Build your ultimate AI agent
Course Description
Time series data is omnipresent in the field of Data Science. Whether it is analyzing business trends, forecasting company revenue or exploring customer behavior, every data scientist is likely to encounter time series data at some point during their work. To get you started on working with time series data, this course will provide practical knowledge on visualizing time series data using Python.
Prerequisites
Curriculum
Course outline
1
Line Plots
You will learn how to leverage basic plottings tools in Python, and how to annotate and personalize your time series plots. By the end of this chapter, you will be able to take any static dataset and produce compelling plots of your data.
- Welcome to the course!50 XP
- Load your time series data100 XP
- Test whether your data is of the correct type100 XP
- Plot your first time series50 XP
- Your first plot!100 XP
- Specify plot styles100 XP
- Display and label plots100 XP
- Customize your time series plot50 XP
- Subset time series data100 XP
- Add vertical and horizontal markers100 XP
- Add shaded regions to your plot100 XP
2
Summary Statistics and Diagnostics
In this chapter, you will gain a deeper understanding of your time series data by computing summary statistics and plotting aggregated views of your data.
3
Seasonality, Trend and Noise
You will go beyond summary statistics by learning about autocorrelation and partial autocorrelation plots. You will also learn how to automatically detect seasonality, trend and noise in your time series data.
4
Work with Multiple Time Series
In the field of Data Science, it is common to be involved in projects where multiple time series need to be studied simultaneously. In this chapter, we will show you how to plot multiple time series at once, and how to discover and describe relationships between multiple time series.
5
Case Study: Unemployment Rate
This chapter will give you a chance to practice all the concepts covered in the course. You will visualize the unemployment rate in the US from 2000 to 2010.
Visualizing Time Series Data in Python
Course
Complete
