Topic outline

  • Preparatory courses in Statistics and Probability

    The Department of Statistical Sciences and the Department of Information Engineering, Electronics and Telecommunications organize some preparatory courses for Sapienza international students and especially for prospective Master students of a.y. 2019/2020:
    • Crash course :: Probability & Statistics
    • Crash course :: START-R

     

    These courses are meant to provide students with the elements of Probability theory and Fundamentals of the R software environment for statistical computing (https://www.r-project.org/) whose knowledge is required for the international MS programmes Statistical Methods and Applications and Data Science.

    The courses are scheduled in early September (11-20) before the formal start of the academic year 2019/2020.

    Attendance is strongly recommended since the concepts covered in the courses will be assumed during the MS programmes.

    It is possible to attend either one of the courses or both. Please specify your choice in the registration form below.

    The Crash course :: Probability is composed of lectures and tutorials, concluding with a short test. Successfully completing the test will be credited as a bonus within the Data Science mandatory course on Statistical Methods for Data Science.

     

  • Registration form

  • Schedule

    • Crash course :: Probability

    Monday 17       :: Room III :: 10:00 - 14:00

    Tuesday 18      :: Room II :: 10:00 - 14:00

    Wednesday 19 :: Room III     :: 10:00 - 14:00

    Thursday 20     :: Room III :: 10:00 - 14:00

    Friday 21          :: Room V-VI :: 10:00 - 14:00

    • Crash course :: START-R

    Monday 23       :: Room CLA-04 :: 09:00 - 17:00

    Wednesday 25 :: Room CLA-04 :: 09:00 - 17:00

    Friday 27          :: Room CLA-04 :: 09:00 - 15:00

    • Crash Course :: Probability

      Course instructors: Pierfrancesco Alaimo di Loro - Tullia Padellini

      The goal of this crash course is to provide you with the basic of probability theory, from a "data science" perspective: i.e. data modelling. We cover both theory and exercises, assuming no prior knowledge of the topic. 


      We are going to (try to) talk about the following topics:

      Lecture 1: (Why) we need a Probabilistic Model. Building blocks: definition of probability - conditioning & independence.

      - Lecture 2: What is a Probabilistic Model. Definition and characterisation of a random variable. Basic summaries: expected value, variance.

      - Lecture 3: Famous Models and how to Use them. Discrete (Binomial, Poisson) and Continuous (Gaussian, Exponential) random variables.

      - Lecture 4: Beyond 1D. Characterising multidimensional random variables: joint, marginal and conditional distribution. Dependence and association: Correlation, Covariance. 

      - Lecture 5: Stat vs Prob and Exercises. Population values vs Sample values. Wrap up what we left out the previous days & solve exercises.



      There are no lectures notes, but you can find all the topics we covered in the following books: 


      All of Statistics - Chapters 1 to 4

      A modern introduction to Probability and Statistics - Sparse stuff from Chapters 1 to 10





      • Start-R :: Bioinformatics

        Crash Course :: Start-R

        Course instructor: Tullia Padellini

        The goal of this crash course is to provide you with the basics of the R environment for data analysis

        Temptative syllabus:

        Lecture 1: 

        • First steps with R and RStudio: user interface, data types, objects, environments, and memory organization
        • Data manipulation: read, select, reorder, summarize (1-dim, 2-dim & lists), export, plot

        Lecture 2: 

        • Basic Probability: random distributions in R
        • Statistics: basic summaries, association measures, linear regression

        - Lecture 3: 

        • Data visualization, reports and interactive documents
        • More on programming and statistical methods