Course Introduction
Competition and antitrust authorities increasingly rely on data analytics to understand market structures, assess competitive conditions, detect potentially harmful conduct, and support evidence-based regulatory decisions. The Competition and Antitrust Data Analytics Training Course equips participants with practical analytical capabilities for transforming market, pricing, transaction, firm, consumer, and regulatory data into actionable intelligence for competition assessment, market monitoring, investigations, and policy development.
Competition-related datasets can include prices, market shares, sales volumes, firm characteristics, procurement information, transaction records, customer behaviour, product characteristics, import and export data, complaints, and industry indicators. Participants will learn how to assess data quality, structure complex datasets, integrate information from multiple sources, identify anomalies, and prepare evidence for rigorous analysis. The course emphasizes practical techniques for descriptive statistics, market segmentation, benchmarking, trend analysis, and visualization.
The course examines how analytics can support competition analysis across market definition, concentration assessment, entry conditions, pricing behaviour, mergers and acquisitions, vertical relationships, procurement markets, and potential restrictive business practices. Participants will explore analytical methods for identifying unusual pricing patterns, concentration trends, coordinated behaviour indicators, exclusionary risks, and other signals that may warrant further investigation. Emphasis will be placed on distinguishing analytical indicators from definitive legal or economic conclusions.
Effective competition enforcement requires analytical frameworks that combine quantitative evidence with economic reasoning, legal standards, institutional context, and reliable documentation. Participants will learn how to develop indicators, dashboards, market-monitoring tools, and analytical reports that support authorized investigators, economists, legal teams, and decision-makers. The course also examines how data visualization and business intelligence can make complex market evidence easier to interpret and communicate.
Emerging technologies are creating new opportunities and challenges for competition authorities through artificial intelligence, algorithmic pricing, automated decision-making, digital platforms, big-data business models, online marketplaces, and computational market monitoring. The course explores analytical approaches to these developments, including algorithmic pricing risks, platform economics, digital market concentration, data-driven competitive advantages, and automated market surveillance. Participants will also consider data governance, confidentiality, cybersecurity, model limitations, and responsible use of advanced analytics.
By the end of the course, participants will be able to apply practical data analytics techniques to competition and antitrust datasets for market studies, screening, investigation support, merger analysis, market monitoring, and institutional reporting. Through practical exercises and an integrated project, participants will strengthen their ability to clean and analyze market data, assess competitive indicators, develop visual intelligence products, identify patterns requiring further examination, and communicate analytical findings clearly to authorized economic, legal, regulatory, and policy audiences.
Duration
5 days
Who Should Attend
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Competition authority commissioners, directors, managers, and senior enforcement officials.
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Competition economists and economic analysts conducting market and competition assessments.
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Antitrust investigators responsible for screening markets, firms, transactions, and business conduct.
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Regulatory economists and policy analysts supporting competition policy and market studies.
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Data analysts, statisticians, and business intelligence specialists working with competition datasets.
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Legal and enforcement professionals supporting antitrust investigations and competition cases.
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Market surveillance officers monitoring prices, market shares, business conduct, and industry developments.
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Merger review specialists analyzing transactions, market structures, and competitive effects.
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Procurement and public-sector competition specialists examining tender markets and bid-related data.
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Consumer protection and market conduct professionals working on overlapping competition and consumer issues.
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ICT and database professionals supporting competition information systems and analytical platforms.
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Monitoring and evaluation professionals assessing competition programmes, market interventions, and regulatory performance.
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Researchers and academics conducting empirical competition, industrial organization, and market studies.
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Consultants and development professionals supporting competition authorities, regulators, and economic policy institutions.
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Professionals involved in digital markets, platform regulation, algorithmic pricing, and emerging competition-policy issues.
Course Objectives
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Develop practical skills for collecting, cleaning, integrating, validating, and analyzing market and competition datasets from multiple authorized sources.
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Apply statistical and economic analytics to assess market structures, concentration, pricing patterns, firm behaviour, demand conditions, and competitive dynamics.
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Analyze market-share, pricing, sales, transaction, and firm-level information to identify patterns requiring further competition assessment or investigation.
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Use spreadsheets, databases, statistical tools, dashboards, and business intelligence platforms to transform complex market data into analytical evidence.
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Develop meaningful indicators for market concentration, competitive conditions, entry barriers, pricing behaviour, market performance, and regulatory monitoring.
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Apply quantitative techniques to support market definition, merger screening, market studies, procurement analysis, and assessment of potentially restrictive business conduct.
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Strengthen analytical approaches for identifying unusual patterns, structural risks, coordinated-behaviour indicators, and other signals requiring expert economic and legal review.
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Explore artificial intelligence, algorithmic pricing, digital platforms, predictive analytics, big-data markets, and emerging computational approaches to competition monitoring.
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Strengthen knowledge of competition-data governance, confidentiality, privacy, cybersecurity, reproducibility, responsible analytics, and appropriate handling of commercially sensitive information.
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Translate complex competition datasets into clear dashboards, market intelligence reports, analytical evidence, and documented findings that support authorized regulatory and policy decisions.
Comprehensive Course Outline
Module 1: Foundations of Competition and Antitrust Data Analytics
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Understanding the role of data analytics in competition enforcement, market monitoring, antitrust investigations, economic analysis, and evidence-based regulatory decision-making.
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Examining competition data ecosystems, including firms, prices, market shares, transactions, consumers, procurement, imports, exports, and industry-level information.
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Understanding fundamental statistical and economic concepts including distributions, correlations, trends, concentration, benchmarking, elasticity, and market-performance indicators.
