Master of Business Administration

This module provides students with advanced methods needed to understand how a product will fit into a competitive market, and how to introduce the product into that market. This includes advanced research on the receptiveness of a market to the new product, as well as strategies for defining and finding the market that will be most receptive to the product. Students consider product introductions with the goal of what Marc Andreesen called ‘product/market fit’ - a ‘good market with a product that can satisfy that market.’

The module trains students to reflect upon and analyse a company’s go-to- market strategy, and it provides students with sophisticated methods of calculating customer acquisition cost, determining customer break-even, and calculating customer lifetime value.

This module provides students with advanced methods needed to understand how a product will fit into a competitive market, and how to introduce the product into that market. This includes advanced research on the receptiveness of a market to the new product, as well as strategies for defining and finding the market that will be most receptive to the product. Students consider product introductions with the goal of what Marc Andreesen called ‘product/market fit’ - a ‘good market with a product that can satisfy that market.’

The module trains students to reflect upon and analyse a company’s go-to- market strategy, and it provides students with sophisticated methods of calculating customer acquisition cost, determining customer break-even, and calculating customer lifetime value.

This course is designed to provide students with a comprehensive overview of the key concepts, techniques, and applications of AI. This course covers the history and evolution of AI, fundamental theories, and essential algorithms, including search methods, knowledge representation, machine learning, and neural networks. Students will explore the practical applications of AI in various domains such as robotics, natural language processing, computer vision, and expert systems, gaining an understanding of how AI technologies are transforming industries and society.

Through a mix of theoretical lectures and hands-on exercises, students will develop a solid grounding in AI principles and practices. They will engage in projects and case studies that illustrate real-world AI applications, enhancing their problem-solving and critical-thinking skills. By the end of the course, students will have a thorough understanding of AI fundamentals and be prepared to delve deeper into specialised AI topics, positioning themselves for success in advanced courses and professional roles within the field of artificial intelligence.

This course equips learners with practical growth product management capabilities across the full growth funnel—acquisition, activation/retention, and monetization—using an experimental and data-informed approach. Learners analyze a product’s current “as-is” growth performance and design “to-be” improvements by creating, validating, and scaling growth loops; optimizing sign-up and activation funnels; and applying retention cohort analysis and churn reduction strategies. The course also develops monetization thinking through buyer targeting, path-to-purchase analysis, premium value, pricing metrics/plans, and unit economics—reinforced through applied, real-world projects (e.g., crafting a growth loop, optimizing a sign-up flow, and evaluating SaaS monetization performance).

This course is aimed to build a strong foundational knowledge of Data Analytics tools used extensively in the Data Science field. There now are powerful data visualisation tools used in the business analytics industry to process and visualise raw business data in a very presentable and understandable format. A good example is Tableau, used by all data analytics departments of companies and in data analytics companies in various fields for its ease of use and efficiency. Tableau uses relational databases, Online Analytical Processing Cubes, Spreadsheets, cloud databases to generate graphical type visualisations. Course starts with visualisations and moves to an in-depth look at the different chart and graph functions, calculations, mapping and other functionality. Students will be taught quick table calculations, reference lines, different types of visualisations, bands and distributions, parameters, motion chart, trends and forecasting, formatting, stories, performance recording and advanced mapping. At the end of this course, students will be prepared, if they desire, to earn such industry desktop certifications as a Tableau Desktop Specialist, a Tableau Certified Associate, or a Tableau Certified Professional.

This module is designed to achieve an understanding of fundamental notions of data presentation and analysis and to use statistical thinking in the context of business problems.

The module addresses modern methods of data exploration (designed to reveal unusual or problematic aspects of databases), the uses and abuses of the basic techniques of inference, and the use of regression as a tool for management and for financial analysis. The potential ethical issues related to each topic will be reviewed. Socially and environmentally relevant data will be utilised throughout the module.

The goal of the module is not to turn students into statisticians but to enable them to appreciate the use of probability in assessing evidence and making decisions, and to be statistically literate consumers of quantitative information generated by economists, biomedical researchers, psychologists, statisticians, survey researchers, and other experts.

