Those who desire to work as data scientists should read this book. The variety of tasks, techniques, and tools employed by data scientists are covered in the book. Conjectural aspects are backed by simulation and modelling applications to help solve real-world situations, and it tackles key concerns relevant to large data analysis employing computational intelligence methodologies. This book teaches you how to apply the right analytic techniques and tools to analyse large data and how to use data to build a compelling story that motivates business action. You will be prepared for the EMC Proven Professional Data Science Certification after reading this book. The learning is supported and explained with examples that you can replicate using open-source software and you can Deploy a structured lifecycle approach to data analytics problems. This focuses on concepts, principles, and practical applications that are applicable to any industry and technological environment. Each chapter concludes with a comprehensive bibliography. A multitude of figures, graphs, and tables are included in addition to the primary text, providing academics working in the fields of big data analytics and artificial intelligence with an invaluable resource. I hope you will benefit from and find this book useful.

The most important thing in business nowadays is data. Therefore, in this data-driven environment, various technologies, processes, and systems have been developed to process, transform, analyse, and store data. There are numerous options for storing and processing large data sets when it comes to big data management. Analyzing data to glean useful information from a particular data collection is known as data analytics. Although they can be used with any data source, these analytics approaches and procedures are typically applied to big data. Businesses can, for instance, use analytics to pinpoint customer preferences, purchasing patterns, and market trends before developing strategies to address them and adapt to changing market conditions. In a scientific perspective, a medical research institution can gather information from clinical trials and accurately assess the efficacy of medications or therapies by doing so. You will be able to show the underlying data more adaptably and intentionally and gain a greater understanding of it by combining these analytics with data visualisation approaches. The main thing to keep in mind is that the underlying data set determines how accurate the analytics are. Analytics will be ineffective or downright wrong if the dataset has inconsistencies or inaccuracies. Any effective analytical technique will take into account external variables including data bias, data purity, and procedure variance. This is an area where normalisation, purification, and transformation of raw data can be quite helpful.In the end, big data, data analytics, and data science all assist people and organisations in dealing with massive data sets and obtaining useful information from them. Data will become indispensable elements in the technological landscape as their significance increases exponentially.

Topics covered by this book:

  • The definition of big data, the necessity for advanced analytics, the distinctions between data science and business intelligence (BI), and the new roles required for the emerging big data ecosystem are all clarified in chapter 1.

  • The data analytics lifecycle is described in Chapter 2 in terms of its six phases, including discovery, data preparation, model planning, model building, communicating outcomes, and operationalizing. These procedures enable data science teams to pinpoint issues and conduct thorough investigations of the datasets required for in-depth analysis.

  • Chapter 3 explains how to conduct exploratory data analysis using certain fundamental visualisation methods and R’s plotting capability. Statistics are essential because they can be available at any point in the Data Analytics Lifecycle, making visualisation valuable for data exploration and presentation. The chapter’s final section focuses on statistical inference techniques like R’s hypothesis testing and analysis of variance.

  • The algorithm to calculate the k means is described in Chapter 4, along with recommendations for how to apply this method to various use cases. It demonstrates how to assign points to the closest centroid using the Euclidean distance function.

  • In Chapter 5, it is discussed how metrics like support, confidence, lift, and leverage can be used to assess the suitability of various potential rules. The chapter concludes by outlining a few ways to increase the efficiency of the Apriori algorithm and discussing some of its advantages and disadvantages.

  • The use of logistic and linear regression to model historical data and forecast future results is covered in Chapter 6. Examples of each regression method are provided using R. There are also several diagnostics to assess the models and underlying presumptions covered.