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Developing a competition analytics framework linking regulatory objectives, economic questions, datasets, analytical methods, indicators, evidence, and authorized decision-making.
Module 2: Competition Data Collection, Quality, Integration, and Management
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Designing reliable approaches for collecting firm-level, product-level, transaction, pricing, procurement, market-share, and industry information from authorized sources.
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Identifying data-quality challenges involving missing observations, inconsistent classifications, duplicate records, outliers, measurement differences, and incompatible reporting periods.
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Integrating competition datasets from regulatory filings, surveys, market studies, administrative records, public sources, and authorized commercial information.
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Developing data dictionaries, metadata standards, validation rules, reproducible workflows, documentation procedures, and quality-assurance frameworks for competition analysis.
Module 3: Spreadsheet, Database, Statistical, and Business Intelligence Analytics
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Applying advanced spreadsheet techniques for market data cleaning, aggregation, pivot analysis, benchmarking, trend analysis, scenario analysis, and analytical reporting.
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Understanding relational databases, structured queries, joins, filters, relationships, and extraction techniques for large competition and market datasets.
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Applying statistical methods for descriptive analysis, correlation, regression, distribution analysis, outlier identification, and interpretation of market indicators.
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Developing interactive dashboards that combine prices, market shares, concentration measures, firm characteristics, transaction data, and market-performance indicators.
Module 4: Market Structure, Market Definition, and Concentration Analytics
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Analyzing market structure using market shares, concentration measures, firm distributions, product characteristics, geographic dimensions, and competitive conditions.
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Applying quantitative techniques to support market-definition analysis while recognizing the importance of legal, economic, and contextual considerations.
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Assessing concentration trends, firm entry and exit, market expansion, structural changes, and competitive dynamics across relevant markets.
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Developing market-structure dashboards and analytical reports that clearly distinguish descriptive evidence from economic interpretation and regulatory conclusions.
Module 5: Pricing, Demand, and Competitive Behaviour Analytics
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Analyzing price levels, price changes, discounts, margins, sales volumes, product characteristics, and other variables relevant to competitive market behaviour.
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Examining pricing dispersion, price movements, correlations, cost-related indicators, demand patterns, and market responses across firms and products.
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Applying analytical techniques to identify unusual pricing patterns, parallel movements, sudden changes, or other signals that may warrant further investigation.
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Developing pricing and market-performance indicators that support evidence-based screening while avoiding unsupported conclusions from statistical patterns alone.
Module 6: Merger, Procurement, and Business Conduct Analytics
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Applying quantitative analytics to support merger screening, transaction analysis, market-share assessment, competitive overlap analysis, and examination of relevant market conditions.
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Analyzing procurement and tender datasets to identify patterns in participation, bidding, prices, suppliers, awards, concentration, and market access.
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Examining vertical relationships, distribution structures, exclusivity indicators, switching conditions, and other business relationships through appropriate analytical frameworks.
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Developing investigation-support dashboards that combine firm, market, transaction, procurement, and conduct indicators for authorized economic and legal review.
Module 7: Geographic, Sectoral, Risk, and Market Intelligence Analytics
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Applying GIS and geographic analytics to understand regional market structures, supplier distribution, service coverage, trade flows, and geographic dimensions of competition.
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Developing competition-risk indicators using market concentration, pricing, firm behaviour, entry conditions, procurement patterns, and other relevant market signals.
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Applying comparative and trend analysis across industries, firms, products, geographic markets, and time periods to identify emerging competitive concerns.
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Creating market intelligence products that support sector prioritization, market studies, surveillance planning, investigation screening, and competition-policy research.
Module 8: AI, Algorithmic Pricing, Digital Markets, and Emerging Competition Issues
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Exploring artificial intelligence, machine learning, automated market monitoring, natural language processing, and predictive analytics for competition intelligence.
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Examining algorithmic pricing, automated decision-making, platform markets, online marketplaces, digital ecosystems, and data-driven business models as emerging competition issues.
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Applying analytical methods to assess digital market concentration, platform activity, switching behaviour, network effects, multi-sided markets, and data-related competitive dynamics.
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Evaluating algorithmic opacity, model limitations, false positives, data dependencies, explainability, cybersecurity, and responsible use of AI in competition analysis.
Module 9: Competition Data Governance, Confidentiality, Cybersecurity, and Responsible Analytics
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Understanding requirements for protecting commercially sensitive information, confidential business records, personal data, regulatory evidence, and investigation materials.
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Examining cybersecurity risks affecting competition databases, regulatory filing systems, market-monitoring platforms, analytical environments, and information-sharing mechanisms.
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Developing governance frameworks covering data ownership, confidentiality, access controls, retention, auditability, reproducibility, documentation, and authorized information sharing.
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Applying responsible analytical practices that distinguish statistical signals from conclusions, document methodologies, communicate uncertainty, and preserve analytical integrity.
Module 10: Integrated Competition and Antitrust Analytics Project
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Designing an integrated competition analytics project combining firm, pricing, market-share, transaction, procurement, geographic, and industry datasets.
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Cleaning, analyzing, and visualizing a realistic market dataset to identify structural trends, pricing patterns, concentration indicators, and areas requiring further examination.
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Developing a practical competition dashboard and analytical report containing market indicators, visualizations, methodological notes, data-quality findings, and carefully interpreted results.
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Presenting evidence-based analytical findings and an investigation-support action plan demonstrating how data analytics can strengthen market monitoring, competition assessment, and regulatory intelligence.
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.
