Business Analytics

This Learning Path delves into data analysis and modeling, focusing on Filter and Row Contexts for data computations, and the use of functions like SUM, SUMX, and Calculate.

This path is designed to build the broad range of expert-level knowledge necessary to confidently pass the PL-300 Data Analyst certification exam (replacement for DA-100).

This Learning Path delves into data manipulation and analysis using SQL and DAX. It covers techniques and practical code examples.

This path will provide you in-depth knowledge of the full range of Power BI best practices, with particular emphasis on building data models specifically optimized for Power BI.

This Learning Path delves into data analysis techniques, focusing on Power BI functions. It guides learners to enhance their data insight extraction skills.

This path is designed to provide you the necessary skills to build full enterprise apps by integrating Power BI , Power Automate and Power Apps. 

This path is designed to help you harness the full power of Power BI in combination with R and Python. You will learn how to conduct advanced analyses and gain insight into complex business questions.

This path is focused on developing expert level skills in data cleaning, data transformation and data modeling. You will learn to maximize the capabilities of the Power Query UI, and also write M code.

Learn how to predict and optimize for results in the future, by utilizing 'what if' parameters and scenario analysis techniques

Disclose the unexpected' by expertly incorporating geospatial data into your analyses and visualizations

Master the ultimate tool for executing and analyzing DAX queries against various sources including Power BI and SSAS

Unlock the power of data analysis and visualization with PandasAI, and transform raw data into actionable insights that drive decision-making and growth.

Learn how to transfer your knowledge of SQL to DAX and vice versa

Build a strong foundation on your MS Excel skills as you progress to advanced level

Create comprehensive financial reports in Power BI that represent key insights in a compelling and dynamic way

Learn how to develop complete Power BI solutions that look compelling and deeply engage your consumers in your insights

Sam McKay talks to Paul Lucassen, one of our Enterprise DNA experts, about the role of geospatial data analytics in the transportation and logistics industry

The session include function Let and how to declare and using parameters in Power Query and DAX

Learn the core areas of the winning entry around the 4 pillars of Power BI report development

Join a casual conversation with Sam and the Power BI Challenge Winner, Greg.

Discover how to visualize, debug, validate and solve complex DAX problems using Excel

Check the Replays of the Feb 2022 Analytics Summit Event for Analysts, Excel Users, Database Professionals, Data Enthusiasts and more.

Inventory insights deep dive

Learn the 10 different elements in building a data-oriented career

Show off your impressive financial report! We are confident that your financial report is a testament to your skills and expertise. We would love to see how you've structured it and how you've made it visually appealing.

Practice your data skills and learn from what is a typical scenario faced by many businesses in various industries

Identify trends and patterns in a variety of factors that influence the cost of living in different regions around the world by using statistical analysis techniques and visualization tools.

Use the data documenter tool to create an effective report template 

Create a summary report, a task that for many data analysts will always find its way across the desk every so often

Provide a business solution by creating an intelligence tool to use by large opticians

Enhance your development skills by creating a supplier insights report

Showcase a report that compares the trends of two different sets of data over time. The report will specifically highlight any divergences in the trends, allowing the reader to easily see any discrepancies in the data.