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Making Evidence-Based Strategic Decisions

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 Decision Factories

Week 2 Data-Enabled Decision Making

Week 3 Low-Code/No-Code Tools for Data Analytics Products

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores digital transformation, a critical topic in 21st-century business

Builds core competencies in data analysis, machine learning, and artificial intelligence

Provides a framework for navigating the challenges of digital transformation

William A. Brantley, the instructor, has expertise in the field of data analytics

Week 2 focuses on data-enabled decision-making, a crucial skill for businesses

Week 3 introduces low-code/no-code tools, making data analytics accessible to a wider audience

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Reviews summary

Digital transformation strategic decisions

According to learners, this course provides a strong foundational framework for understanding and implementing evidence-based strategic decisions in the context of digital transformation. Students praise its ability to distill complex topics like AI in data analytics and data-enabled decision making into comprehensible modules. Many found the emphasis on low-code/no-code tools particularly beneficial for accessibility. However, some learners noted that while comprehensive in its breadth, the course occasionally offered a broad overview rather than deep technical dives. While it provides excellent conceptual models, learners might need to seek additional resources for direct practical implementation, especially for specific organizational challenges.

Offers a wide-ranging introduction, not deep technical detail.

"The course covers a lot of ground, but I felt it lacked true depth in specific areas like AI."

"It's a high-level introduction, so don't expect to become a data analytics expert."

"I appreciated the breadth, but a deeper dive into some topics would have been beneficial."

Introduces low-code/no-code tools for wider appeal.

"I really liked the focus on low-code/no-code tools, making it easy to apply concepts without heavy coding."

"Even as a non-technical manager, I could understand how to leverage these tools strategically."

"The practical examples with accessible tools were a highlight for me."

Offers actionable frameworks for digital transformation.

"I found the frameworks presented incredibly useful for making strategic decisions."

"The course gave me practical models to approach digital transformation challenges."

"It provided a clear structure for how to use data to build a competitive business."

Requires additional effort to fully apply concepts.

"I wished for more hands-on activities to truly solidify the practical application of the theories."

"While the concepts are great, I found it challenging to directly implement them in my work without further guidance."

"The course provides the 'what' and 'why,' but less of the 'how' for immediate application."

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 Making Evidence-Based Strategic Decisions with these activities:

Review polynomial regression

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Refresh your knowledge of polynomial regression to solidify your understanding of data analysis and machine learning.

Browse courses on Polynomial Regression

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Review the concept of polynomial regression
Practice fitting polynomial regression models to data

Read 'Data Science for Business'

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Gain a foundational understanding of data science concepts and applications in a business context.

View Data Science for Business: What You Need to... on Amazon

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Read one chapter per week and take notes.
Summarize key concepts and examples from each chapter.

Join a Study Group

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Connect with classmates and discuss course material, assignments, and projects.

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Find or form a study group with classmates.
Meet regularly to review course content and work on assignments.

Nine other activities

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Practice feature engineering

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Practice feature engineering techniques to improve the accuracy of your machine learning models.

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Identify potential features from a dataset
Create new features by transforming existing features

Build a data analytics dashboard using Python

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Build a dashboard that displays key data and metrics from a dataset. This will help you apply the concepts of data visualization and dashboarding you learn in the course.

Browse courses on Data Visualization

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Gather and clean the data
Choose the appropriate visualization types
Design and implement the dashboard
Deploy and monitor the dashboard

Practice Data Analysis Using Python

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Improve your data analysis skills by regularly working on practice problems and exercises.

Browse courses on Data Analysis

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Find online resources for data analysis practice problems.
Set aside dedicated time each week to practice data analysis.
Review your solutions and identify areas for improvement.

Follow Online Tutorials on Data Analytics Tools

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Supplement your learning by exploring online tutorials and resources on data analytics software and techniques.

Browse courses on Data Analytics Tools

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Identify online tutorials or courses for the desired data analytics tools.
Watch or read the tutorials and practice using the tools.
Complete exercises or projects provided in the tutorials.

Develop a Data Visualization Project

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Enhance your understanding of data visualization by creating an interactive dashboard or presentation.

Browse courses on Data Visualization

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Identify a dataset for your visualization project.
Choose a data visualization tool and learn its features.
Design and implement your data visualization.
Present your project to classmates or colleagues for feedback.

Create a data visualization dashboard

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Create a data visualization dashboard to present data insights in a visually appealing and interactive format.

Browse courses on Data Visualization

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Choose a dataset and identify key metrics
Design the layout and structure of the dashboard
Create visualizations to represent the data

Build a Machine Learning Model

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Develop hands-on experience in machine learning by building and evaluating a model using a real-world dataset.

Browse courses on Machine Learning

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Choose a machine learning algorithm and dataset.
Train and test your model using cross-validation.
Evaluate the performance of your model and identify areas for improvement.
Deploy your model and monitor its performance over time.

Attend a Data Analytics Workshop

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Gain hands-on experience and insights from experts by attending a data analytics workshop or conference.

Browse courses on Data Analytics

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Identify and register for a relevant data analytics workshop or conference.
Attend the workshop and actively participate in sessions.
Network with professionals and learn about the latest trends in data analytics.

Contribute to an Open-Source Data Analytics Project

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Deepen your understanding of data analytics tools and techniques by contributing to an open-source project.

Browse courses on Open Source

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Identify an open-source data analytics project on platforms like GitHub.
Read the project documentation and familiarize yourself with the codebase.
Identify an area where you can contribute and submit a pull request.

Career center

Learners who complete Making Evidence-Based Strategic Decisions 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 Making Evidence-Based Strategic Decisions.

The Visual Display of Quantitative Information...

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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.

Making Evidence-Based Strategic Decisions

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What's inside

Syllabus

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Reviews summary

Digital transformation strategic decisions

Activities

Career center

Reading list

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