Predictive, Prescriptive Analytics For Business Decision Making from edX

Prescriptive analytics can cut through the clutter of immediate uncertainty and changing conditions. It can help prevent fraud, limit risk, increase efficiency, meet business goals, and create more loyal customers.

Prescriptive analytics is a type of data analytics—the use of technology to help businesses make better decisions through the analysis of raw data. Specifically, prescriptive analytics factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from immediate to long term.

What's inside

Learning objectives

Understand the difference between cross sectional and longitudinal data.
Differentiate between a prediction and forecasting problem scenario and apply these concepts towards data led decision making.
Understand parametric and non parametric modelling approach towards addressing the key tradeoff between predictive accuracy and explain- ability of models.

Use lpp towards building multiple “what if “ scenarios which are widely used in business decision making.
Conceptualize gradient descent algorithm which is a key foundation for most of the widely used machine learning algorithms to be introduced subsequently.

Understand the difference between cross sectional and longitudinal data.
Differentiate between a prediction and forecasting problem scenario and apply these concepts towards data led decision making.
Understand parametric and non parametric modelling approach towards addressing the key tradeoff between predictive accuracy and explain- ability of models.
Use lpp towards building multiple “what if “ scenarios which are widely used in business decision making.
Conceptualize gradient descent algorithm which is a key foundation for most of the widely used machine learning algorithms to be introduced subsequently.

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Explores prescriptive analytics, which helps businesses make better decisions through the analysis of raw data and suggested courses of action or strategy

Uses LPP towards building multiple “What if “ scenarios, which are widely used in business decision making and strategic planning

Conceptualizes Gradient Descent Algorithm, which is a key foundation for most of the widely used Machine learning algorithms

Differentiates between prediction and forecasting problem scenarios, which helps apply these concepts towards data led decision making

Understands Parametric and Non Parametric modelling approach, which helps address the key tradeoff between Predictive accuracy and Explain-ability of models

Reviews summary

Analytics for business decision making

According to learners, this course provides a solid foundation in predictive and prescriptive analytics for business contexts. Students found the coverage of key concepts like Linear Programming (LPP) and Gradient Descent to be valuable for understanding decision-making models. While many appreciated the theoretical depth and the instructor's expertise, some reviewers noted a potential gap in practical application and hands-on exercises, suggesting it might be more theoretical than expected. The course seems best suited for those seeking an overview of analytical concepts relevant to business, potentially requiring supplemental hands-on practice depending on learning goals.

Pace may vary in suitability depending on background.

"For me, as a beginner, the pace felt a bit fast when complex math was introduced."

"I found the course pace manageable, but prior exposure to stats or math would help."

"Some topics felt rushed, while others were covered extensively."

"The jump in difficulty between modules could be challenging."

The instructor is knowledgeable in the field.

"The instructor clearly knows the subject matter very well."

"I appreciated the depth of knowledge demonstrated by the lecturer."

"The explanations were delivered by someone with evident expertise."

"It was clear the concepts were taught by a seasoned professional."

Successfully introduces fundamental techniques like LPP and Gradient Descent.

"The sections on LPP and Gradient Descent were particularly helpful and well-explained."

"It successfully introduced key machine learning fundamentals relevant to prescriptive analytics."

"Understanding LPP was a stated objective, and the course delivered on that effectively."

"I now have a basic grasp of how algorithms like Gradient Descent work."

Provides a strong basis in core analytical concepts.

"The course provided a solid theoretical background in predictive and prescriptive analytics."

"I gained a deep understanding of the underlying principles of LPP and gradient descent."

"The lectures clearly explained the mathematical concepts behind the models."

"It lays a good foundation for understanding how analytics can drive business decisions."

Could benefit from more real-world examples or hands-on.

"While the theory is there, I found myself wishing for more practical case studies and hands-on exercises."

"Needed more real-world applications. It felt a bit too academic at times."

"I would have liked to see more demonstrations of how these models are applied in actual business scenarios."

"The course could use more assignments that involve coding or using software for implementation."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Predictive, Prescriptive Analytics For Business Decision Making with these activities:

Review Basic Statistics and Probability

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Reinforce foundational statistical concepts to better understand the predictive modeling techniques used in prescriptive analytics.

Browse courses on Probability

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Review descriptive statistics (mean, median, standard deviation).
Practice probability calculations (conditional probability, Bayes' theorem).
Familiarize yourself with common probability distributions (normal, binomial).

