Datacamp course https://app.datacamp.com/learn/skill-tracks/time-series-with-python.

Time series data is one of the most common data types and understanding how to work with it is a critical data science skill if you want to make predictions and report on trends. In this track, you'll learn how to manipulate time series data using pandas, work with statistical libraries including NumPy and statsmodels to analyze data, and develop your visualization skills using Matplotlib, SciPy, and seaborn. You'll then apply your time series skills using real-world data, including financial stock data, UFO sightings, CO2 levels in Maui, monthly candy production in the US, and heartbeat sounds. By the end of this track, you'll know how to forecast the future using ARIMA class models.

This training consists of the following chapters:

1. Manipulating Time Series Data in Python: In this course we'll learn the basics of manipulating time series data. Time series data are data that are indexed by a sequence of dates or times. We'll learn how to use methods built into Pandas to work with this index. We'll also learn how resample time series to change the frequency. This course will also show us how to calculate rolling and cumulative values for times series. Finally, we'll use all your new skills to build a value-weighted stock index from actual stock data.
2. Time Series Analysis in Python: From stock prices to climate data, time series data are found in a wide variety of domains, and being able to effectively work with such data is an increasingly important skill for data scientists. This course will introduce us to time series analysis in Python. After learning about what a time series is, we'll learn about several time series models ranging from autoregressive and moving average models to cointegration models. Along the way, we'll learn how to estimate, forecast, and simulate these models using statistical libraries in Python. We'll see numerous examples of how these models are used, with a particular emphasis on applications in finance.
3. Visualizing Time Series Data in Python: 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.
4. ARIMA Models in Python: Have you ever tried to predict the future? What lies ahead is a mystery which is usually only solved by waiting. In this course, we will stop waiting and learn to use the powerful ARIMA class models to forecast the future. We will learn how to use the statsmodels package to analyze time series, to build tailored models, and to forecast under uncertainty. How will the stock market move in the next 24 hours? How will the levels of CO2 change in the next decade? How many earthquakes will there be next year? We will learn to solve all these problems and more.
5. Machine Learning for Time Series Data in Python: Time series data is ubiquitous. Whether it be stock market fluctuations, sensor data recording climate change, or activity in the brain, any signal that changes over time can be described as a time series. Machine learning has emerged as a powerful method for leveraging complexity in data in order to generate predictions and insights into the problem one is trying to solve. This course is an intersection between these two worlds of machine learning and time series data, and covers feature engineering, spectograms, and other advanced techniques in order to classify heartbeat sounds and predict stock prices.