Название: Common Data Sense for Professionals: A Process-Oriented Approach for Data-Science Projects
Автор: Rajesh Jugulum
Издательство: Routledge
Год: 2022
Страниц: 123
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

Data is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections of data to determine strategy and marketing. Data scientists take data, analyze it, and create models to help solve problems. You may have heard of companies having data management teams or chief information officers (CIOs) or chief data officers (CDOs), etc. They are all people who work with data, but their function is more related to vetting data and preparing it for use by data scientists.

The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think Big Data analysis requires tools held only by advanced degree holders. Although advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a Data Science project. Because we are all already data users, with some simple strategies and exposure to basic analytical software programs, anyone who has the proper tools and determination can solve Data Science problems. The process presented in this book will help empower individuals to work on and solve data-related challenges.

The goal of this book is to provide a step-by-step guide to the Data Science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study, illustrating how the various topics discussed can be applied. Essentially, this book helps traditional business people solve data-related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity.

Although this book is intended to help regular people by providing a step-by-step guide with simple game plans/strategies, it is important to have the right tools. The later chapters of the book will address data collection, model building, and analysis of data. In order to properly analyze data and begin model building, you will need access to data analytics software programs. The software will help you sort through your data and manipulate your data in order to gather insights. The choice of your software has no influence on the overall data science process.

One final point. More than anything else, success in Data Science depends on talent. Many will conclude that to mean world-class data scientists. In my view, it is far more complex than that. Everyone, quite literally everyone, needs to do more with data. Said differently, why let the professional data scientists have all the fun!