Review 'Business Analytics: The Science of Data-Driven Decision Making'

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Gain a broader understanding of business analytics and its role in data-driven decision making.

View Putting Knowledge to Work on Amazon

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Read the chapters on predictive and prescriptive analytics.
Focus on the case studies to understand real-world applications.
Take notes on key concepts and techniques.

Follow Tutorials on Linear Programming

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Develop practical skills in using Linear Programming (LPP) to build 'What if' scenarios for business decision making.

Browse courses on Linear Programming

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Find online tutorials on LPP using tools like Excel Solver or Python libraries.
Work through examples of formulating and solving LPP problems.
Apply LPP to a business case study.

Four other activities

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Discuss Gradient Descent with Peers

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Deepen understanding of the Gradient Descent algorithm through collaborative learning and discussion.

Browse courses on Gradient Descent

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Organize a study group with classmates.
Explain the concept of Gradient Descent to each other.
Discuss the challenges and limitations of Gradient Descent.

Review 'Data Science for Business'

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Expand your understanding of data science principles and their application to business problems.

View Data Science for Business on Amazon

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Read the chapters on data mining and data-analytic thinking.
Focus on the examples and case studies.
Reflect on how these principles can be applied to your own work.

Build a Predictive Model for Customer Churn

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Apply predictive modeling techniques to a real-world problem, such as predicting customer churn, to solidify understanding of the course material.

Browse courses on Predictive Modeling

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Gather data on customer behavior and demographics.
Choose a predictive modeling algorithm (e.g., logistic regression, decision tree).
Train and evaluate the model.
Interpret the results and identify key drivers of churn.

Create a Presentation on 'What If' Scenario Analysis

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Develop communication skills by presenting a 'What if' scenario analysis using prescriptive analytics techniques.

Browse courses on Scenario Analysis

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Choose a business problem and define key variables.
Develop multiple scenarios based on different assumptions.
Use LPP or other techniques to analyze the scenarios.
Create a presentation summarizing the findings and recommendations.

Career center

Learners who complete Predictive, Prescriptive Analytics For Business Decision Making will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data scientists use advanced techniques to analyze large datasets, discover patterns, and develop predictive models that drive strategic decision-making. This is a highly analytical role that typically requires a strong background in mathematics and statistics. This course’s focus on prediction and forecasting of business scenarios, as well as parametric and nonparametric modeling, aligns well with the work of a data scientist. The course also introduces important machine learning concepts, such as gradient descent algorithms, which are important techniques in the field. A data scientist would find this course highly relevant to their daily activities.

See salaries and explore the career path for Data Scientist

Business Analyst

A business analyst interprets data to identify trends, patterns, and important insights that can inform business strategy and decision making. Business analysts work to bridge the gap between business needs and technical knowledge, and they provide valuable support to various stakeholders. The course’s focus on applying predictive and prescriptive analytics to data-led decision-making is directly relevant for a business analyst, who often uses these techniques. The course will help you to better understand approaches to modeling and how to apply them to various business scenarios.

See salaries and explore the career path for Business Analyst

Risk Analyst

A risk analyst identifies, assesses, and mitigates potential risks to an organization. Risk analysts often use data-driven approaches to make informed decisions about risk management. This work often involves using predictive models to anticipate potential problems. This course’s focus on prescriptive analytics for decision-making directly applies to the role of a risk analyst, who frequently uses similar techniques. The course’s coverage of prediction and forecasting, as it applies to data led decision making, is directly relevant to the daily tasks of a risk analyst.

See salaries and explore the career path for Risk Analyst

Operations Research Analyst

An operations research analyst uses analytical and mathematical techniques to help organizations make better decisions about operations, logistics, and resource allocation. They develop models to analyze complex problems and recommend optimum strategies. The course's emphasis on using linear programming (LPP) to build 'What if' scenarios is directly relevant to this role, as operations research analysts commonly use this type of modeling. This course also helps build a solid foundation in data led decision making, a crucial aspect for this role.

See salaries and explore the career path for Operations Research Analyst

Market Research Analyst

A market research analyst studies market conditions, consumer behavior, and competitor activities to provide insights for marketing strategy and product development. The data collected by the market research analyst will be used by organizations to make better business decisions. This course’s focus on data-led decision making is central to the role of the market research analyst. The course’s study of prediction and forecasting, as well as the use of linear programming to build 'what if' scenarios, will provide the analyst with the tools necessary to be effective in this role.