This course equips learners with practical growth product management capabilities across the full growth funnel—acquisition, activation/retention, and monetization—using an experimental and data-informed approach. Learners analyze a product’s current “as-is” growth performance and design “to-be” improvements by creating, validating, and scaling growth loops; optimizing sign-up and activation funnels; and applying retention cohort analysis and churn reduction strategies. The course also develops monetization thinking through buyer targeting, path-to-purchase analysis, premium value, pricing metrics/plans, and unit economics—reinforced through applied, real-world projects (e.g., crafting a growth loop, optimizing a sign-up flow, and evaluating SaaS monetization performance).

This course builds job-ready digital marketing skills through hands-on practice and expert guidance. Learners will run live campaigns, analyze performance, and apply proven strategies to engage audiences and strengthen brand presence. Topics include social media advertising, content planning, audience targeting, and campaign evaluation—preparing students for real-world digital marketing roles.

This course introduces the principles and practices of Agile software development. Learners develop practical skills in Agile methodologies, sprint planning, and iterative improvement, while gaining experience with team communication, metrics, and real-world Agile workflows.

This module examines how leaders can most effectively use the resources of their team members to achieve business outcomes. The module develops managerial and leadership competences, focussing on how key improvements in the general strategy and techniques of managing people can produce outcomes more significant than isolated improvements to employee performance.

The module provides students with concepts to support them across their careers as they continue to develop effective delegation, management strategy, and engagement with people inside of an organisation.

This course teaches learners how to use data to shape and optimize product strategy. Students will build data pipelines, analyze key product metrics, and apply data-driven insights to guide product innovation and drive successful outcomes.

This course builds job-ready digital marketing skills through hands-on practice and expert guidance. Learners will run live campaigns, analyze performance, and apply proven strategies to engage audiences and strengthen brand presence. Topics include social media advertising, content planning, audience targeting, and campaign evaluation—preparing students for real-world digital marketing roles.

This module examines how leaders can most effectively use the resources of their team members to achieve business outcomes. The module develops managerial and leadership competences, focussing on how key improvements in the general strategy and techniques of managing people can produce outcomes more significant than isolated improvements to employee performance.

The module provides students with concepts to support them across their careers as they continue to develop effective delegation, management strategy, and engagement with people inside of an organisation.

This course is aimed at providing students with a comprehensive understanding of how to design and implement systems that exhibit intelligent behaviour. This course explores a range of topics including expert systems, autonomous agents, knowledge representation, and reasoning. Students will delve into the principles of how these systems can mimic human decision-making processes, adapt to changing environments, and perform complex tasks autonomously.

The course integrates theoretical concepts with practical applications through hands-on projects and case studies, allowing students to develop and deploy intelligent systems in real-world scenarios. By working with advanced tools and techniques, students will learn to build systems that can handle uncertainty, learn from experience, and interact effectively with users and other systems. Upon completion, students will be equipped with the skills to create sophisticated AI solutions and contribute to the development of cutting-edge intelligent technologies in their professional careers.

This course builds essential skills in data analysis and business analytics. Learners will use Excel, SQL, and Power BI to collect, clean, model, and visualize data, developing the ability to generate clear insights that support effective decision-making.

This course is designed to equip students with the skills to harness AI technologies for enhanced decision-making processes. This course explores the integration of AI techniques, such as predictive analytics, decision trees, reinforcement learning, and optimization algorithms, to support and improve decision-making in various contexts. Students will learn how to develop and implement AI-driven models that can analyse complex data, predict outcomes, and provide actionable insights to inform strategic decisions in business, healthcare, finance, and other sectors.

Through a blend of theoretical knowledge and practical applications, students will engage in projects and case studies that illustrate the power of AI in transforming decision-making practices. They will gain hands-on experience with tools and methodologies used to build intelligent decision support systems, ensuring they can apply these skills to real-world challenges. By the end of the course, students will be adept at creating AI solutions that enhance decision-making capabilities, positioning themselves as valuable assets in any organisation seeking to leverage AI for competitive advantage.

