Chapter 18: Summary, Practice Case Studies, and Conclusions

This chapter will provide a summary of the book, some practice case studies, and lastly offer some concluding remarks.

In this chapter, we're going to cover the following main subchapters:

• A summary of the book
• Practice case studies
• Conclusions

A summary of the book

Congratulations on your excellent journey of learning through the course of this book; you've acquired invaluable skills. You learned various skills in the four parts of this book. In the following subchapter, we will go over what we learned in each part of this book.

Part 1 – Technical requirements

In this part of the book, which lasted from Chapter 1, Review of the Core Modules of NumPy and Pandas through Chapter 4, Databases, we covered all the technical and foundational concepts, techniques, and technologies that you will need for effective data preprocessing. Specifically, in Chapter 1, Review of the Core Modules of NumPy and Pandas, and Chapter 2, Review of Another Core Module – Matplotlib, we picked up all the foundation Python programming skills that we will need for data preprocessing. In Chapter 3, Data – What Is It Really? we acquired a fundamental understanding of data and the different analytics paths that have implications...

Practice case studies

This subchapter introduces 10 practice case studies. Each case study introduces a dataset and provides an analytics goal that can be achieved by preprocessing and analyzing the dataset. While each case study comes with a few analytics questions (AQs), don't allow them to close your mind to other possibilities. The suggested AQs are only meant to get you started.

We will start with a very meaningful and valuable case study that can provide real value to many levels of decision makers.

Google Covid-19 mobility dataset

Since the beginning of the recent COVID 19 pandemic, the United States (US) had various responses to combat Covid-19, varying from state to state. Each state implemented different health and safety precautions and followed different timeframes when shutting down the state. Many factors contributed to each state's health regulations, such as the number of Covid-19 cases, population density, and healthcare systems; however, most states...

Conclusions

Allow me to start concluding this book by congratulating you on having gone through this journey of learning about data analytics and data preprocessing. I am confident that your learning about data analytics and data preprocessing does not end here, and you are already planning to learn more useful tools and pick up valuable skills. So, how about we conclude this book by examining a few routes for learning and improvement?

My first suggestion would be to cover your base and take advantage of all of the learning resources that this book has to offer so that you can deepen your learning and bring your skill level closer to second nature. The end of most chapters provides exercises for exactly this purpose. Furthermore, the three case studies in Chapters 15 through 17 can be expanded upon and improved; doing that would be a great way to improve your learning. Lastly, this current chapter provided many starting points and case studies to practice the skills you've...

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