Data & AI

Statistics for Data Science

This course will cover the essential statistical concepts that underpin a data science task. The course will start with an overview of the most important concepts of descriptive statistics (mean/median/mode, dispersion, skewness, quantiles, percentages, graphical representations), followed by an introduction to probability with particular focus on the most important probability distributions (uniform, Gaussian, Poisson, binomial, etc.). Equipped with this knowledge, the course will then dive into statistical inference, first point estimation and confidence intervals, and second hypothesis testing. These concepts will be explained intuitively and examples of how to use them will be provided. Depending on the participants’ interests and experience, we may go further into hypothesis testing (ANOVA, goodness-of-fit testing, etc.). Finally, the course will touch upon statistical learning by discussing linear regression and logistic regression.

Content
  • Overview of descriptive statistics, including measures of central tendency (mean, median, mode), dispersion, skewness, and quantiles, as well as the use of percentages and graphical representations for data visualization.
  • Introduction to the fundamental concepts of probability, with a focus on key probability distributions such as uniform, Gaussian (normal), Poisson, and binomial distributions.
  • Equip participants with the knowledge of statistical inference, covering point estimation, confidence intervals, and hypothesis testing.
  • Depending on the participants’ interests and experience, delve deeper into advanced hypothesis testing methods (e.g., ANOVA, goodness-of-fit testing) and introduce basic concepts of statistical learning, such as linear regression and logistic regression.
Learning Outcomes

On successful completion of this course, learners will be able to:

  • Calculate and interpret key descriptive statistics, including measures of central tendency (mean, median, mode), dispersion (variance, standard deviation), skewness, and quantiles.
  • Understand and apply the concepts of probability and probability distributions, such as uniform, Gaussian (normal), Poisson, and binomial distributions.
  • Perform statistical inference techniques, including point estimation and constructing confidence intervals.
  • Conduct hypothesis testing, including understanding p-values and making decisions based on test results.


(Depending on participants' interests and experience) Apply advanced hypothesis testing methods, such as ANOVA and goodness-of-fit tests, to analyze complex datasets. Develop foundational skills in statistical learning techniques, including performing linear regression and logistic regression for predictive modeling.

Training Method

The course follows a traditional classroom format with slide presentations, fostering interactive engagement and participation involvement.

Organised By
Digital Learning Hub Luxembourg
Digital Learning Hub Luxembourg
Certification
Participation Only
Prerequisites

No prerequisites necessary


Planning and location
Session 1
12/05/2025 - Monday
15:00 - 18:00
Session 2
19/05/2025 - Monday
15:00 - 18:00
Session 3
26/05/2025 - Monday
15:00 - 18:00
Available Edition(s):

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36.00 € 36.0 EUR 36.00 €

36.00 €

Not Available For Sale

Your trainer(s) for this course
Christophe LEY
Christophe LEY

Christophe Ley is Professor of Applied Statistics at the University of Luxembourg and co-founder of GrewIA, a company offering comprehensive tailor-made training and innovative consulting solutions in Artificial Intelligence and Data Science.