See salaries and explore the career path for Market Research Analyst

Supply Chain Analyst

A supply chain analyst optimizes an organization's supply chain operations by analyzing data, forecasting demand, and creating strategies for efficiency and risk management. They utilize analytical tools and models to solve problems and improve performance. The course’s focus on the use of linear programming to build 'What if' scenarios will be of direct use for a supply chain analyst, who must be able to develop and evaluate these scenarios on a regular basis. The course's focus on data driven decision making is also highly applicable to the daily tasks of a supply chain analyst.

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Management Consultant

A management consultant analyzes business problems and offers data-driven strategies to improve performance and achieve organizational goals. This career involves evaluating current practices, identifying areas for improvement, and recommending solutions that leverage data analysis. The concepts covered in this course, such as differentiating between prediction and forecasting and applying these to data-led decision-making will greatly assist a management consultant in creating effective strategies and solving business problems. A strong grasp of data analysis is crucial to this role, making this course a valuable investment.

See salaries and explore the career path for Management Consultant

Product Manager

A product manager guides the development and strategy of products, using data analysis, market research, and customer feedback to inform product decisions. A product manager will often need to make forecasts for product demand, based on changing market conditions. The course’s focus on predictive and prescriptive analytics, as well as its emphasis on data led decision making, will help a product manager make better informed decisions about the product strategies. By learning how to differentiate between prediction and forecasting, the product manager will gain skills applicable to their daily work.

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Financial Analyst

A financial analyst analyzes financial data, creates financial models, and provides recommendations on how to make better investment decisions. This role involves examining past financial performance and also developing forecasts for future performance. The course’s focus on understanding prediction and forecasting problems, as well as its discussion of data led decision making, is directly applicable to the daily tasks of a financial analyst. Learning about parametric and nonparametric modeling can also enhance the analyst’s ability to develop more robust and effective financial models. A financial analyst would find this course extremely useful.

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Pricing Analyst

A pricing analyst determines optimal pricing strategies for products or services by analyzing market data, cost structures, and historical performance. They develop models to predict demand and maximize profitability. Pricing analysts often use predictive and prescriptive analytics. The course's focus on data-led decision making will provide practical methods for this role. Further, the course’s instruction on differentiating between prediction and forecasting will enable a pricing analyst to better manage their tasks. This course provides many valuable tools for a pricing analyst.

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Actuary

An actuary analyzes the financial costs of risk and uncertainty, often in the insurance and finance industries. Actuaries use data analysis, statistical modeling, and mathematical techniques to assess and manage risk. Many actuaries often have advanced degrees in mathematics or statistics. The course’s focus on predictive analytics and its coverage of parametric and nonparametric modeling is directly relevant to the work of an actuary, who frequently uses these methods. An actuary would also be interested in the course’s use of linear programming for decision making. This course will help an actuary better model risk.

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Strategic Planner

A strategic planner helps organizations develop and implement long term goals by analyzing market trends, competitive landscape, and internal capabilities. They often use data to build various scenarios and understand the impact of different strategic choices. The course’s focus on using linear programming (LPP) to build ‘What if’ scenarios will be especially relevant for a strategic planner. This course helps guide a strategic planner through data led decision making, a core aspect of the role, making this course highly useful.

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Business Intelligence Analyst

A business intelligence analyst uses data to provide insights and visualizations to stakeholders, improving overall business processes. This role requires a strong understanding of data modeling. While business intelligence analysts might not create advanced predictive models themselves, they work with and present the output of these models. This course’s discussion of predictive and prescriptive analytics helps provide the analyst with the knowledge needed to work effectively with models. Additionally, the focus of the course on data-led decision making is highly relevant to the role of the analyst. A business intelligence analyst may find this course helpful.

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Logistics Analyst

A logistics analyst optimizes the movement of goods and resources by analyzing data, identifying inefficiencies, and implementing solutions to improve performance and reduce costs. The course’s focus on building “What if” scenarios through linear programming will help a logistics analyst gain a deeper understanding of the impact of decisions on costs and efficiency. This course's focus on data-led decision making will be particularly relevant for a logistics analyst. They may find this course useful in their career.

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Project Manager

A project manager plans, organizes, and directs the completion of specific projects, ensuring they are on time and within budget. This role often involves making strategic decisions based off of data analysis. The course’s focus on the use of linear programming to build ‘what if’ scenarios will be of great help to a project manager. This course may be useful for a project manager who wants to better understand how to use data to inform their decisions.

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Predictive, Prescriptive Analytics For Business Decision Making

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Analytics for business decision making

Activities

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