INTRODUCTION TO CODING AND DATA MANAGEMENT-2 PRACTICE

The aim of this course is to provide students with hands-on experience in cleaning, processing, and visualizing data. Specifically, students will learn to use a programming language to read and write data in standard formats, process it to extract useful information, and create visualizations and plots to effectively communicate and explain the information derived from the data.

This course is designed to complement "Introduction to Coding and Data Management-II," which provides foundational tools for data management through programming. Students will have the opportunity to apply the problem-solving techniques and algorithmic thinking covered in the lectures.

The students will accomplish the following objectives:

-Knowledge:
i) Acquire the ability to utilize common libraries, such as NumPy and Pandas, along with advanced data structures to tackle specific challenges;
ii) Gain an understanding of common data visualization techniques and learn to create visualizations using the Seaborn library;
iii) Learn how to effectively organize code into modules and classes.

-Application of Knowledge:
i) Employ complex library structures to organize, clean, and analyze data in order to solve formal algorithmic problems;
ii) Structure solution code using modules and classes for better organization.

-Communication:
i) Create a variety of data visualizations for preliminary analysis and final presentations.

Understanding of content in Introduction to Coding and Data Management – I, and in particular the basics of Python programming and of complex data structures.

• Review of the midterm test
* File operations (reading, writing, appending)
* Data manipulation in Pandas (joining data frames, aligning, handling of invalid values and mismatches, merging and splitting columns, timestamp management, correcting inconsistencies)
* Managing several data sets at once and summarising (obtaining distributions, getting percentiles, discovering parallels among data fields, fixing timestamp issues, getting monthly or yearly summaries)
* Data visualisation (plotting histogram using different libraries in Python, plotting line plot and scatterplot in Seaborn, adding other visual elements like size and hue)

Python for Data Analysis. O'Reilly. Wes McKinney.

This course does not have an official evaluation. Students learning in this course will affect their performance in the course Introduction to Coding and Data Management-II.
In that course, evaluation is achieved through a written exam. The written exam assess the capability of the student to apply problem solving techniques to simple problems.
The exam consists in a set of multiple choice questions and programming exercises, where the student is asked to write a small program to solve a given simple problem.
After the written test, an optional oral exam follows (at the discretion of the professor).

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This course does not have an official evaluation. Students learning in this course will affect their performance in the course Introduction to Coding and Data Management-II.
In that course,
A. scores in the 18-22 range will be awarded in the presence of:
- sufficient understanding of common data management and plotting libraries
- sufficient skills in the development of code using common libraries for data analysis
B. scores in the 23-26 range will be awarded in the presence of:
- fair or good understanding of common data management and plotting libraries
- fair or good skills in the development of code using common libraries for data analysis
C. scores in the 27-30 range will be awarded in the presence of:
- excellent understanding of common data management and plotting libraries
- excellent skills in the development of code using common libraries for data analysis

Hands-on sessions with programming in Python