Probability for Data Science
36,00 €
The course will first focus on probability distributions, covering measures of central tendency (mean, median, and mode), variance, skewness, kurtosis, and the concept of expected value. Visual tools for data description and distribution visualization will also be introduced. In a second part, we will delve into the probability distribution of multivariate random variables, exploring joint distribution, marginal distribution, conditional distribution, and covariance.
12 in stock
Start date
June 24, 2024
End date
June 27, 2024
Language(s) of the training
English
Languages spoken by the coach(es)
English, German, Luxembourgish, French
Instructor(s)
Prof. Dr. Ivan Nourdin, Prof. Dr. Christophe Ley, Prof. Dr. Stéphane Bordas
Contents
During this course, we will:
- Provide a comprehensive understanding of statistical measures to describe probability distributions and datasets.
- Introduce measures of central tendency, variance, skewness, and kurtosis, alongside the concept of expected value.
- Familiarize participants with visual tools for data description and distribution visualization.
- Explore the probability distribution of multiple random variables through joint, marginal, and conditional distributions.
- Introduce covariance as a measure of association between random variables and its implications in data analysis.
- Equip participants with the ability to interpret correlation coefficients and understand multivariate distributions.
Objective
The course follows a traditional classroom format with slide presentations, fostering interactive engagement and participation involvement.
Learning Outcomes
On successful completion of this course, learners will be able to:
- Demonstrate proficiency in calculating and interpreting measures of central tendency and dispersion, including variance and standard deviation.
- Understand the concept of expected value and its significance in describing the central tendency of random variables.
- Utilize visual tools such as box plots, kernel density estimation, and QQ plots for data description and distribution visualization.
- Analyze the probability distribution of multiple random variables through joint, marginal, and conditional distributions.
- Calculate and interpret covariance as a measure of the relationship between random variables.
- Interpret correlation coefficients and understand their implications in assessing linear relationships between variables.
- Apply the concepts learned to real-world datasets and derive meaningful insights for decision-making in data science tasks.
Schedule
This training has a total duration of 9 hours and takes place over 3 days:
- 24-06-2024: 16:00 – 19:00
- 25-06-2024: 16:00 – 19:00
- 27-06-2024: 16:00 – 19:00
Format and Location
This course takes place ON-SITE
Terres Rouges building
14, porte de France
L-4360 Esch/Alzette
Level
Beginner
Prerequisites
No prerequisites necessary
Additional Info
Certification
This training does not have any assessment or exams; a certificate of participation will be issued to participants.
Esco Skills
probability theory, analyse scientific data, inspect data, statistics
Esco Occupations
data analyst, meteorologist, weather forecaster, astronomer, sensory scientist, economist, statistician, data scientist