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William A. Brantley

Every modern organization is a digital organization or will rapidly become digital. Artificial intelligence, Google/Amazon/Facebook/Uber, and big data have dramatically raised customer expectations and demand.

Organizations that are effective in using data will win in the economies of the mid-21st century. These must-have core competencies include data analysis, machine learning, data visualizations, data mining, and predictive analytics, and deep learning. Organizations that won't or can't digitally transform will go the way of Blockbuster or Border's Bookstore.

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Every modern organization is a digital organization or will rapidly become digital. Artificial intelligence, Google/Amazon/Facebook/Uber, and big data have dramatically raised customer expectations and demand.

Organizations that are effective in using data will win in the economies of the mid-21st century. These must-have core competencies include data analysis, machine learning, data visualizations, data mining, and predictive analytics, and deep learning. Organizations that won't or can't digitally transform will go the way of Blockbuster or Border's Bookstore.

The organization that better harnesses the power of data to create a superior customer experience will thrive in the new business realities.

The question is, how does an organization digitally transform? There are many digital technologies for organizations to choose from - too many choices! And digital technologies are only part of creating a digital organization. The employees must be trained in the new technologies, leaders must learn how to use data in making strategic decisions, and the organization's business processes must be reinvented. So many choices to make and the stakes have never been higher!

This course will give you a framework to help you successfully navigate the challenges posed by digital transformation. First, we will discuss how to use the organization's dynamic capabilities to start the digital transformation. Second, we will use fitness landscapes to build a competitive digital business model. Finally, we will implement a strategic foresight function to help evolve the digital business model for the organization's continued success.

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What's inside

Syllabus

Week 1: The Digital Enterprise
Week 2: Dynamic Capabilities
Week 3: Fitness Landscapes
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Explores strategies for digital transformation in various industries, such as finance, healthcare, and retail
Examines the impact of artificial intelligence, big data, and cloud computing on business operations
Develops analytical skills using tools like R and Python, which are widely used in data science roles
Provides strategies for creating a data-driven culture within an organization
Taught by instructors with experience in data science and business transformation
Covers the latest trends and advancements in data science, including deep learning and machine learning

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Reviews summary

Strategic data transformation for leaders

According to learners, this course offers a largely positive and essential strategic framework for navigating digital transformation. Students praise its focus on organizational change and the practical application of concepts like Dynamic Capabilities, Fitness Landscapes, and Strategic Foresight. The instructor's explanations are clear and concise, and the lectures are engaging, making complex ideas accessible. However, some learners express expectation mismatch, noting that despite "Data Analytics" in the title, the course is highly conceptual and lacks technical depth or hands-on activities. It is best suited for non-technical leaders and managers rather than data scientists.
Strong on strategy, but lacks hands-on data analytics tools.
"If you're looking for coding or deep dive into algorithms, this isn't it. But for understanding the 'why' and 'how' of organizational transformation, it's great."
"I signed up thinking I'd learn more about the 'data analytics' part, but it's heavily focused on 'organization' and strategy. While the concepts are valid, it wasn't what I expected."
"The 'data analytics' in the title is misleading, as it's not about the technical aspects at all. It might be good for someone completely new to the strategic side of things."
The course content maintains a high academic standard.
"The University of Maryland content is always high quality."
"This course exceeded my expectations."
"Provides a valuable framework for understanding the strategic implications of data in an organization."
Well-structured lectures and clear, concise explanations.
"The instructor's explanations were clear and concise, making complex ideas accessible."
"The course is well-structured and the lectures are engaging."
"The content is very well-presented, and the instructor clearly knows their subject."
Provides frameworks for leaders in digital transformation.
"Absolutely essential for any business leader navigating the digital age. The focus on strategic foresight and building a competitive digital business model using data was a game-changer for my perspective."
"Excellent course for senior leadership and managers. It provides the necessary mental models and frameworks to understand and drive digital transformation."
"The course truly helps leaders understand how to leverage data beyond just the technical aspects. Highly recommend for managers!"
Some found it too abstract, desiring more tangible examples.
"I found some of the content a bit too abstract. While it aims for 'practical application,' it felt more theoretical at times."
"I wish there were more practical exercises or case studies to reinforce the strategic frameworks."
"It's a good starting point but doesn't provide all the answers for immediate implementation. Good for conceptual understanding, but less so for hands-on 'how-to'."

