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Data acquisition mining processing

Data acquisition, processing and mining for AI#

Outline#

  1. Basic Python for data science (DataFrames, CSV/JSON, Numpy array, Pandas) - just intro, not details (=what is needed for labs)
  2. Data acquisition: advanced SQL (join for dataset construction, sampling via SQL), Web APIs (REST, OAuth, etc.), scraping, (maybe) intro to streaming data
  3. Data cleaning and transformation: missing data imputation, outlier detection, data leakage
  4. Data mining: asocciation rules / frequent items, clustering, EDA/visualization for data cleaning/processing

In brief#

  • Period: semester 7
  • Credits: 3 ECTS
  • Number of hours: 24h
  • Apogée:
  • Basic working knowledge of C or C++ and Python.
  • Basic familiarity with Git.

Pedagogical team#

  • Silviu Maniu

Evaluation#

TP/prpject and final written exam