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Advanced Analytics using Excel

This Excel Analytics course teaches learners techniques for data extraction, cleaning, and modeling. Learners master Power Query for importing and refining data and Power Pivot and DAX for building...

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Course Code WA3645
Duration 5 days
Available Formats Classroom

This Excel Analytics course teaches learners techniques for data extraction, cleaning, and modeling. Learners master Power Query for importing and refining data and Power Pivot and DAX for building and managing complex data models. This Excel course also covers creating interactive reports with charts, pivot tables, and automating tasks using VBA macros. Additionally, learners explore Excel’s AI-driven tools, enabling them to perform predictive analyses, conduct What-If scenarios, and leverage natural language processing (NLP) for enhanced decision-making. The course concludes with a half-day capstone project that allows learners to apply the skills they’ve learned in a practical scenario.

Skills Gained

  • Extract and clean data using Power Query from various sources, ensuring high-quality data.
  • Build and manage robust data models with Power Pivot and DAX, including relationships, hierarchies, and calculations.
  • Create dynamic, interactive reports using charts, pivot tables, and dynamic arrays for effective data visualization.
  • Automate repetitive tasks with VBA macros to increase productivity.
  • Utilize AI tools in Excel to perform predictive analysis, uncover trends, and enhance decision-making.

Prerequisites

To be successful in this course, learners should have:

  • Basic knowledge of Excel, including working with formulas, charts, and tables.
  • Familiarity with basic statistical and data analysis concepts is useful but not necessary.

Course Details

Setup Requirements

  • A computer with Microsoft Excel (preferably Office 365 or Excel 2019/2021).
  • Access to the internet for downloading sample datasets.

Data Extraction / Clean-up

  • Introduction to Power Query for data extraction
  • Importing data from various sources: CSV, Excel, Web, and databases
  • Techniques for cleaning data, including removing duplicates, filtering, and transforming data
  • Understanding data types and dynamic arrays in Excel
  • Using lookup functions, error handling, and data transformation with loops
  • Practical exercise: Importing and cleaning data from multiple sources

Data Modelling

  • Overview of Power Pivot for building data models
  • Importing data into Power Pivot and managing multiple data sources
  • Creating relationships, hierarchies, and applying formatting to the data model
  • Introduction to DAX: Creating calculated columns and measures
  • Exploring logical and statistical functions in DAX for advanced calculations
  • Formula linking, auditing, and troubleshooting in data models
  • Practical exercise: Building a data model with Power Pivot and DAX

Reports

  • Creating dynamic charts to visualize data insights
  • Designing pivot tables to summarize and analyze large datasets
  • Using dynamic arrays to create responsive, real-time reports
  • Introduction to VBA macros for automating repetitive tasks
  • Recording, editing, and running macros in Excel
  • Practical exercise: Automating a report generation process using VBA

Insights and AI Features

  • Conducting What-If Analysis to explore potential impacts on sales, budget, or other metrics
  • Using Excel Insights to identify trends, patterns, and anomalies in the data
  • Leveraging AI tools like Forecasting and Analysis Toolpak for predictive analysis
  • Introduction to natural language processing (NLP) in Excel 365 using Q&A features
  • Practical exercise: Using Q&A in Excel to generate insights and predictions from data
  • Exploring advanced AI tools and their applications in decision-making processes

Capstone Project (Half-Day)

  • Apply the concepts learned throughout the course to a real-world dataset
  • Create a data model, generate dynamic reports, and automate tasks using Excel’s features
  • Present your findings and demonstrate your use of AI tools, What-If Analysis, and data visualization techniques

Conclusion