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 Transforming with Data Analytics and Organization with these activities:
Review 'Big Data' by Viktor Mayer-Schönberger and Kenneth Cukier
Build a stronger foundational understanding of data science concepts by independently reviewing the preeminent book on the subject.
Show steps
  • Read the book
  • Summarize key concepts
  • Identify areas for further research
Join a Study Group for Data Analysis
Enhance learning through collaborative discussions, knowledge sharing, and peer support, solidifying understanding and fostering critical thinking.
Browse courses on Data Analysis
Show steps
  • Find a study group or start your own
  • Meet regularly to discuss course material
  • Work on group projects
Seek Mentorship from Industry Professionals
Connect with experienced professionals in the field of data science to gain insights, guidance, and support, enhancing practical knowledge and career growth.
Show steps
  • Attend industry events
  • Connect with professionals on LinkedIn
  • Request informational interviews
Five other activities
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Show all eight activities
Follow Tutorials on Data Mining Techniques
Develop proficiency in data mining techniques by following guided tutorials, gaining a deeper understanding of algorithms and their applications.
Browse courses on Data Mining
Show steps
  • Identify relevant tutorials
  • Follow the tutorials step-by-step
  • Complete practice exercises
Complete Python Practice Drills on DataCamp
Strengthen Python skills by applying them in practical exercises, leading to proficiency in data handling.
Browse courses on Python
Show steps
  • Sign up for a DataCamp account
  • Complete the Python Data Analysis with Pandas course
  • Practice regularly
Attend Workshops on Machine Learning
Enhance understanding of machine learning algorithms and techniques through interactive workshops, fostering practical knowledge and hands-on experience.
Browse courses on Machine Learning
Show steps
  • Identify relevant workshops
  • Register and attend workshops
  • Participate actively in hands-on activities
Volunteer at a Data Science Hackathon
Gain hands-on experience in a collaborative environment, contribute to real-world projects, and build teamwork and problem-solving skills.
Show steps
  • Identify hackathons in your area
  • Register and join a team
  • Participate in the hackathon
Create a Data Visualization Dashboard
Build practical skills in data visualization by creating an interactive dashboard, reinforcing data analysis and presentation techniques.
Browse courses on Data Visualization
Show steps
  • Choose a dataset
  • Design the dashboard
  • Develop the dashboard using Tableau or Power BI
  • Present the dashboard

