In this first chapter, you will prepare and explore your new dataset in Power BI, to set things up for your inventory analysis. Of course, as with a real-world dataset, things might not be perfect, so you should keep your eyes open to check your data is in order and makes sense.

Inventory Analysis with Power BI

Power BI recap

Preparing data

Cleaning and exploring data

Cost of Goods Sold

Revenue and profit

Insight of inventory analytics

Exploratory Analysis

Here, you will continue developing more interesting metrics and methods for inventory analysis in Power BI, such as inventory turnover and ABC analysis, to help you investigate which items are best for WarmeHands Inc.

Analysis for Inventory Control and Management

Identifying tools and metrics

Sales by year

Inventory turnover

Contrasting revenue

ABC analysis

Preliminary results

Reviewing results

Inventory Analysis

In the final chapter, it's time to start creating dashboard-style pages to share information with suitable applications on management practices, such as assisting in purchase decisions to improve stock management efficiently.

Reporting analytics

Dashboard objective

Smart purchasing

Inspecting categories

Control decisions

Management solutions

Item recommendations

Wrap-up

Reporting inventory analytics

DataCamp vs. Local Experience

Exercises and Datasets

Metadata sheet

Looking for a way to challenge what you have learned in Power BI? This case study can help you be prepared to tackle real-world problems in the field of inventory analysis.
You’ll analyze a dataset from a wonderful fictitious company named WarmeHands Inc. As with many retailers, they want to investigate possible improvements in inventory management and purchases, but they need some help. Here is where you come to the rescue. But as you will see, in a real-world case, not everything is perfect.
You will start using some Power Query for cleaning and exploring the data to verify everything makes sense. Next, you’ll work with some manipulations and intermediate DAX formulas for analyzing the clean information to obtain focused insights. Finally, you’ll finish by creating some attractive yet informative report pages.
By the time you’re finished, you’ll be more confident performing an inventory analysis using Power BI.


<h2>Apply Your Skills with a Power BI Case Study</h2>
Looking for a way to challenge what you have learned in Power BI? This case study can help you be prepared to tackle real-world problems in the field of inventory analysis.
You’ll analyze a dataset from a wonderful fictitious company named WarmeHands Inc. 
<br><br>
<h2>Deep-dive into Inventory Management </h2>
As with many retailers, they want to investigate possible improvements in inventory management and purchases, but they need some help. Here is where you come to the rescue. But as you will see, in a real-world case, not everything is perfect.
<br><br>
<h2>Utilize Power Query for Data Cleaning</h2>
You will start using some Power Query for cleaning and exploring the data to verify everything makes sense. Next, you’ll work with some manipulations and intermediate DAX formulas for analyzing the clean information to obtain focused insights. Finally, you’ll finish by creating some attractive yet informative report pages.
<br><br>
By the time you’re finished, you’ll be more confident performing an inventory analysis using Power BI.


Introduction to Power BI

Data Preparation in Power BI

DAX Functions in Power BI

This Power BI case study follows a real-world business use case on tackling inventory analysis using DAX and visualizations. 

Case Study: Inventory Analysis in Power BI

This Power BI case study follows a real-world business use case on tackling inventory analysis using DAX and visualizations. 


Statistician and Data Scientist

Analysis progression

Sharing dashboards

Further challenges

Congratulations!

https://s3.amazonaws.com/assets.datacamp.com/production/course_29868/subtitles/course_29868_4aa58d259f3c5557c091875abadcb4b9.vtt

Reporting inventory analytics - Wrap-up

Wrap-up

1. Wrap-up

2. Analysis progression

3. Sharing dashboards

4. Further challenges

5. Congratulations!

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