English speakers: Data Science for Research - CEDRE Program 2 - Data Preprocessing for and in Research: Practical Techniques for Effective Data Management - ST CHARLES

- Introduce data preprocessing and preparation for advanced research analysis.
- Collecting and compiling relevant information from various sources and different databases.
- Integration, processing, and exploitation of databases through the SQL language.
- Introduction to the use of Python libraries for data preprocessing.
- Review fundamental statistical concepts.
- Create visualizations with the aim of scientific publication.
- Automate repetitive processes.

1. Introduction to data preprocessing in research.
2. Present the steps of data preprocessing.
3. Database modeling. Types of databases and management systems. Conceptual data model.
4. Relational databases. Introduction to the SQL language. Fundamentals of SQL queries. CRUD operations (acronym for Create, Read, Update, and Delete) and joins.
5. Python libraries for data preprocessing (pandas and numpy). File reading. Data-frame manipulation. Indexing, selection, and assignment.
6. Tools for descriptive statistics; types of variables: qualitative, quantitative, nominal, ordinal, discrete, continuous. Measures of central tendency and dispersion.
7. Dimensionality reduction Part 1: Feature transformation.

- Foundations of Python programming or completion of the Module 1 of the CEDRE 1 program.
- Personal or professional computer required.
- Prior installation of Anaconda is necessary; you can download it from https://www.anaconda.com/

Upon completion of this module, participants will have acquired the skills to:
- Identify various types of Database Management Systems - DBMS.
- Recognize the structure of a database - whether relational or non-relational.
- Extract relevant information from a relational database.
Participants will also have developed the abilities to:
- Create and comprehend a Conceptual Data Model.
- Read, write, and modify content within a database using SQL.
- Query databases with Python.
- Manipulate databases from different sources, including:
o Merging databases.
o Adding or removing rows or columns in a database.
o Identifying errors or inconsistencies.
- Generate curves or other types of graphs from a database.
- Conduct statistical analysis.
- Clean databases.
- Apply data preprocessing techniques in research

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