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

Identifying time series analysis types

Time series variation

Analyzing birth rates

Analyzing influenza

Basics of time series data transformation

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

Options

Expanding Window Functions

Rolling Window Functions

Identifying window functions

Average population

Segment performance

Rolling average sales

Rolling total quantity

Applications of window functions

Year over Year Calculation

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

Información financiera en Power BI

Total number of employees hired since the company started

Average hours worked per week since the merger

Total benefits paid to employees before the merger.

Total number of employees hired in the last three months.

Average signing bonus paid in the last year.

Average employee churn in the last six months.

Identifying window functions

Time Series Analysis in Power BI

Ejercicio interactivo práctico