This course equips learners with in-demand skills in data pre-processing, visualization, and analysis using Power BI. Students gain hands-on experience with Data transformation, Power Query, DAX, and Power BI Report Design while developing strong Data visualization design and Power BI Report Customization capabilities to build effective analytical dashboards.

The Digital Action Programme for Business Administration provides a capstone course in which students deepen and apply their learning through a 'Digital Action Programme' (DAP). In the DAP, students are grouped into cohorts (typically five students) and must work both individually and together on a specific, real, contemporary business consultancy problem related to their specialisation (Data Analytics; Marketing; Finance; International Business; DEI), normally proposed by a cooperating organisation (corporation or non-profit), which results in a comprehensive solution proposal. This provides students with a real-world business consultancy engagement, and the opportunity to produce, both individually and as a team, a substantial piece of relevant, scholarly, and actionable research, to be presented directly to stakeholders in the cooperating organisation. Over the course of the DAP, students fulfil the learning objectives: each student demonstrates their comprehensive knowledge and understanding of key business processes; each student uses multidisciplinary approaches to perform critical analyses of real business issues in situations of uncertainty and incomplete information in order to develop an actionable solution; each student practises teamwork, exercises their leadership skills, and reflects on their own performance and the performance of their cohort; and each student communicates to members of their cohort, the cooperating organisation, and faculty members from Woolf. Students are required to demonstrate autonomy, individual scholarly acumen, self-reflection in their engagement with peers, role adaptability within their cohort, and teamwork while engaged in the DAP. The goal of the DAP is (1) to fulfil the learning objectives and (2) to produce a project portfolio related to the area of specialisation containing an analysis of the business problem and the proposed solution. DAP Roles and Responsibilities (a)Individual students Students are required to take responsibility for their own work, they must act autonomously on the basis of their prior learning and experience, and they must individually generate key research results that contribute to the DAP. Each student must individually contribute through assignment submissions, which are marked on their individual merits. The final mark on the course (as described below) consists of 50% for the individual research submissions, and 50% for the cohort's final project taken as a whole. The final project contains individual contributions related to the student’s specialisation, but requires teamwork, and is graded as a whole in terms of its fulfilment of the learning objectives. Thus 15 ECTS worth of the course is based on individual work, and 15 ECTS is based on the collaborative work of the Cohort. (b)Cohorts Cohorts are groups of about 5 students that are assigned to address a single business problem, on which they commit to working both individually and as a team. Cohort members are selected based on their area of specialisation. All cohorts must agree to a cohort charter, which outlines the roles and responsibilities of the team. The cohort charter must include the following topics: timeliness; comprehensive designation of areas of responsibility, including gathering meeting agenda items, chairing meetings, meeting note-taking, and being the point of contact for the cooperating organisation; a schedule of rotating leadership positions across the modules units, and a commitment to professional teamwork that prioritises the goal of the DAP. Cohorts are encouraged to address issues that arise within the group together. However, should intervention be necessary, their Woolf teacher will be available to resolve any problems or conflicts. (c)Teachers All cohorts are assigned a Woolf teacher to facilitate three cohort tutorials for each unit, and all cohorts are assigned a designated contact person from a cooperating organisation. The role of the teacher in cohort tutorials is to ensure that students are achieving the learning objectives and that the cohort is on course with their program roadmap. As the DAP progresses, students are expected to increase their management over the tutorial meetings, including setting the meeting agenda. (d)Cooperating organisations Cooperating organisations must register and be verified with Woolf, provide an initial portfolio of basic information on the company, provide a designated contact person, and agree to the standard 'cooperating organisation framework' –which commits them to attend a minimum number of meetings with a cohort, and they are encouraged to provide students with access to the executive members of their organisation. Additionally, it is expected that cooperating organisations provide an environment where students can engage with a variety of employees and departments where collaboration and communication are used to complete business tasks. Students will work with the cooperating organisation on a relevant and specific, real, contemporary business consultancy problem. As such, the organisation should offer support when needed and provide a supervisor what is in direct contact with the student and Woolf faculty members. At the conclusion of the experience, the supervisor will provide a report to Woolf faculty addressing the outcome of the project and if the consultancy problem was resolved. In cases where relations with a cooperating organisation become untenable for any reason, and the cohort is unable to continue with the relationship, then cohorts will be provided with the choice of (a) continuing their DAP without further input from the cooperating organisation, (b) switching to a new cooperating organisation, or (c) selecting a contemporary business problem on the basis of publicly available information and in agreement with their teacher. DAP Timeline of Assignments Each unit of the module normally requires about 75 hours of work from each member of the cohort. Individuals must complete their projects on schedule –neither early nor late –and in response to the requirements of their project; cohorts have the opportunity to adjust the amount of time dedicated to each unit. The cohort meetings are an opportunity for the instructor to check in on the team's progress; they are a key checkpoint for individual submissions, and they provide milestones in the progress of the DAP. Before every cohort meeting, each student is required to submit a status report on individual and team performance. At the end of the DAP, every cohort submits a Final Report, Final Presentation, and Final Reflection on their experience. The Final Report consists of the following components:

