Data science and big data analytics pdf free download

In the recent years, there has been an exponential growth in the both structured. Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use.

The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. Tags Big Data. Marcadores: Big Data. No comments:. Newer Post Older Post Home. Subscribe to: Post Comments Atom.

Best Contents. View 3 excerpts, cites background. Data science, big data and granular mining. Pattern Recognit. The role of data visualization and analytics in performance management: Guiding entrepreneurial growth decisions.

Big Data Analytics in Developing Economies. In a world increasingly driven by data, most developed economies are leveraging big data to achieve greater feats in various sectors of their economies. From advertisement, commerce, healthcare, and … Expand.

Business, Computer Science. Factor-based big data and predictive analytics capability assessment tool for the construction industry. The book is aimed at senior data analysts, consultants, analytics practitioners, and Ph. Chapter 1 Discusses big data and analytics. It starts with some example application areas, followed by an overview of the analytics the process model, and job profiles involved, and concludes by discussing key analytic model requirements.

Chapter 2 Provides an overview of data collection, sampling, and preprocessing. Data is the key ingredient to any analytical exercise, hence the importance of this chapter. It discusses sampling, types of data elements, visual data exploration and exploratory statistical analysis, missing values, outlier detection, and treatment, standardizing data, categorization, weights of evidence coding, variable selection, and segmentation.

Chapter 3 Discusses predictive analytics. It starts with an overview of the target definition and then continues to discuss various analytics techniques such as linear regression, logistic regression, decision trees, neural networks, support vector machines, and ensemble methods bagging, boosting, random forests.

In addition, multiclass classification techniques are covered, such as multiclass logistic regression, multiclass decision trees, multiclass neural networks, and multiclass support vector machines.

The chapter concludes by discussing the evaluation of predictive models.