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Technical Data Sheet.

This page details the content of the taught module, explaining, in turn, the prerequisites, objectives, and teaching method.


Readers are strongly encouraged to consult this section to learn more and become more familiar with the module.




The following lines, written in accordance with Decree No. 903 of July 20, 2023 (establishing the curriculum for obtaining a Bachelor's degree in Communication), present the module, its teaching, and the prerequisites, one by one. By clicking on each accordion, you will find more details.
Some elements have further explanations in the Foreword section, which students are encouraged to consult with interest.

Faculty Faculty of Humanities and Social Sciences
Department Department of Humanities
Field of Study Information and Communication Sciences
Target Audience Second Year Bachelor's
Description Educational Support
Course Title Data Presentation and Analysis
Instructor Farouk Bahloul
Contact farouk.bahloul@univ-bejaia.dz
Total Hours Forty-five (45) hours
Hours per Week One hour thirty (01h30) / week
Duration Fifteen (15) weeks / Semester Module (S III)
Credits Two (02)
Coefficient Two (02)
Course Overview The Data Presentation and Analysis module is part of the methodological teaching unit and is therefore practice-oriented.
The module requires students to master a set of knowledge with the goal of providing an analysis based on both theoretical reasoning [logical and epistemological] and analytical [Statistics, Data Science].
The module is at the intersection of several scientific disciplines, benefiting from statistical sciences to determine the appropriate terminology for analysis.
The module also relies, to a large extent, on data science for analyzing the typologies of relationships between variables within the framework of quantitative data analysis. Finally, the module also partially draws on the methodological teaching from the first year, as data analysis requires a certain skill that echoes a range of knowledge.
The module content also involves considerations related to writing skills and abilities. One of the aims of the module is to assist students in mastering the methods and techniques of composing analysis reports (quantitative, qualitative, or mixed) while adhering to the most commonly used bibliographic standards (styles). [ We will present the objectives, prerequisites, and content of the course in the following sections ].

The teaching of the module aims at objectives that we can categorize as follows:

  • Introduce the student to analytical reasoning
    Through this course material, we aim to guide the student in engaging with data. Officially part of the methodological teaching unit, the Data Presentation and Analysis module provides an opportunity to work with concrete information. We will focus the knowledge in this module on its technical aspects; data analysis involves calculations and graphical representations.

  • Prepare the student for conducting field surveys
    The content of the material emphasizes the necessity of data collection. The three axes of this support range from conceptualization (definition in variables) to the analysis and interpretation of survey data. This task cannot be accomplished without mastering the fundamentals of survey work. With the development of data analysis tools, we have also allocated a significant portion to working with the Python language.


  • Familiarize the student with the principles of statistical logic for the humanities
    In data analysis and representation, a large amount of knowledge from statistics is involved. Data analysis relies heavily on statistics, and therefore we have deemed it important to give more emphasis to the logic of mathematical formulas, their construction, and their meaning. We support the idea that learning a tool or programming language for data analysis involves mastering the theoretical concepts that structure the conversion between theory and computerized application.

  • Develop the student's data analysis mechanisms for surveys
    Every data analysis is a new experience in itself. Data analysis takes a relatively long time given the scope of the research conducted. One of the teaching objectives is to instill in students a mindset that questions data systematically and continuously, otherwise the analysis process will become a series of steps to be validated.

  • Enable the student to write a research report based on survey data
    This objective is the logical result of the previous ones. Indeed, data analysis, however relevant, must be well communicated and conveyed through "analysis" and writing rules. In this teaching, we will focus on providing students with the basics of disseminating information.

For the teaching of the Data Presentation and Analysis module, no specific prerequisites are required. As preparation, the instructor will provide a detailed presentation of the mathematical and methodological procedures that the student will need to assimilate the content of the course.
The module will also benefit from the knowledge acquired in the schools and methods module concerning sampling and survey tools in the humanities.

The content of the module is divided into three Blocks (each Block containing a set number of lessons , with each lesson spread across a series of sessions ).
Below, we will provide an overview of the key aspects of the course content. You can click on the Summary item, which will redirect you to a page presenting the module content interactively.

Module Content

  • Block I | Data Analysis in Humanities and Social Sciences
  • Lesson 1 : Introduction to Research in Humanities and Social Sciences
  • Lesson 2 : Introduction to Data Analysis

  • Block II | Descriptive Analysis & Probability
  • Lesson 1 : Univariate Descriptive Analysis
    • Session 1.1. : Central Tendency Parameters
    • Session 1.2. : Dispersion Parameters
    • Session 1.3. : Position Parameters
  • Lesson 2 : Bivariate Descriptive Analysis
    • Session 2.1. : Two-Way Statistics
    • Session 2.2. : Bivariate Statistical Indices
  • Lesson 3 : Finite Probabilities & Combinatorial Analysis
    • Session 3.1. : Probabilities on a Finite Universe
    • Session 3.2. : Principles of Combinatorial Analysis

  • Block III | Statistical Inference
  • Lesson 1 : Sampling and Estimation
  • Lesson 2 : The \(t\) Test
  • Lesson 3 : The \(\chi^2\) Test
  • Lesson 4 : Analysis of Variance
    • Session 4.1. : One-Way Analysis of Variance
    • Session 4.2. : Factorial Analysis of Variance
  • Lesson 5 : Correlation & Linear Regression
    • Session 5.1. : Correlation
    • Session 5.2. : Linear Regression
  • Lesson 6 : Non-Parametric Tests.

  • Appendices
    • Appendix 1 : Glossary
    • Appendix 2 : Statistical Tables

Assessment consists essentially of a final semester exam and continuous evaluation.

The module is taught both in-person and online. Students have access to an e-learning space to interact with the instructor.