COMPUTER PROGRAMMING AND DATA MANAGEMENT - 2

Academic year
2020/2021 Syllabus of previous years
Official course title
COMPUTER PROGRAMMING AND DATA MANAGEMENT - 2
Course code
EM1404 (AF:338399 AR:179566)
Modality
Blended (on campus and online classes)
ECTS credits
6 out of 12 of COMPUTER PROGRAMMING AND DATA MANAGEMENT
Degree level
Master's Degree Programme (DM270)
Educational sector code
INF/01
Period
2nd Term
Course year
1
Where
VENEZIA
Moodle
Go to Moodle page
This course covers part of the "quantitative" aspects of the master program, and aims to provide the student with knowledge and skills on the computational aspects fundamental for the data science field.

More specifically, the goal of this course is to teach students how to use a programming language to write scripts and more complex software libraries to handle data, including storage, loading, preparation, processing and visualization.
The course provides an introduction to the basics of the design and implementation of data processing software applications and libraries. Data processing is intended as the many activities that can be performed on data by means of automatic computation, including preprocessing, handling and analysis.

This course teaches students to define a problem and its solution in terms of data structures and information and to use a programming language to solve it effectively.

The students will achieve the following objectives:

Knowledge: i) learn the basics of data handling and the most common data structures; ii) understand how to interpret and write data handling programs in the Python programming language;

Application of knowledge: i) analyze problems and design formal algorithmic solutions using data structures; ii) translate solutions into computer programs to be applied to data.

Communication: i) generate basic data visualizations for preliminary analysis.
The student must have attended proficiently the first module of this course. Specifically he/she must already know how to write programs in the Python programming language.
1) Introduction
2) Recap of Python built-in data structures and functions
3) Using the NumPy numerical computing package
4) Using he Pandas library
5) Storing and loading data from different sources
6) Data cleaning and preparation
7) Data wrangling, aggregation and manipulation
9) Plotting and visualization
10) Handling time series
11) Modeling libraries in Python
Python for Data Analysis, 2nd Edition, Wes McKinney, O'Reilly Media, Inc., ISBN: 9781491957660
The written exam is aimed at assessing the programming skill and the problem solving capability, through the solution of exercises on the course subjects.
Theoretical and practical lectures.
Exercise lectures.
English
written
Definitive programme.
Last update of the programme: 16/08/2020