    3. Implementing Financial Metrics in DAX. 

Session 6 (Capstone Project) :

    1. Real-world dataset 
    2. designing an efficient data pipeline to connect to the data source. 
    3. Performing Data Transformation and Cleaning. 
    4. Select the appropriate data model. 
    5. Writing the needed DAX formulas to implement the measures. 
    6. Wireframing an initial dashboard layout. 
    7. Implementing the dashboard design.
    8. Performance optimization.

Required Tools:

    1. Microsoft Excel 365 or at least Office 2016.