1. Title, abstract, and table of contents

2. Industry and competition report

3. Report on the cooperating organisation

4. Report on the business problem

5. Report on the potential solutions analysing their merits and weakness

6. Recommended solution with an implementation plan

7. Full financial model

8. Bibliography Items 2-7 (which may be adjusted in coordination with the cohort teacher), each have a Directly Responsible Individual (the DRI), who undertakes all the research for the section of the Final Report. Each DRI must elicit feedback and review from other members of the cohort, who must contribute feedback to every other section of the report. The Final Presentation is typically a slide deck between 20 and 40 slides, and it is a fully collaborative project. The Final Reflection is a reflective analysis on the DAP experience, and it must contain an individual report from each member and a joint concluding statement. The course concludes with each member providing a peer review of their cohort peers, including strengths and areas of improvement. The timeline of the course assignments is set by the cohort at the start, and adjusted in consultation with the teacher as the DAP progresses. The outline of assignment submissions is as follows: Unit 1 ●Standard cohort charter discussed, revised, and agreed ●Project timeline with designated areas of responsibility Unit 2-3 ●Draft title and abstract for the final report ●Industry information gathering ●Draft report on the cooperating organisation ●Draft report on the industry landscape Unit 4-5 ●Problem and opportunity diagnose ●Creative generation of varied potential solutions Unit 6 ●Evaluation of potential solutions ●Preliminary financial models of potential solutions Unit 7-8 ●Recommended solution ●Implementation plan Unit 9-10 ●Final Report ●Final Presentation ●Final Reflection and cohort debrief ●Peer evaluation

This course helps students translate advanced mathematical/statistical/scientific concepts into code. This is a module for writing code to solve real-world problems. It introduces programming concepts (such as control structures, recursion, classes and objects) assuming no prior programming knowledge, to make this course accessible to advanced professionals from scientific fields like Biology, Physics, Medicine, Chemistry, Civil & Mechanical Engineering etc.  After building a strong foundation for converting scientific knowledge into programming concepts, the course advances to dive deeply into Object-Oriented Programming and its methodologies. It also covers when and how to use inbuilt-data structures like 1-Dimensional and 2-Dimensional Arrays before introducing the concepts of computational complexity to help students write optimised code using appropriate data structures and algorithmic design methods.

This course is dedicated to equipping students with the skills to develop and apply models that forecast future trends and behaviours based on historical data. This course covers a range of predictive techniques, including linear and logistic regression, time series analysis, ensemble methods, and advanced machine learning algorithms. Students will learn how to build, validate, and deploy predictive models to make accurate forecasts in various domains such as finance, healthcare, and marketing. The course emphasises both theoretical understanding and practical application, with students engaging in hands-on projects and case studies that demonstrate real-world uses of predictive modelling. By working with diverse datasets and employing state-of-the-art tools, students will gain experience in model selection, performance evaluation, and optimization. By the end of the course, students will be adept at creating robust predictive models that drive data-informed decision-making and contribute to strategic planning in their professional fields.

