Course Introduction

Applied Data Science for Competition Policy and Market Surveillance Training Course provides a practical framework for using data science, statistical analysis, machine learning, and market intelligence techniques to support competition analysis and systematic market monitoring. Participants learn how to transform pricing, sales, firm, consumer, transaction, trade, and industry data into evidence that can inform structured regulatory and economic analysis.

The course explores how data science can support the analysis of market structures, competitive conditions, pricing behaviour, concentration, entry and exit, product availability, consumer trends, and firm-level performance. Participants examine methods for integrating administrative, commercial, survey, transaction, and public datasets to identify patterns that may warrant further economic or regulatory investigation.

Participants develop practical analytical capabilities for market definition support, concentration analysis, price monitoring, firm profiling, demand analysis, merger screening, and market surveillance. The program combines descriptive statistics, econometric concepts, predictive modeling, anomaly detection, and visualization while emphasizing the distinction between analytical indicators and formal legal or economic conclusions.

A major focus is placed on monitoring market dynamics over time. Participants learn how historical price observations, product information, firm characteristics, market shares, transaction records, import and export information, and other indicators can be analyzed to detect significant changes in market conditions. Forecasting and anomaly detection techniques are introduced to help analysts identify patterns requiring additional examination.

The program also addresses data quality, governance, confidentiality, transparency, reproducibility, and responsible analytical practice. Participants examine challenges such as missing observations, inconsistent product definitions, changing firm identifiers, sampling limitations, measurement errors, and potential biases in commercial or administrative datasets. They learn how to document analytical assumptions and communicate uncertainty without overstating what the data can establish.

By the end of the course, participants will be able to design and apply data science workflows for competition policy and market surveillance, from data acquisition and preparation through exploratory analysis, statistical modeling, visualization, validation, and reporting. The course is designed to strengthen institutional analytical capacity and support rigorous, transparent, and evidence-based monitoring of market conditions.

Duration

10 days

Who Should Attend

• Competition authority officials responsible for market studies, investigations, economic analysis, or regulatory intelligence.

• Market surveillance officers monitoring prices, product availability, firm behaviour, market developments, and consumer-facing conditions.

• Competition economists and economic analysts conducting market structure, concentration, pricing, demand, or firm-level analysis.

• Data analysts and business intelligence specialists supporting competition policy, regulatory monitoring, and market intelligence functions.

• Consumer protection professionals analyzing prices, product availability, business practices, and consumer market conditions.

• Regulatory policy professionals involved in competition policy, market regulation, economic policy, or sector oversight.

• Investigators and case officers seeking analytical methods for screening markets, firms, transactions, and potential areas requiring further examination.

• Trade and industry analysts working with import, export, production, pricing, firm, and sector-level datasets.

• Statisticians and researchers conducting quantitative analysis of markets, industries, consumers, firms, and economic behaviour.

• Monitoring and evaluation specialists assessing market interventions, regulatory initiatives, competition conditions, and market outcomes.

• Legal and compliance professionals seeking greater understanding of quantitative evidence used in competition and market surveillance processes.

• Financial and market intelligence professionals analyzing firm performance, prices, transactions, market concentration, and industry developments.

• Digital economy and technology policy professionals monitoring online platforms, digital markets, pricing, consumer activity, and technology-sector competition.

• Data governance and IT professionals supporting secure analytical environments for confidential regulatory and market information.

• Senior managers and executives seeking practical capabilities for using data science to strengthen competition analysis and market surveillance.

Course Objectives

• Develop practical data science capabilities for analyzing market structures, firms, prices, transactions, consumers, products, trade, and industry performance.

• Understand how to translate competition policy and market surveillance questions into measurable analytical problems suitable for descriptive and predictive data science methods.

• Apply data preparation, integration, cleaning, validation, and standardization techniques to firm-level, transaction, pricing, market, and regulatory datasets.

• Use exploratory data analysis to identify patterns in prices, market shares, concentration, product availability, firm entry and exit, sales, and market performance.

• Develop market and firm segmentation approaches that support systematic analysis of industries, competitors, products, consumer groups, and relevant market characteristics.

• Apply statistical and machine learning techniques to identify unusual market patterns, changes in pricing behaviour, and developments that may warrant additional investigation.

• Analyze market concentration and structural indicators using appropriate measures while understanding the assumptions and limitations associated with different analytical approaches.

• Apply time-series forecasting and trend analysis to monitor prices, sales, market activity, product availability, and other indicators of changing market conditions.

• Build dashboards and visualization systems that communicate market developments, pricing patterns, firm activity, concentration indicators, and surveillance results clearly to decision-makers.

