Section outline

  • Admission - Academic Year 2026/2027

    CALL FOR APPLICATION - 2026/2027 INTAKE:

    • Students with international qualification: candidates with international qualifications will benefit from a new application platform. Application period is open.
      The platform is exclusively for students (both Italian and international) holding a foreign qualification. Please consult Apply@Unimib website

    • Students with an Italian qualification: The call for application and the list of candidates admitted to the program following the interview will be published in the next few months at the webpage https://www.unimib.it/graduate/data-science.
     
    ADMISSION INTERVIEW SYLLABUS:

    On this webpage, students can find the topics that will be evaluated during the oral admission interview, along with the corresponding textbooks. 
     
    COMPUTER SCIENCE 
     
    1. Programming Fundamentals
    • Control structures, functions, input/output (required)
    • Data types, object orientation (preferably in Python or R).
    • Tabular data management (Dataframe, dplyr, pandas)
    2. Data structures and algorithms
    • Arrays (required)
    • Lists, hashes/dictionaries
    3. Operating systems
    • Linux shell
    • Basic terminal commands (cd, ls, cp, cat)
    4. Databases and SQL
    • Relational model, SQL queries.

    Recommended textbook: 
    • Kenneth Leroy Busbee and Dave Braunschweig. "Programming Fundamentals: A Modular Structured Approach, 2nd Edition".
    • Allen B. Downey. "Think Python".
     
    STATISTICS
     
    1. Fundamentals of Probability
    • Sample spaces, events, conditional probability, independence, Bayes' theorem.
    2. Random variables and probability distributions
    • Discrete and continuous probability distributions, density and distribution functions, expected value and variance.
    3. Sampling distributions and limit theorems
    • Central limit theorem, law of large numbers, distributions of sample mean and variance.
    4. Descriptive statistics
    • Measures of central tendency, dispersion, frequency distributions, graphical representations.
    5. Statistical inference
    • Point and interval estimation, confidence intervals, hypothesis testing.
    6. Linear regression
     
    Recommended textbook: 
    • Mood, Alexander McFarlane. "Introduction to the Theory of Statistics".