This course teaches learners how to track, analyze, and interpret user data using Google Analytics 4 (GA4). Students will uncover meaningful insights, optimize user experiences, and support data-driven decision-making through hands-on GA4 practice.

This is a core and foundational course which aims to equip the student with the ability to model, design, implement and query relational database systems for real-world data storage & processing needs. Students would start with diagrammatic tools (ER-diagram) to map a real world data storage problem into entities, relationships and keys. Then, they learn to translate the ER-diagram into a relational model with tables. SQL is then introduced as a de facto tool to create, modify, append, delete, query and manipulate data in a relational database. Due to SQL’s popularity, the course spends considerable time building the ability to write optimised and complex queries for various data manipulation tasks. The module exposes students to various real world SQL examples to build solid practical knowledge. Students then move on to understanding various trade-offs in modern relational databases like the ones between storage space and latency. Designing a database would need a solid understanding of normal forms to minimise data duplication, indexing for speedup and flattening tables to avoid complex joins in low-latency environments. These real-world database design strategies are discussed with practical examples from various domains. Most of this course uses the open source MySQL database and cloud-hosted relational databases (like Amazon RDS) to help students apply the concepts learned on real databases via assignments.

This course is designed to provide students with a comprehensive overview of the key concepts, techniques, and applications of AI. This course covers the history and evolution of AI, fundamental theories, and essential algorithms, including search methods, knowledge representation, machine learning, and neural networks. Students will explore the practical applications of AI in various domains such as robotics, natural language processing, computer vision, and expert systems, gaining an understanding of how AI technologies are transforming industries and society.

Through a mix of theoretical lectures and hands-on exercises, students will develop a solid grounding in AI principles and practices. They will engage in projects and case studies that illustrate real-world AI applications, enhancing their problem-solving and critical-thinking skills. By the end of the course, students will have a thorough understanding of AI fundamentals and be prepared to delve deeper into specialised AI topics, positioning themselves for success in advanced courses and professional roles within the field of artificial intelligence.

This course is aimed to build a strong foundational knowledge of Data Analytics tools used extensively in the Data Science field. There now are powerful data visualisation tools used in the business analytics industry to process and visualise raw business data in a very presentable and understandable format. A good example is Tableau, used by all data analytics departments of companies and in data analytics companies in various fields for its ease of use and efficiency. Tableau uses relational databases, Online Analytical Processing Cubes, Spreadsheets, cloud databases to generate graphical type visualisations. Course starts with visualisations and moves to an in-depth look at the different chart and graph functions, calculations, mapping and other functionality. Students will be taught quick table calculations, reference lines, different types of visualisations, bands and distributions, parameters, motion chart, trends and forecasting, formatting, stories, performance recording and advanced mapping. At the end of this course, students will be prepared, if they desire, to earn such industry desktop certifications as a Tableau Desktop Specialist, a Tableau Certified Associate, or a Tableau Certified Professional.

This course introduces the principles and practices of Agile software development. Learners develop practical skills in Agile methodologies, sprint planning, and iterative improvement, while gaining experience with team communication, metrics, and real-world Agile workflows.

This course teaches learners how to use data to shape and optimize product strategy. Students will build data pipelines, analyze key product metrics, and apply data-driven insights to guide product innovation and drive successful outcomes.

This course teaches learners to transform marketing data into actionable insights. Students will collect, analyze, and model data, then communicate findings using Excel and Tableau to support informed business and marketing decisions.

This course builds essential skills in data analysis and business analytics. Learners will use Excel, SQL, and Power BI to collect, clean, model, and visualize data, developing the ability to generate clear insights that support effective decision-making.

This course is designed to equip students with the skills to harness AI technologies for enhanced decision-making processes. This course explores the integration of AI techniques, such as predictive analytics, decision trees, reinforcement learning, and optimization algorithms, to support and improve decision-making in various contexts. Students will learn how to develop and implement AI-driven models that can analyse complex data, predict outcomes, and provide actionable insights to inform strategic decisions in business, healthcare, finance, and other sectors.

