Quantitative Economics and Statistics

Program positioning within the LMD system

This specialization is offered as an academic Bachelor's degree within the LMD (Licence-Master-Doctorate) system. The curriculum is structured into six semesters and organized through modular teaching units (fundamental, methodological, exploratory, and transversal), designed to ensure a gradual accumulation of competencies from theoretical foundations to advanced quantitative applications.

Scientific rationale

The program combines:

·        a theoretical foundation in economics (microeconomics, macroeconomics, and fields such as public, monetary, and international economics, etc.);

·        a quantitative and statistical toolkit (statistics, mathematics, econometrics, time series analysis, operations research, etc.).

Its purpose is to enable students to understand, analyze, and model economic phenomena in a measurable, estimable, and forecastable manner, while making effective use of supporting quantitative tools and software.

Educational objectives and learning outcomes

The program aims to:

·        Develop quantitative and qualitative knowledge for analyzing macroeconomic and microeconomic phenomena.

·        Strengthen the ability to apply quantitative methods (estimation, analysis, forecasting) in economics.

·        Integrate optimization and operations research techniques to address decision-making problems within economic organizations.

Progression logic across semesters

The pedagogical annex to Decision No. 994 outlines a clear ascending pathway:

·        Year 1 (Semesters 1-2): fundamentals (accounting, microeconomics, introduction to economics, statistics, mathematics, law/sociology of organizations, language), with early exposure to digital skills and programming (e.g., Python basics within exploratory units).

·        Year 2 (Semesters 3-4): deeper coverage of macroeconomics and its fields (public finance, monetary economics, international/Algerian economy, etc.) alongside expanded tools (advanced statistics, operations research), with methodological openness to recent topics related to artificial intelligence within exploratory units.

·        Year 3 (Semesters 5-6): specialization phase (econometrics, time series analysis, operations research, data analysis/data science and machine learning), complemented by statistical software modules (including open-source tools) and a capstone project translating learning outcomes into an applied deliverable.

Teaching and assessment methods (general features)

The program uses both in-person and distance learning modalities. Assessment typically combines:

·        continuous assessment plus a final exam (often 40% / 60% in most modules);

·        some modules assessed by exam only or by continuous assessment only;

·        the capstone project, usually assessed through continuous assessment (CA).

Employability and academic pathways

Graduates may pursue employment in a broad range of public and private institutions (administrations, banks, financial institutions, firms, etc.) as well as entrepreneurial initiatives. Academic pathways toward related specializations at the Bachelor's, Master's, or Doctoral levels may be available, subject to academic coherence and prerequisite requirements.