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Digital Strategies

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Reading list

We've selected 14 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 Digital Strategies.
Provides a practical guide to developing and implementing digital strategies. It covers topics such as customer experience, data analytics, and innovation. It is written by a team of experts in the field and is suitable for both business leaders and marketers.
Provides a practical guide to getting customers for your startup. It covers topics such as customer acquisition, marketing, and sales. It is written by two successful entrepreneurs and is suitable for both aspiring and experienced entrepreneurs.
Provides a framework for understanding the platform economy. It covers topics such as network effects, data analytics, and platform governance. It is written by three leading experts on the platform economy and is suitable for both business leaders and policymakers.
Provides a framework for understanding exponential organizations. It covers topics such as artificial intelligence, robotics, and the Internet of Things. It is written by three leading experts on exponential organizations and is suitable for both business leaders and entrepreneurs.
Provides a critical analysis of the surveillance economy. It covers topics such as data privacy, artificial intelligence, and the future of work. It is written by a leading expert on the surveillance economy and is suitable for both business leaders and policymakers.
Provides a guide to digital transformation for businesses. It covers topics such as customer experience, data analytics, and innovation. It is written by a leading expert on digital transformation and is suitable for both business leaders and managers.
Provides a comprehensive overview of artificial intelligence. It covers topics such as machine learning, natural language processing, and computer vision. It is written by two leading experts on artificial intelligence and is suitable for both students and researchers.
Provides a practical guide to machine learning for engineers. It covers topics such as data preprocessing, model selection, and hyperparameter tuning. It is written by a leading expert on machine learning and is suitable for both engineers and data scientists.
Provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It is written by three leading experts on deep learning and is suitable for both students and researchers.
Provides a comprehensive overview of reinforcement learning. It covers topics such as Markov decision processes, value functions, and policy gradients. It is written by two leading experts on reinforcement learning and is suitable for both students and researchers.
Provides a practical guide to natural language processing with Python. It covers topics such as text preprocessing, text classification, and text generation. It is written by three leading experts on natural language processing and is suitable for both students and practitioners.
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