Through a blend of theoretical knowledge and practical applications, students will engage in projects and case studies that illustrate the power of AI in transforming decision-making practices. They will gain hands-on experience with tools and methodologies used to build intelligent decision support systems, ensuring they can apply these skills to real-world challenges. By the end of the course, students will be adept at creating AI solutions that enhance decision-making capabilities, positioning themselves as valuable assets in any organisation seeking to leverage AI for competitive advantage.

This course equips learners with in-demand skills in data pre-processing, visualization, and analysis using Power BI. Students gain hands-on experience with Data transformation, Power Query, DAX, and Power BI Report Design while developing strong Data visualization design and Power BI Report Customization capabilities to build effective analytical dashboards.

This course is aimed at providing students with a comprehensive understanding of how to design and implement systems that exhibit intelligent behaviour. This course explores a range of topics including expert systems, autonomous agents, knowledge representation, and reasoning. Students will delve into the principles of how these systems can mimic human decision-making processes, adapt to changing environments, and perform complex tasks autonomously.

The course integrates theoretical concepts with practical applications through hands-on projects and case studies, allowing students to develop and deploy intelligent systems in real-world scenarios. By working with advanced tools and techniques, students will learn to build systems that can handle uncertainty, learn from experience, and interact effectively with users and other systems. Upon completion, students will be equipped with the skills to create sophisticated AI solutions and contribute to the development of cutting-edge intelligent technologies in their professional careers.

