Business Analytics

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 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 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.

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 help you build expert level skills in the implementation, monitoring and governance of Power BI, including making optimal use of the Service through dataflows, datasets, thin reports, etc.

This path is the most in-depth and rigorous on the list, designed to provide you expert-level skills in all of the key pillars of Power BI. 

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

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

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

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

Implement advanced & practical analytical techniques inside of Power BI and find insights you never thought possible in an intuitive way

Learn all about DAX calculations in Power BI. Learn introductory theory as well as how to put it into practice when developing Power BI models. 

This module discusses the different features of the Analyst Hub platform and the tips to maximize the use of tools to enhance Power BI development.

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

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

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

Implement some creative Row-Level-Security techniques effectively and learn how to dynamically anonymize data

Solve advanced analytical questions using Power BI

Implement selected design techniques and chat formatting options to enhance overall user experience (UX)

Supply chain management involves the flow of goods and services.

Learn how usability testing can reveal crucial usability information not revealed by A/B testing, focus groups, or surveys

Power BI Freestyle Session w/ Sam McKay

Streamline business processes through automation with no code

Analyze the data of a large insurance company who are due to undergo an internal audit with regard to its complaint’s procedure

Use the data documenter tool to create an effective report template 

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.

An opportunity to design and build a report that gives insights into customer behavior

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

Analyze data from marketing campaigns and initiatives, using visualization tools and statistical analysis techniques to identify trends and patterns, and use these insights to inform marketing strategy and decision-making.

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.