# Tutorials#

## 1. Getting started with Lightkurve#

The first set of tutorials covers the basics of using Lightkurve. This includes getting to grips with the basic Lightkurve objects and how to load and work with data products from the Kepler, K2, and TESS missions. For a complete listing of all classes and methods, please consult the API docs.

### 1.1. Lightkurve objects#

### 1.2. Working with Kepler & TESS data products#

## 2. Creating and correcting light curves#

The second section focuses on the various ways in which light curves
can be extracted from the pixel data, and on the removal of instrument noise
(often referred to as *systematics*) from those light curves.

### 2.1. Creating light curves#

### 2.2. Identifying instrumental noise#

- Instrumental Noise in
*Kepler*and*K2*#1: Data Gaps and Quality Flags - Instrumental Noise in
*Kepler*and*K2*#2: Spurious Signals and Time Sampling Effects - Instrumental Noise in
*Kepler*and*K2*#3: Seasonal and Detector Effects - Instrumental Noise in
*Kepler*and*K2*#4: Electronic Noise - How to identify time-variable background noise (“rolling bands”)?
- How to load and use Cotrending Basis Vectors for Kepler, K2 and TESS

### 2.3. Removing instrumental noise#

- Removing noise from Kepler, K2, and TESS light curves using Cotrending Basis Vectors (
`CBVCorrector`

) - Removing scattered light from
*TESS*light curves using linear regression (`RegressionCorrector`

) - Removing noise from
*K2*and*TESS*light curves using Pixel Level Decorrelation (`PLDCorrector`

) - Removing noise from K2 light curves using Self Flat Fielding (
`SFFCorrector`

)

## 3. Science examples#

In the final section we demonstrate scientific data analysis tasks which astronomers commonly apply to time series data.