• Develop analytical screening approaches that can help prioritize markets, firms, transactions, or developments for further economic or regulatory examination without treating model outputs as final findings.

• Apply responsible data science principles covering confidentiality, data governance, transparency, reproducibility, model validation, explainability, bias management, and appropriate interpretation of analytical evidence.

• Design end-to-end analytical workflows that connect competition questions with data acquisition, economic analysis, statistical modeling, visualization, reporting, validation, and continuous market monitoring.

Comprehensive Course Outline

Module 1: Foundations of Data Science for Competition Policy

• Examine the role of data science in competition economics, market surveillance, regulatory intelligence, market studies, and evidence-based policy analysis.

• Explore common competition datasets, including firm records, prices, transactions, market shares, consumer information, trade data, and product catalogues.

• Understand the data science lifecycle from defining an analytical question through data preparation, exploration, modeling, validation, visualization, and reporting.

• Identify high-value analytical use cases for market screening, price monitoring, concentration analysis, firm profiling, and surveillance of changing competitive conditions.

Module 2: Market Data Sources and Analytical Architecture

• Examine administrative, commercial, transaction, survey, web-derived, trade, financial, and public data sources relevant to competition and market surveillance.

• Explore methods for integrating firm, product, price, transaction, market share, geographic, and industry datasets while maintaining consistent identifiers.

• Understand analytical databases, data warehouses, data lakes, APIs, and secure analytical environments used to manage large-scale market datasets.

• Develop market data inventories documenting data ownership, collection frequency, coverage, quality, accessibility, confidentiality, and analytical relevance.

Module 3: Data Quality and Preparation for Market Analysis

• Identify data-quality problems involving duplicate firms, inconsistent product definitions, missing prices, changing identifiers, classification differences, and incomplete transaction records.

• Apply techniques for cleaning, standardizing, transforming, joining, and validating market datasets for descriptive analysis, econometric work, and machine learning.

• Develop validation rules for detecting anomalous prices, inconsistent quantities, duplicated observations, impossible values, and other records requiring review.

• Create reproducible data preparation workflows that document transformations, assumptions, exclusions, quality checks, and limitations affecting market analysis.

Module 4: Exploratory Market and Firm Analysis

• Apply descriptive statistics and exploratory analysis to prices, sales, market shares, firm characteristics, product availability, and industry-level performance.

• Examine relationships among firm size, market position, pricing, product characteristics, geographic presence, and other variables relevant to competitive conditions.

• Analyze market developments across sectors, regions, products, firms, customer groups, and time periods to identify meaningful patterns and changes.

• Translate exploratory findings into clearly defined analytical questions for deeper economic analysis, surveillance, screening, or further investigation.

Module 5: Market Structure and Concentration Analytics

• Examine market structure using indicators such as firm counts, market shares, concentration measures, entry and exit, and distribution of firm activity.

• Apply appropriate methods for calculating and interpreting concentration indicators while recognizing the importance of market definition and data quality.

• Analyze changes in market structure over time to identify shifts in firm participation, market shares, competitive positioning, and industry composition.

• Develop visualizations and analytical reports that communicate structural indicators without presenting quantitative measures as standalone conclusions about competitive effects.

Module 6: Price Monitoring and Market Surveillance

• Analyze price observations across products, firms, locations, channels, customer groups, and time periods to identify market developments requiring closer examination.

• Apply time-series techniques to understand trends, seasonality, volatility, price dispersion, and structural changes within monitored markets.

• Develop price monitoring indicators that distinguish ordinary variation from unusual movements based on transparent and documented analytical criteria.

• Build surveillance dashboards showing price movements, product availability, market changes, firm activity, and other indicators relevant to ongoing market monitoring.

Module 7: Firm Behaviour and Competitive Analytics

• Analyze firm-level information covering sales, prices, product ranges, market participation, geographic presence, business relationships, and other measurable characteristics.

• Develop analytical profiles that help researchers understand differences in firm behaviour, market positioning, product strategies, and commercial activity.

• Apply statistical techniques to examine associations between firm characteristics and observed market outcomes while distinguishing correlation from causal evidence.

• Identify analytical patterns that may justify additional economic or regulatory review while maintaining appropriate separation between screening signals and formal findings.

Module 8: Demand, Consumer, and Product Analytics

• Analyze consumer transactions, survey information, product characteristics, purchasing patterns, and other demand-side datasets relevant to market assessment.

• Apply segmentation methods to identify consumer groups based on observable preferences, purchasing behaviour, product usage, or relevant demographic characteristics.

• Explore demand estimation concepts and statistical relationships between prices, quantities, product characteristics, and consumer behaviour.

