Data Science in Layman's Terms: Time Series Analysis

Modeling Time Series Data

What you'll learn
Time series forecasting with modern nonlinear models, neural networks, and AI
Time series classification, with a project on predicting heart attackes from ECG data
Time series segmentation, with a project categorizing distinct periods of football QB performance
Signal processing, with a project detecting gravitational waves hidden amongst noise
Anomaly detection, with a project detecting faulty inverters at solar power plants
Geospatial-temporal analysis, with a project creating a dashboard to analyze crime in San Francisco
How to build a dashboard with Dash and Plotly
How to deploy machine learning as a service (MLaaS), using an API
How to generate music with AI
How to build & utilize custom neural networks for time series, including LSTMs and Transformers

Description
This course explores a specific domain of data science: time series analysis. The lectures explain topics in time series from a high level perspective, so that you can get a logical understanding of the concepts without getting intimidated by the math or programming. Whether you are new to time series or an experienced data scientist, this course covers every aspect of time series. Topics in time series analysis include:

Forecasting - Predicting the future

Classification - Categorize a series

Segmentation - Breaking a series into periods of distinct characteristics

Anomaly Detection - Identifying unexpected observations

Signal Processing - Extracting signal from noise

Geospatial-Temporal Analysis - Analyzing time series with a location component

The later half of the course entails several projects for you to get your hands dirty with time series analysis in Python. You will learn about modern time series forecasting models and AI, how to build them, and implement them to do extraordinary things.

Generate music with AI

Deploy a model to an API to provide machine learning as a service (MLaaS)

Build a dashboard with Dash/Plotly

Build different types of RNNs and Transformers, using TensorFlow, for time series modeling

Analyze different types of data sources, like CSV, JSON, GeoJSON, HDF5, and MIDI

By the end of this course, you will be able to handle any time series problem. You will be equipped with the knowledge to build powerful forecasting models, and be able to deploy them.