# Statistical Data Analysis: Application to Atmospheric Science

- Institution: IIT Mandi
- Department: Multidisciplinary
- Teacher: Prof. Sarita Azad
- Level: Undergraduate
- Language: English
- Subjects: Mathematics, Earth and environment
- Source: [NPTEL](https://nptel.ac.in/courses/127106011)
- Licence: [CC BY-NC-SA](https://nptel.ac.in/faq) (Non-commercial use only.)
- Syllabus: [at the source](https://nptel.ac.in/courses/127106011)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Statistical Data Analysis: Applications to Atmospheric Science provides a comprehensive introduction to data analytics through theory and hands-on practice. The course covers data classification, descriptive and inferential statistics, and exploratory data analysis.

Learners will develop skills in visualization, trend analysis, stochastic modeling, and forecasting techniques. It also introduces multivariate methods such as regression, clustering, Principal Component Analysis (PCA), and Self-Organizing Maps (SOM/Kohonen Maps).

Every concept is supported through practical implementation using Python code provided to learners. Although the examples are drawn primarily from climate and atmospheric datasets, the methods are broadly applicable across disciplines.

Intended audience: UG/PG/PhD all disciplines

## Lectures

1. [Introduction to Data Analytics -I](https://invisiblenetwork.ai/learn/nptel/127106011/ac579134c599) (YouTube)
2. [Introduction to Data Analytics -II](https://invisiblenetwork.ai/learn/nptel/127106011/1d9d9efc792d) (YouTube)
3. [Introduction to Data Analytics -III](https://invisiblenetwork.ai/learn/nptel/127106011/4fcb50e658c4) (YouTube)
4. [Introduction to Data Analytics- IV](https://invisiblenetwork.ai/learn/nptel/127106011/74cef23af575) (YouTube)
5. [Statistical Data Analysis- Case study on Himalayan climate data](https://invisiblenetwork.ai/learn/nptel/127106011/d9744dd02890) (YouTube)
6. [Introduction to Statistics](https://invisiblenetwork.ai/learn/nptel/127106011/93b282cce056) (YouTube)
7. [Fundamentals of Probability DIstribution](https://invisiblenetwork.ai/learn/nptel/127106011/30f07a30ed01) (YouTube)
8. [Probability Distributions for Time Series Prediction-I](https://invisiblenetwork.ai/learn/nptel/127106011/0177bf5206d3) (YouTube)
9. [Probability Distributions for Time Series Prediction-II](https://invisiblenetwork.ai/learn/nptel/127106011/aac2cea94196) (YouTube)
10. [Statistical Data Analysis- Case study on Himalayan Pollution Data](https://invisiblenetwork.ai/learn/nptel/127106011/e12cc2df030b) (YouTube)
11. [Time Series Forecasting-I](https://invisiblenetwork.ai/learn/nptel/127106011/df800e22dea8) (YouTube)
12. [Time Series Forecasting -II](https://invisiblenetwork.ai/learn/nptel/127106011/8bf856796e0a) (YouTube)
13. [Time Series Forecasting-III](https://invisiblenetwork.ai/learn/nptel/127106011/e481ea68631a) (YouTube)
14. [Time Series Forecasting- IV](https://invisiblenetwork.ai/learn/nptel/127106011/17bc8e7e5698) (YouTube)
15. [Case study: decline of SO4 and its role in Himalayan warming](https://invisiblenetwork.ai/learn/nptel/127106011/99fbd135f3f1) (YouTube)
16. [K-means Clustering](https://invisiblenetwork.ai/learn/nptel/127106011/7cc664c2d13c) (YouTube)
17. [K-means clustering cont.](https://invisiblenetwork.ai/learn/nptel/127106011/f3fe8fcacc09) (YouTube)
18. [Introduction to PCA](https://invisiblenetwork.ai/learn/nptel/127106011/f5dc38a84857) (YouTube)
19. [Self-organizing Maps](https://invisiblenetwork.ai/learn/nptel/127106011/0fd8eda3bd10) (YouTube)
20. [Self-organizing Maps Cont.](https://invisiblenetwork.ai/learn/nptel/127106011/e94bf12f63d8) (YouTube)