The Digital Action Programme for Business Administration provides a capstone course in which students deepen and apply their learning through a 'Digital Action Programme' (DAP). In the DAP, students are grouped into cohorts (typically five students) and must work both individually and together on a specific, real, contemporary business consultancy problem related to their specialisation (Data Analytics; Marketing; Finance; International Business; DEI), normally proposed by a cooperating organisation (corporation or non-profit), which results in a comprehensive solution proposal. This provides students with a real-world business consultancy engagement, and the opportunity to produce, both individually and as a team, a substantial piece of relevant, scholarly, and actionable research, to be presented directly to stakeholders in the cooperating organisation. Over the course of the DAP, students fulfil the learning objectives: each student demonstrates their comprehensive knowledge and understanding of key business processes; each student uses multidisciplinary approaches to perform critical analyses of real business issues in situations of uncertainty and incomplete information in order to develop an actionable solution; each student practises teamwork, exercises their leadership skills, and reflects on their own performance and the performance of their cohort; and each student communicates to members of their cohort, the cooperating organisation, and faculty members from Woolf. Students are required to demonstrate autonomy, individual scholarly acumen, self-reflection in their engagement with peers, role adaptability within their cohort, and teamwork while engaged in the DAP. The goal of the DAP is (1) to fulfil the learning objectives and (2) to produce a project portfolio related to the area of specialisation containing an analysis of the business problem and the proposed solution. DAP Roles and Responsibilities (a)Individual students Students are required to take responsibility for their own work, they must act autonomously on the basis of their prior learning and experience, and they must individually generate key research results that contribute to the DAP. Each student must individually contribute through assignment submissions, which are marked on their individual merits. The final mark on the course (as described below) consists of 50% for the individual research submissions, and 50% for the cohort's final project taken as a whole. The final project contains individual contributions related to the student’s specialisation, but requires teamwork, and is graded as a whole in terms of its fulfilment of the learning objectives. Thus 15 ECTS worth of the course is based on individual work, and 15 ECTS is based on the collaborative work of the Cohort. (b)Cohorts Cohorts are groups of about 5 students that are assigned to address a single business problem, on which they commit to working both individually and as a team. Cohort members are selected based on their area of specialisation. All cohorts must agree to a cohort charter, which outlines the roles and responsibilities of the team. The cohort charter must include the following topics: timeliness; comprehensive designation of areas of responsibility, including gathering meeting agenda items, chairing meetings, meeting note-taking, and being the point of contact for the cooperating organisation; a schedule of rotating leadership positions across the modules units, and a commitment to professional teamwork that prioritises the goal of the DAP. Cohorts are encouraged to address issues that arise within the group together. However, should intervention be necessary, their Woolf teacher will be available to resolve any problems or conflicts. (c)Teachers All cohorts are assigned a Woolf teacher to facilitate three cohort tutorials for each unit, and all cohorts are assigned a designated contact person from a cooperating organisation. The role of the teacher in cohort tutorials is to ensure that students are achieving the learning objectives and that the cohort is on course with their program roadmap. As the DAP progresses, students are expected to increase their management over the tutorial meetings, including setting the meeting agenda. (d)Cooperating organisations Cooperating organisations must register and be verified with Woolf, provide an initial portfolio of basic information on the company, provide a designated contact person, and agree to the standard 'cooperating organisation framework' –which commits them to attend a minimum number of meetings with a cohort, and they are encouraged to provide students with access to the executive members of their organisation. Additionally, it is expected that cooperating organisations provide an environment where students can engage with a variety of employees and departments where collaboration and communication are used to complete business tasks. Students will work with the cooperating organisation on a relevant and specific, real, contemporary business consultancy problem. As such, the organisation should offer support when needed and provide a supervisor what is in direct contact with the student and Woolf faculty members. At the conclusion of the experience, the supervisor will provide a report to Woolf faculty addressing the outcome of the project and if the consultancy problem was resolved. In cases where relations with a cooperating organisation become untenable for any reason, and the cohort is unable to continue with the relationship, then cohorts will be provided with the choice of (a) continuing their DAP without further input from the cooperating organisation, (b) switching to a new cooperating organisation, or (c) selecting a contemporary business problem on the basis of publicly available information and in agreement with their teacher. DAP Timeline of Assignments Each unit of the module normally requires about 75 hours of work from each member of the cohort. Individuals must complete their projects on schedule –neither early nor late –and in response to the requirements of their project; cohorts have the opportunity to adjust the amount of time dedicated to each unit. The cohort meetings are an opportunity for the instructor to check in on the team's progress; they are a key checkpoint for individual submissions, and they provide milestones in the progress of the DAP. Before every cohort meeting, each student is required to submit a status report on individual and team performance. At the end of the DAP, every cohort submits a Final Report, Final Presentation, and Final Reflection on their experience. The Final Report consists of the following components:

1. Title, abstract, and table of contents

2. Industry and competition report

3. Report on the cooperating organisation

4. Report on the business problem

5. Report on the potential solutions analysing their merits and weakness

6. Recommended solution with an implementation plan

7. Full financial model

8. Bibliography Items 2-7 (which may be adjusted in coordination with the cohort teacher), each have a Directly Responsible Individual (the DRI), who undertakes all the research for the section of the Final Report. Each DRI must elicit feedback and review from other members of the cohort, who must contribute feedback to every other section of the report. The Final Presentation is typically a slide deck between 20 and 40 slides, and it is a fully collaborative project. The Final Reflection is a reflective analysis on the DAP experience, and it must contain an individual report from each member and a joint concluding statement. The course concludes with each member providing a peer review of their cohort peers, including strengths and areas of improvement. The timeline of the course assignments is set by the cohort at the start, and adjusted in consultation with the teacher as the DAP progresses. The outline of assignment submissions is as follows: Unit 1 ●Standard cohort charter discussed, revised, and agreed ●Project timeline with designated areas of responsibility Unit 2-3 ●Draft title and abstract for the final report ●Industry information gathering ●Draft report on the cooperating organisation ●Draft report on the industry landscape Unit 4-5 ●Problem and opportunity diagnose ●Creative generation of varied potential solutions Unit 6 ●Evaluation of potential solutions ●Preliminary financial models of potential solutions Unit 7-8 ●Recommended solution ●Implementation plan Unit 9-10 ●Final Report ●Final Presentation ●Final Reflection and cohort debrief ●Peer evaluation