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.
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.
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
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Syllabus
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Activities
Review polynomial regression
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Refresh your knowledge of polynomial regression to solidify your understanding of data analysis and machine learning.
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Polynomial Regression
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Review the concept of polynomial regression
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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.
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Data Science for Business: What You Need to...
on Amazon
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Read one chapter per week and take notes.
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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.
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Meet regularly to review course content and work on assignments.
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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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Feature Engineering
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Identify potential features from a dataset
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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.
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Data Visualization
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Gather and clean the data
-
Choose the appropriate visualization types
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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.
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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.
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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.
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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.
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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.
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Data Analytics
Show steps
-
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
Show steps
-
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.
Review polynomial regression
Show steps
Refresh your knowledge of polynomial regression to solidify your understanding of data analysis and machine learning.
Browse courses on
Polynomial Regression
Show steps
- Review the concept of polynomial regression
- Practice fitting polynomial regression models to data
Read 'Data Science for Business'
Show steps
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
Show steps
- Read one chapter per week and take notes.
- Summarize key concepts and examples from each chapter.
Join a Study Group
Show steps
Connect with classmates and discuss course material, assignments, and projects.
Show steps
- Find or form a study group with classmates.
- Meet regularly to review course content and work on assignments.
Practice feature engineering
Show steps
Practice feature engineering techniques to improve the accuracy of your machine learning models.
Browse courses on
Feature Engineering
Show steps
- Identify potential features from a dataset
- Create new features by transforming existing features
Build a data analytics dashboard using Python
Show steps
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
Show steps
- 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
Show steps
Improve your data analysis skills by regularly working on practice problems and exercises.
Browse courses on
Data Analysis
Show steps
- 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
Show steps
Supplement your learning by exploring online tutorials and resources on data analytics software and techniques.
Browse courses on
Data Analytics Tools
Show steps
- 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
Show steps
Enhance your understanding of data visualization by creating an interactive dashboard or presentation.
Browse courses on
Data Visualization
Show steps
- 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
Show steps
Create a data visualization dashboard to present data insights in a visually appealing and interactive format.
Browse courses on
Data Visualization
Show steps
- 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
Show steps
Develop hands-on experience in machine learning by building and evaluating a model using a real-world dataset.
Browse courses on
Machine Learning
Show steps
- 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
Show steps
Gain hands-on experience and insights from experts by attending a data analytics workshop or conference.
Browse courses on
Data Analytics
Show steps
- 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
Show steps
Deepen your understanding of data analytics tools and techniques by contributing to an open-source project.
Browse courses on
Open Source
Show steps
- 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.
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