Big Data Analytics for Economic and Social Research Course

Introduction
Big data analytics has transformed the way economic and social research is conducted by enabling the analysis of large, complex, and high-frequency datasets that traditional methods cannot adequately handle. This course provides participants with a structured and applied understanding of how big data tools enhance empirical research, policy analysis, and evidence-based decision-making.
The programme bridges conventional economic and social research methodologies with advanced data analytics techniques. Participants will learn how administrative data, transactional data, social media data, satellite imagery, and digital traces can be systematically analyzed to generate new insights into economic behavior and social outcomes.
A strong emphasis is placed on practical data analytics workflows, including data acquisition, cleaning, integration, and analysis. The course demonstrates how big data complements surveys and official statistics by improving timeliness, granularity, and predictive power in economic and social research applications.
Participants will explore advanced analytical techniques such as machine learning, predictive modeling, and network analysis, focusing on their application to labor markets, poverty analysis, public service delivery, health, education, and financial inclusion research.
The course also addresses critical issues related to data governance, ethics, privacy, and bias in big data research. Participants will examine how responsible data use and transparent methodologies are essential for maintaining credibility and policy relevance in economic and social analysis.
By the end of the programme, participants will possess applied skills to design, implement, and interpret big data analytics projects for economic and social research, enabling them to support data-driven policy formulation, academic research, and institutional decision-making.

Duration

10 days

Who Should Attend

• Economic and social science researchers
• Government statisticians and policy analysts
• Development practitioners and monitoring and evaluation specialists
• Data analysts working in public policy and research institutions
• Central bank and ministry of finance research staff
• Academics and postgraduate students
• Think-tank researchers and consultants
• NGO and international organization professionals

Course Objectives

• To equip participants with advanced competencies in applying big data analytics techniques to economic and social research questions using diverse and large-scale datasets.
• To strengthen participants’ ability to integrate big data sources with traditional surveys and official statistics for improved empirical analysis and policy relevance.
• To develop practical skills in data collection, preprocessing, and management for economic and social research applications.
• To enhance understanding of machine learning and predictive modeling techniques and their use in analyzing complex economic and social phenomena.
• To build capacity in applying big data analytics to labor market analysis, poverty measurement, inequality assessment, and social service delivery evaluation.
• To strengthen analytical skills in identifying patterns, trends, and causal relationships within large and unstructured datasets.
• To improve participants’ ability to design reproducible and transparent big data research workflows aligned with international research standards.
• To enhance competence in visualizing and communicating complex analytical results to policymakers and non-technical stakeholders.
• To promote awareness of ethical, legal, and privacy considerations in the use of big data for economic and social research.
• To introduce modern tools and programming environments commonly used in big data analytics for social science research.
• To strengthen participants’ capacity to evaluate data quality, bias, and limitations inherent in big data sources.
• To enable participants to apply big data analytics in policy evaluation, impact assessment, and evidence-based decision-making processes.

Comprehensive Course Outline

Module 1: Introduction to Big Data in Economic and Social Research

• Big data concepts and characteristics
• Role of big data in modern research
• Comparison with traditional data sources
• Research opportunities and limitations

Module 2: Data Sources for Economic and Social Analytics

• Administrative and transactional data
• Digital platforms and social media data
• Remote sensing and geospatial data
• Data access and ownership issues

Module 3: Data Management and Preprocessing

• Data cleaning and transformation techniques
• Handling missing and noisy data
• Data integration and harmonization
• Scalable data storage solutions

Module 4: Programming Tools for Big Data Analytics

• Introduction to Python and R for analytics
• Data manipulation libraries and frameworks
• Working with large datasets efficiently
• Reproducible research practices

Module 5: Exploratory Data Analysis and Visualization

• Descriptive analytics for big data
• Visualization techniques for large datasets
• Identifying patterns and anomalies
• Communicating insights effectively

Module 6: Machine Learning for Economic Research

• Supervised and unsupervised learning methods
• Model selection and validation
• Predictive modeling applications
• Interpretation of machine learning results

Module 7: Text and Social Media Analytics

• Text mining and natural language processing
• Sentiment analysis for social research
• Measuring public opinion and behavior
• Ethical considerations in text analytics

Module 8: Network and Graph Analytics

• Social and economic network concepts
• Network data construction and analysis
• Applications in labor markets and finance
• Visualization of network structures

Module 9: Big Data for Labor Market and Poverty Analysis

• Employment and wage analytics
• Informality and labor mobility studies
• Poverty mapping using alternative data
• Inequality measurement techniques

Module 10: Geospatial Analytics for Social Research

• GIS fundamentals and spatial data
• Satellite imagery and night-time lights
• Spatial econometrics applications
• Policy insights from geospatial analysis

Module 11: Predictive Analytics and Policy Evaluation

• Forecasting social and economic indicators
• Early warning systems and risk analysis
• Policy impact prediction models
• Scenario analysis for decision-making

Module 12: Big Data and Impact Evaluation

• Integrating big data into evaluation design
• Quasi-experimental methods and analytics
• Measuring program effectiveness
• Validity and reliability challenges

Module 13: Ethics, Privacy, and Data Governance

• Data protection and privacy frameworks
• Bias and fairness in analytics
• Responsible data use principles
• Institutional governance structures

Module 14: Emerging Technologies in Data Analytics

• Artificial intelligence in social research
• Cloud computing and big data platforms
• Automation and real-time analytics
• Future research trends

Module 15: Communicating Big Data Research Results

• Policy briefs and analytical reports
• Data storytelling techniques
• Visualization for policymakers
• Managing uncertainty and limitations

Module 16: Project and Applied Case Studies

• Designing a big data research project
• Applied economic and social case studies
• Presentation and peer review
• Translating research into policy action

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, airport transfers, Upskill gift package, guided tour and buffet lunch.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better