• Develop analytical approaches for understanding product substitution, consumer responses, availability patterns, and changing demand conditions using appropriate data.

Module 9: Entry, Exit, and Market Dynamics

• Analyze firm entry, exit, expansion, contraction, and changes in market participation to understand the evolution of competitive landscapes.

• Apply longitudinal and survival-analysis concepts to examine enterprise duration, market participation, and patterns of entry and exit across industries.

• Identify relationships between market dynamics, investment, trade, regulation, technology, demand, and other observable factors influencing industry evolution.

• Develop monitoring indicators for tracking market structure changes and identifying developments that may require additional sector-level analysis.

Module 10: Transaction and Anomaly Analytics

• Explore transaction-level data to identify unusual combinations of prices, quantities, products, firms, locations, or transaction characteristics.

• Apply anomaly detection techniques to establish analytical baselines and identify observations that differ materially from expected patterns.

• Develop transaction screening frameworks that combine statistical indicators with contextual information and clearly documented review procedures.

• Evaluate analytical alerts using appropriate performance measures while recognizing that unusual observations may have legitimate explanations requiring human examination.

Module 11: Merger and Market Screening Analytics

• Examine how structured datasets can support preliminary screening of markets, firms, transactions, and industry developments for further economic analysis.

• Develop analytical indicators using firm size, market shares, concentration, product overlap, geographic presence, and other measurable market characteristics.

• Apply data visualization and comparative analysis to organize large volumes of market information for systematic analytical screening.

• Document screening assumptions and limitations carefully so that quantitative indicators are used as evidence for further assessment rather than treated as conclusive legal or economic determinations.

Module 12: Digital Markets and Platform Analytics

• Analyze digital market data involving online platforms, product listings, transactions, search activity, pricing, consumer engagement, and seller participation.

• Examine analytical challenges associated with rapidly changing digital products, multi-sided platforms, algorithmic pricing, network effects, and platform-dependent datasets.

• Apply data science techniques to monitor online market indicators, product availability, price dispersion, platform activity, and changes in digital market participation.

• Develop responsible analytical workflows for digital markets that account for data access constraints, measurement limitations, changing platform structures, and the need for reproducible analysis.

Module 13: Predictive Analytics and Machine Learning for Market Surveillance

• Explore supervised and unsupervised machine learning methods relevant to anomaly detection, market classification, firm profiling, price monitoring, and surveillance prioritization.

• Apply feature engineering techniques to transform firm, product, transaction, pricing, market share, and industry information into useful model variables.

• Develop model training, validation, and testing procedures that reduce overfitting, prevent data leakage, and provide meaningful assessments of analytical performance.

• Evaluate model interpretability, stability, bias, monitoring requirements, and human-review processes before incorporating machine learning into regulatory analytical workflows.

Module 14: Dashboards, Visualization, and Regulatory Intelligence

• Design dashboards combining prices, market shares, firm activity, concentration measures, product availability, transactions, and other market surveillance indicators.

• Apply visualization principles for selecting appropriate charts, indicators, filters, comparisons, and drill-down functions for analysts and decision-makers.

• Develop executive and analytical reporting structures that distinguish high-level market indicators from detailed firm, product, transaction, and pricing information.

• Communicate quantitative findings through clear analytical narratives that explain observed patterns, evidence quality, uncertainty, limitations, and areas requiring additional investigation.

Module 15: Data Governance and Responsible Competition Analytics

• Examine confidentiality, privacy, cybersecurity, access controls, data-sharing arrangements, retention, auditability, and governance requirements for competition datasets.

• Develop analytical governance procedures covering model documentation, validation, reproducibility, version control, review, monitoring, and institutional accountability.

• Identify potential analytical bias arising from incomplete market coverage, sampling limitations, inconsistent reporting, historical data gaps, and changing market structures.

• Design responsible data science practices that support transparent methodology, appropriate interpretation, secure information handling, human review, and defensible analytical reporting.

Module 16: Integrated Competition and Market Surveillance Project

• Develop an end-to-end data science project addressing a realistic market surveillance or competition analysis challenge from problem definition through data preparation, analysis, modeling, and reporting.

• Integrate market structure analysis, pricing analytics, firm profiling, transaction analysis, anomaly detection, forecasting, and visualization into a coherent analytical workflow.

• Evaluate the completed solution using technical, economic, governance, reproducibility, and operational criteria while documenting assumptions, limitations, and validation findings.

• Present project findings through an executive-ready dashboard and analytical report that clearly distinguish observed evidence, analytical indicators, uncertainties, and areas requiring further economic or regulatory assessment.

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 a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

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

Airport pickup and 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.