## Data Science

*Under development*

## Course Description

Recently, data science has become one of the most demanding fields with applications in science, engineering, economic, and huminites. This is undergraduate (seniors) comprehensive course on data science. It covers topics from basic mathematical concepts up to practical applications. Hands on implementation using Python (mainly) and R languages are the main core of the labs. This course is designed for science/engineering majors (essential background is required) but can be adjusted for other majors as well.

## Course outline:

**Lecture#01:**Introduction to data science and applications

**Lecture#02:**What is R language

**Lecture#03:**Introduction to Python

**Lecture#04:**Start with Mathematics: Linear algebra, probabilities, Bayesian theory.

**Lecture#05:**Data acquisition, analysis and visualization

**Lecture#06:**Principal Component Analysis and Support Vector Machine

**Lecture#07:**Clustering and Regression

**Lecture#08:**Big data analysis and understanding

**Lecture#09:**Deep learning and GANs

**Lecture#10:**Application project (I)

**Lecture#11:**Application project (II)

**Lecture#12:**Review

## Reference Books

- Bruce, P. and Bruce, A. "Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python", O'Reilly Media, 2020
- Kotu, V. and Deshpande, B. "Data Science: Concepts and Practice", 2nd Ed. Morgan Kaufmann, 2018
- Gerstman, B. Burt. "Basic Biostatistics: Statistics for Public Health Practice", Jones & Bartlett Learning 2014

## Other materials

- Become a data scientists in 8 steps by DataCamp.
- Data science tutorial by javaTpoint
- Python (download)
- R (download)