Data Wrangling and Preprocessing in Data Science A Practical Guide

Collect and Import dаta: Discover effective methods for sourcing data from various locations such as relational databases, APIs, files, and through web scraping. This section covers best practices for ensuring that the initial data collection is robust and well-documented.●
Clean dаta: Master techniques for addressing common issues such as missing values, duplicate entries, and inconsistencies in the data. You'll learn how to make informed decisions about whether to remove, impute, or transform problematic data.●
Transform dаta: Explore methods for scaling, normalizing, and encoding categorical variables. These techniques ensure that your data is in the proper format for modeling and analysis.●
Engineer and Select Features: Understand how to create new, informative features from existing data and select the most relevant ones to enhance the performance of your models. This process is vital for reducing complexity and improving the predictive power of your data.●
Reshape and Aggregate dаta: Develop skills in restructuring data to enable more effective analysis. You'll learn how to pivot tables, melt data into long formats, and aggregate data to generate meaningful summaries.●
Handle Outliers and Anomalies: Learn to identify and address unusual data points that can negatively impact your results. The book covers various statistical and visualization techniques for detecting and managing outliers and anomalies.●
Work with Text dаta: Gain expertise in preprocessing text data using techniques such as tokenization, stop word removal, stemming, and lemmatization. This is a core skill for anyone working with unstructured data.●
Automate Workflows: Discover strategies for automating your data wrangling workflows using scripting and various tools to improve efficiency and consistency. The book emphasizes creating modular and reusable code.The book also provides hands-on examples utilizing popular Python libraries like Pandas and NumPy, and it explores data wrangling techniques within SQL, R, and Apache Spark to ensure you are familiar with a variety of environments. This resource will be invaluable for both beginners and experienced data scientists, equipping you with the practical skills necessary to tackle real-world data challenges. The book also contains a glossary of data wrangling terms to assist you along the way.