---
title: "Skills in Statistics, Data Science and Machine Learning"
---


* Statistics

  - Knowledge of Linear Models and Generalised Linear Models
    (including logistic regression), both in theory and in
    applications
  - Classical Statistical inference (maximum likelihood estimation,
    method of moments, minimal variance unbiased estimators) and
    testing (including goodness of fit)
  - Nonparametric statistics
  - Bootstrap methods, hidden Markov models
  - Knowledge of Bayesian Analysis techniques for inference and
    testing: Markov Chain Monte Carlo, Approximate Bayesian
    Computation, Reversible Jump MCMC
  - Good knowledge of R for statistical modelling and plotting

* Data Analysis

  - Experience with large datasets, for classification and regression
  - Descriptive statistics, plotting (with dimensionality reduction)
  - Data cleaning and formatting
  - Experience with unstructured data coming directly from embedded
    sensors to a microcontroller
  - Experience with large graph and network data
  - Experience with live data from APIs
  - Data analysis with Pandas, xarray (Python) and the tidyverse (R)
  - Basic knowledge of SQL

* Graph and Network Analysis

  - Research project on community detection and graph clustering
    (theory and implementation)
  - Research project on Topological Data Analysis for time-dependent
    networks
  - Random graph models
  - Estimation in networks (Stein's method for Normal and Poisson
    estimation)
  - Network Analysis with NetworkX, graph-tool (Python) and igraph (R
    and Python)

* Time Series Analysis

  - experience in analysing inertial sensors data (accelerometer,
    gyroscope, magnetometer), both in real-time and in post-processing
  - use of statistical method for step detection, gait detection, and
    trajectory reconstruction
  - Kalman filtering, Fourier and wavelet analysis
  - Machine Learning methods applied to time series (decision trees,
    SVMs and Recurrent Neural Networks in particular)
  - Experience with signal processing functions in Numpy and Scipy
    (Python)

* Machine Learning

  - Experience in Dimensionality Reduction (PCA, MDS, Kernel PCA,
    Isomap, spectral clustering)
  - Experience with the most common methods and techniques
  - Random forests, SVMs, Neural Networks (including CNNs and RNNs),
    both theoretical knowledge and practical experience
  - Bagging and boosting estimators
  - Cross-validation
  - Kernel methods, reproducing kernel Hilbert spaces, collaborative
    filtering, variational Bayes, Gaussian processes
  - Machine Learning libraries: Scikit-Learn, PyTorch, TensorFlow,
    Keras

* Simulation

  - Inversion, Transformation, Rejection, and Importance sampling
  - Gibbs sampling
  - Metropolis-Hastings
  - Reversible jump MCMC
  - Hidden Markov Models and Sequential Monte Carlo Methods
