INTRODUCTION TO CODING AND DATA MANAGEMENT-2

Academic year 2020/2021 Syllabus of previous years
Official course title INTRODUCTION TO CODING AND DATA MANAGEMENT-2
Course code ET7006 (AF:332688 AR:178847)
Modality On campus classes
ECTS credits 6 out of 12 of INTRODUCTION TO CODING AND DATA MANAGEMENT
Degree level Bachelor's Degree Programme
Educational sector code ING-INF/05
Period 4th Term
Course year 1
Where RONCADE
Contribution of the course to the overall degree programme goals
The goal of this course is to teach students how to cleanse, process and visualize data. In particular, the students will learn how to use a programming language to read and write data from standard formats, process it to extract useful information, and visualize and plot it in order to show and explain its content.
Expected learning outcomes
The course introduces basic tools in the field of data management through programming.
Programming is intended as a way to model real-world problems and to design algorithmic solutions to solve them.
This course teaches students problem solving techniques and algorithmic thinking.
Technical topics cover algorithms, data structures, and Python programming.

The students will achieve the following objectives:

Knowledge: i) learn how to use common libraries (e.g., NumPy and Pandas) and complex data structures to address specific problems; ii) understand common data visualizations techniques and how to use common library (Seaborn) objects to create data visualizations; iii) understand how to organize code into modules and classes.

Application of knowledge: i) use complex library structures to organize, cleanse and analyze data to solve formal algorithmic problems; ii) organize solution code into modules and classes.

Communication: i) generate various data visualizations for preliminary analysis and final presentation.
Pre-requirements
Understanding of content in Introduction to Coding and Data Management – I, and in particular the basics of Python programming and of complex data structures.
Contents
• Structuring the code with modules and classes
• Data representation (txt, csv, json, …)
• File read and write
• Data cleansing
• Basics of data processing, analysis and visualization with Panda (series, dataframes, operation, mapping, join) and NumPy (matrices, operations, statistical functions)
• Basics of data visualization (data dimensionality, graphs, charts, maps) with Seaborn
Referral texts
Python for Data Analysis. O'Reilly. Wes McKinney.

Instructor notes.
Assessment methods
The student will be evaluated based on their capability to analyze a problem, model a solution, and translate it into a computer program.

Students are evaluated based on an oral discussion of their team project design, project code and knowledge of course content.
Teaching methods
Lectures and hands-on sessions.
Type of exam
oral
Definitive programme.
Last update of the programme
22/07/2020