![]() ![]() We will come back to those techniques in detail in future chapters of course, but you must bear with it now. For example, to remove outliers, necessary techniques such as statistical plotting, specifically a box plot and scatter plot, will be used. It is not possible to cover the topics in an entirely sequential fashion. Examples involving the scikit-learn preprocessing module.Data imputation with an assessment of downstream task requirements.Collecting data from various data sources with a focus on data quality.Here are the topics that will be covered in this chapter: We prepared a GitHub repository that contains ready-to-run codes from this chapter as well as the rest. Data standardization clearly requires an understanding of statistics such as standard deviation. For example, outlier removal will be very hard to conduct without a scatter plot. They are correlated with other topics, such as visualization and basic statistics concepts. You are going to learn five key topics in this chapter. You get the fish from the water or from the fish market, examine it, and process it a little bit before bringing it to the chef. This book is designed and written to close this gap for you.Īs the first chapter in the book, we start with the very first step of a data science project: collecting, cleaning data, and performing some initial preprocessing. However, there is a lack of a systematic approach to the statistical side of data science. You may already be familiar with common Python toolkits and have followed trending tutorials online. Thank you for purchasing this book and welcome to a journal of exploration and excitement! Whether you are already a data scientist, preparing for an interview, or just starting learning, this book will serve you well as a companion. Finally, you’ll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning.īy the end of this Essential Statistics for Non-STEM Data Analysts book, you’ll have learned how to build and present a self-contained, statistics-backed data product to meet your business goals. Once you’ve uncovered the working mechanism of data science algorithms, you’ll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. As you advance, you’ll find out how statistical concepts emerge from different stages of data science pipelines, understand the summary of datasets in the language of statistics, and use it to build a solid foundation for robust data products such as explanatory models and predictive models. You’ll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. The book starts by showing you how to preprocess data and inspect distributions and correlations from a statistical perspective. This book is a detailed guide covering the math and various statistical methods required for undertaking data science tasks. Statistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines.
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