STATISTICAL LEARNING FOR DATA SCIENCE - 1 - PRACTICE

The objective of the course is to develop statistical skills for the analysis of high dimensional
data and solve forecasting and classification problems occurring in a wide variety fields including business, economics and technology.

Regular and active participation in the teaching activities offered by the course and in
independent research activities will enable students to:
1. (knowledge and understanding)
- acquire knowledge and understanding regarding advanced statistical learning methods for synthesis, prediction and classification using data also in presence of complex structures and high-dimensionality
2. (applying knowledge and understanding)
- pre-process a dataset and prepare it for further analysis
- apply autonomously advanced statistical methods for synthesizing information, make predictions and classifications using high-dimensional data
- apply autonomously statistical software for the analysis of high-dimensional data
3. (making judgements)
- make autonomous judgements about the validity and feasibility of different statistical techniques and understand the effects of these on the outcomes of the analyses
- present the results in a clear and concise manner, using tools for reproducible reports and
research

The course will make use of basic mathematical and statistical concepts such as functions, integrals, derivatives, matrices, distributions, estimation and hypothesis testing. Students
are expected to possess knowledge of statistics at STAT-100 level.

The course is divided into two parts. The first part focuses on introducing tools for
reproducible research. Such tools will be applied in the second part of the course about statistical learning.



Tools for data science and reproducible research
- Introduction to R and Rstudio
- Writing reports using Rmarkdown

Data visualization, data wrangling, data tyding

Statistical Inference
- Sampling
- Estimation
- Hypothesis testing

Statistical learning
- Linear regression
- Classification
- Resampling methods
- Linear model selection and regularization
- Nonlinear models

James G, Witten D, Hastie T, Tibshirani R (2015). An Introduction to Statistical Learning. 6th
version. Springer. Webpage http://www-bcf.usc.edu/~gareth/ISL/ Chapters 1-7
Chester Ismay, Albert Y. Kim (2019) Statistical Inference via Data Science: A ModernDive
into R and the tidyverse! , CRC Press ( https://moderndive.com/ )
Yihui Xie (2019) bookdown: Authoring Books and Technical Documents with R Markdown,
CRC/Press ( https://bookdown.org/yihui/bookdown/ )
Additional reading and materials will be distributed during the course through Moodle.

The achievement of the course objectives is assessed through a final written exam.

The final written exam consists of three or four exercises designed to measure
1. the theoretical knowledge of the course topics,
2. the ability to apply them for solving real data problems.
The final score is the sum of the scores of the exercises.    During the written test the use of books, notes, or electronic media is *not* allowed.

A total score exceeding 30 corresponds to 30 with honors.

The course consists of a combination of conventional theoretical classes focused on
description of methods and practice sessions describing the implementation and application
of the methods to real problems. Methods will be implemented with the statistical language R
( www.r-project.org ). Students are encouraged to bring their own laptops (not tablets!) and to experiment
with the code during the course.   

English

This is the first module of a 12 credit course.
The information refers to the whole course.

Students should register in the related course web page of the university e-learning platform moodle.unive.it

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