An introduction to time series data. Learn to identify common patterns and formats of time data, and transform them to meet your needs.

Fundamentals of time series data

Options

Short-term

Long-term

Future

Identifying time series analysis types

Time series variation

Analyzing birth rates

Analyzing influenza

Basics of time series data transformation

Date Transformation Process

Order of operations

Transforming dates

Calculating shipping delays

Calculating receivables delay

Analyzing promotion effectiveness

Introduction to time series data

Learn about and create window functions within Power BI to analyze data over a set period of time.

Window functions in Power BI

Identifying window functions

Average population

Segment performance

Rolling average sales

Rolling total quantity

Applications of window functions

Steps to calculate year over year change

Analyzing five star reviews

Historical segment performance

Discount rate distribution

Customer ranking

Working with window functions 

Learn the basics of forecasting and how to predict values in the future using different methods in Power BI.

Forecasting in Power BI

Forecasting order of operations

Forecasting types of energy consumption

Setting forecast expectations

Forecasting MSFT close price

Hindcasting MSFT close price

Forecasting in Power BI: DAX

When to use CAGR

Forecasting types of energy consumption - DAX

Creating forecasting variables

Forecasting with CAGR

Forecasting with CMGR

Course wrap-up

Looking ahead with forecasting

DataCamp vs. Local Experience

Exercises and Datasets

Analyze Time-Series Data With Power BI
You've taken datasets and analyzed them from every possible angle, but did you know that there is one dimension that every analysis shares in common? That's right; it's time! Have you ever thought about how your data varies over time as well?

Practice Common Analysis Techniques
In this Time Series Analysis in Power BI course, you will work with two different datasets and practice common techniques to analyze data over time. You will start by analyzing Superstoredata for Point of Sale (PoS) data across various locations and times, where you will calculate changes with different kinds of window, period-to-date, and period-over-period functions.

Use Built-In and DAX Algorithms
Then, you will dive into financial data from Microsoft (NASDAQ: MSFT) and see how we can forecast data into the future, using both built-in and DAX algorithms to do so.

Following the course, you will have a well-rounded toolkit to tackle time series data and master the fourth dimension.


<h2>Analyze Time-Series Data With Power BI</h2>
You've taken datasets and analyzed them from every possible angle, but did you know that there is one dimension that every analysis shares in common? That's right; it's time! Have you ever thought about how your data varies over time as well?
<br><br>
<h2>Practice Common Analysis Techniques</h2>
In this Time Series Analysis in Power BI course, you will work with two different datasets and practice common techniques to analyze data over time. You will start by analyzing Superstoredata for Point of Sale (PoS) data across various locations and times, where you will calculate changes with different kinds of window, period-to-date, and period-over-period functions.
<br><br>
<h2>Use Built-In and DAX Algorithms</h2>
Then, you will dive into financial data from Microsoft (NASDAQ: MSFT) and see how we can forecast data into the future, using both built-in and DAX algorithms to do so.
<br><br>
Following the course, you will have a well-rounded toolkit to tackle time series data and master the fourth dimension.


Exploratory Data Analysis in Power BI

Intermediate DAX in Power BI

Learn to analyze data over time with this practical course on Time Series Analysis in Power BI. Work with real datasets & practice common techniques. 


Master Time Series Analysis in Power BI with Real-World Data

Time Series Analysis in Power BI

Finanzielle Berichterstattung an der Macht BI

The number of influenza cases we have seen this year to date.

The total revenue we have generated in the last sixty days.

The change in influenza cases in the fall over the last three years.

Visualizing the annual revenue over the last decade.

Forecasting the number of influenza cases in the next 30 days.

Forecasting our total revenue into the next two years.

Identifying time series analysis types

Time Series Analysis in Power BI

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