# Coursebooks

## Statistics for data science

#### Lecturer(s) :

Olhede Sofia Charlotta

English

#### Summary

Statistics lies at the foundation of data science, providing a unifying theoretical and methodological backbone for the diverse tasks enountered in this emerging field. This course rigorously develops the key notions and methods of statistics, with an emphasis on concepts rather than techniques.

#### Keywords

Data science, inference, likelihood, regression, regularisation, statistics.

#### Learning Prerequisites

##### Required courses

Real analysis, linear algebra, probability.

##### Recommended courses

A first course in statistics.

##### Important concepts to start the course

Students taking the course will need a solid grasp of notions from analysis (limits, sequences, series, continuity, differential/integral calculus) and linear algebra (linear subspaces, bases, dimension, eigendecompositions, etc). Though the course will cover a rapid review of probability, a first encounter with the subject is necessary (random variables, distributions/densities, independence, conditional probability). Familiarity with introductory level notions of statistics would be highly beneficial but not necessary.

#### Learning Outcomes

By the end of the course, the student must be able to:
• Derive properties of fundamental statistical procedures
• Estimate model parameters from empirical observations
• Test hypotheses related to the structural characteristics of a model
• Construct confidence bounds for model parameters and predictions
• Contrast competing models in terms of fit and parsimony

#### Assessment methods

Final exam.

Dans le cas de l¿art. 3 al. 5 du Règlement de section, l¿enseignant décide de la forme de l¿examen qu¿il communique aux étudiants concernés.

#### Resources

##### Bibliography

Davison, A.C. (2003). Statistical Models, Cambridge.

Panaretos, V.M. (2016). Statistics for Mathematicians. Birkhäuser.

Wasserman, L. (2004). All of Statistics. Springer.

Friedman, J., Hastie, T. and Tibshirani, R. (2010). Elements of Statistical Learning. Springer

### In the programs

• Computational science and Engineering, 2019-2020, Master semester 1
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Computational science and Engineering, 2019-2020, Master semester 3
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Communication Systems - master program, 2019-2020, Master semester 1
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Communication Systems - master program, 2019-2020, Master semester 3
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Data Science, 2019-2020, Master semester 1
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Data Science, 2019-2020, Master semester 3
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Electrical and Electronics Engineering, 2019-2020, Master semester 1
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Electrical and Electronics Engineering, 2019-2020, Master semester 3
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Management, Technology and Entrepreneurship, 2019-2020, Master semester 1
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Management, Technology and Entrepreneurship, 2019-2020, Master semester 3
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Data science minor, 2019-2020, Autumn semester
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks
• Electrical Engineering (edoc), 2019-2020
• Semester
Fall
• Exam form
Written
• Credits
6
• Subject examined
Statistics for data science
• Lecture
4 Hour(s) per week x 14 weeks
• Exercises
2 Hour(s) per week x 14 weeks

MoTuWeThFr
8-9
9-10
10-11
11-12
12-13CE3
13-14 CE1100
CE1101
14-15 CM1
15-16
16-17
17-18
18-19
19-20
20-21
21-22
Lecture
Exercise, TP
Project, other

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• Autumn semester
• Winter sessions
• Spring semester
• Summer sessions
• Lecture in French
• Lecture in English
• Lecture in German