Career center

Learners who complete Transforming with Data Analytics and Organization will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Transforming with Data Analytics and Organization.
This classic book by Edward Tufte must-read for anyone interested in data visualization. It covers a wide range of topics, from the principles of visual perception to the design of effective charts and graphs.
Seminal work on the grammar of graphics, a formal language for describing data visualizations. It covers a wide range of topics, from the basics of the grammar of graphics to the use of the grammar of graphics to create a variety of charts and graphs.
The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.
The definitive mathematical canon of modern deep learning, establishing foundational theory across numerical computation, backpropagation optimization, autoencoders, and generative modeling.
A timeless mathematical masterpiece that provides an uncompromising Bayesian and probabilistic foundation for linear classifiers, graphical models, kernel methods, and sampling algorithms.
The definitive mathematical and theoretical canon of statistical learning, establishing the rigorous analytical foundations of regularized regression, tree ensembles, and high-dimensional inference.
Guides deep learning practitioners step-by-step through implementing transformer self-attention, token embeddings, and multi-layer perceptrons in pure PyTorch without relying on high-level wrappers.
Provides a broad and accessible introduction to predictive analytics for a general audience. It focuses on the real-world applications and implications of predictive analytics across various industries, without getting bogged down in technical details. It's an excellent starting point for anyone looking to understand the fundamental concepts and the impact of predictive analytics.
Introduces the fundamental principles of data science and data-analytic thinking from a business perspective. It helps readers understand how to extract valuable knowledge and business value from data, covering various data mining techniques without getting overly technical. Based on an MBA course, it uses real-world business problems to illustrate concepts, making it highly relevant for business-oriented individuals and professionals.
A distilled practitioner field guide outlining best practices for the entire machine learning lifecycle, from problem formulation and feature engineering to model deployment and monitoring.
Provides an essential hands-on manual for fine-tuning pre-trained transformer representations, covering transfer learning workflows, tokenizer optimizations, and production pipeline deployment.
Delivers an end-to-end systems architecture guide for building reliable production ML systems, addressing data engineering trade-offs, real-time feature computation, and model drift mitigation.
Illuminates modern generative neural architectures through celebrated visual diagrams and code workflows, covering contrastive embeddings, semantic search, and retrieval-augmented generation.
Written by the creator of the pandas library, this book practical, hands-on guide to data manipulation, cleaning, processing, and analysis using Python. It is an essential resource for anyone looking to use Python for data analytics, covering key libraries like pandas, NumPy, and Jupyter. It includes numerous real-world case studies and is widely used by students and professionals.
Examines production-grade engineering patterns for training and serving large language models, addressing distributed GPU training, synthetic data pipelines, and quantitative model evaluations.
Focusing on the crucial aspect of communicating insights, this book teaches the fundamentals of data visualization and how to tell compelling stories with data. It provides practical guidance and real-world examples to help readers create effective visualizations and presentations. is highly recommended for anyone who needs to present data-driven findings clearly and persuasively, regardless of their technical background.
Bridges linear algebra, multivariable calculus, vector geometry, and probability directly to fundamental machine learning algorithms like PCA, Gaussian mixture models, and support vector machines.
Delivers an interactive, comprehensive curriculum teaching multi-layer perceptrons, convolutional neural networks, attention mechanisms, and optimization math with runnable multi-framework code.
Similar to its R counterpart, this book focuses on data mining for business analytics but uses Python for illustrations. It's a comprehensive resource for students and professionals looking to apply data mining techniques to business problems using Python.
Presents an applied approach to data mining concepts and methods specifically for business analytics, using R for illustrations. It covers various data mining algorithms and their application to business problems, making it highly relevant for those in business-related programs or careers.
Provides a comprehensive overview of data visualization, covering the basics of visual perception, data types, and chart types. It great resource for beginners who want to learn the fundamentals of data visualization.
Provides a comprehensive overview of digital transformation, covering key concepts, strategies, and case studies. It is suitable for both beginners and experienced professionals.
Provides a broad, introductory overview of data analytics concepts, making it ideal for beginners across various disciplines. It covers key data concepts and includes real-world examples and case studies to solidify understanding. Many universities use this book as a textbook for introductory data analytics courses. It serves as excellent background reading for anyone new to the field.
Instructs data scientists on applied model evaluation, cross-validation, hyperparameter tuning, and pipeline construction through Scikit-Learn maintainers Andreas Müller and Sarah Guido.
Provides a comprehensive overview of the adaptive landscape model. It discusses how this model can be used to understand the evolutionary process, and how it can be used to predict the outcomes of evolutionary processes.
Offers a comprehensive guide to the predictive modeling process, covering data preprocessing, model tuning, and various regression and classification techniques. It emphasizes practical application with numerous real-life examples and includes extensive R code. This valuable resource for those looking to deepen their understanding and gain hands-on experience.
Focuses on modeling techniques for predictive analytics, specifically addressing business problems and providing solutions using R. It combines business context with quantitative tools, making it relevant for analysts and managers. It covers various business cases and analytical methods.
Offers an accessible and engaging introduction to the fundamentals of statistics, a critical component of data analytics. It explains key statistical concepts using real-world examples and relatable anecdotes, making it an excellent resource for those without a strong mathematical background. It helps build a solid foundation in statistical thinking necessary for data analysis.
In the age of information overload, this book provides essential tools and frameworks for critically evaluating data, statistics, and claims presented as data-driven. It equips readers with the skills to identify misleading information and understand the potential for manipulation. This highly relevant book for navigating the contemporary data landscape.
Investigates the engineering realities of running automated ML pipelines in production, addressing data drift detection, automated retraining loops, telemetry infrastructure, and continuous validation.
Details battle-tested infrastructure patterns for deploying deep reasoning models, addressing memory bandwidth limits, continuous GPU batching, semantic caching, and operational latency minimization.
Provides a comprehensive introduction to data mining with a focus on practical tools and techniques, particularly using the Weka software. It widely used textbook and a good resource for understanding the practical aspects of applying data mining algorithms.
Focuses specifically on time series forecasting, a key area within predictive analytics. It covers a wide range of forecasting methods, from simple approaches to more advanced models, and provides practical guidance on applying them. It's an excellent resource for those interested in predicting future values based on historical data.
Details cloud-native DevOps architectures for orchestrating automated machine learning lifecycles, integrating CI/CD triggers, model registry synchronization, and automated hyperparameter sweep workers.
Focuses on the techniques for mining data from the web and other massive datasets. It is particularly relevant for understanding contemporary data mining challenges related to big data. It covers topics such as link analysis, social network analysis, and recommendation systems, making it valuable for those interested in large-scale data mining applications.
Focuses on token segmentation, attention context preservation, and hierarchical chunking, giving deep learning engineers targeted techniques to maximize transformer retrieval accuracy.
Analyzes edge-inference architectures for open-weights neural models, tackling parameter quantization, GGUF execution, KV cache memory bottlenecks, and efficient desktop serving.
A classic since its publication, this book remains highly relevant in highlighting how statistics can be manipulated or misinterpreted to present a misleading picture. It teaches readers to critically evaluate statistical claims and recognize common pitfalls. This valuable read for anyone who works with or consumes data and wants to avoid being misled.
Examines the interplay between dynamic capabilities and innovation, highlighting the importance of these capabilities for driving innovation and achieving competitive advantage. It provides insights into how organizations can develop and leverage dynamic capabilities to create innovative products, services, and processes.
Examines the resource-based view of the firm in relation to dynamic capabilities, exploring how organizations can leverage their resources and capabilities to create and sustain competitive advantage. It provides a theoretical framework for understanding the role of dynamic capabilities in shaping organizational performance.
This practical guide provides a framework for assessing and developing dynamic capabilities, offering a step-by-step approach to improving organizational performance. It includes a comprehensive maturity model and case studies to illustrate the effective implementation of dynamic capabilities.
Offers a comprehensive collection of tools and techniques for conducting foresight exercises.
Published recently, this book focuses on the practical implementation and operationalization of predictive analytics within a business context. It covers the necessary tools, frameworks, and best practices for deploying predictive models in an enterprise setting, making it highly relevant for professionals.
Bridges the gap between data science and business, focusing on how predictive analytics and machine learning can be applied to solve business problems and make better decisions. It's particularly relevant for those interested in the business applications of predictive analytics.

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