Become a data wrangling expert and make well-informed decisions by effectively utilizing and analyzing raw unstructured data in a systematic manner
Key Features
- Implement query optimization during data wrangling using the SQL language with practical use cases
- Master data cleaning, handle the date function and null value, and write subqueries and window functions
- Practice self-assessment questions for SQL-based interviews and real-world case study rounds
Book Description
The amount of data generated continues to grow rapidly, making it increasingly important for businesses to be able to wrangle this data and understand it quickly and efficiently. Although data wrangling can be challenging, with the right tools and techniques you can efficiently handle enormous amounts of unstructured data.
The book starts by introducing you to the basics of SQL, focusing on the core principles and techniques of data wrangling. You’ll then explore advanced SQL concepts like aggregate functions, window functions, CTEs, and subqueries that are very popular in the business world. The next set of chapters will walk you through different functions within SQL query that cause delays in data transformation and help you figure out the difference between a good query and bad one. You’ll also learn how data wrangling and data science go hand in hand. The book is filled with datasets and practical examples to help you understand the concepts thoroughly, along with best practices to guide you at every stage of data wrangling.
By the end of this book, you’ll be equipped with essential techniques and best practices for data wrangling, and will predominantly learn how to use clean and standardized data models to make informed decisions, helping businesses avoid costly mistakes.
What you will learn
- Build time series models using data wrangling
- Discover data wrangling best practices as well as tips and tricks
- Find out how to use subqueries, window functions, CTEs, and aggregate functions
- Handle missing data, data types, date formats, and redundant data
- Build clean and efficient data models using data wrangling techniques
- Remove outliers and calculate standard deviation to gauge the skewness of data
Who This Book Is For
This book is for data analysts looking for effective hands-on methods to manage and analyze large volumes of data using SQL. The book will also benefit data scientists, product managers, and basically any role wherein you are expected to gather data insights and develop business strategies using SQL as a language. If you are new to or have basic knowledge of SQL and databases and an understanding of data cleaning practices, this book will give you further insights into how you can apply SQL concepts to build clean, standardized data models for accurate analysis.
Table of Contents
- Database Introduction
- Data profiling and preparation before Data Wrangling
- Data wrangling on string datatype
- Data wrangling on date datatype
- Handling Null values
- Pivoting data using SQL
- Subqueries
- Aggregate Function
- Window Function
- SQL query tuning
- Descriptive Statistics using SQL
- Time series with SQL
- Outlier Detection
Raghav Kandarpa is an experienced Data Scientist in Finance and logistics industry with expertise in SQL, Python, Building Machine Learning Models, Financial Data Modelling, and Statistical Analysis. He holds a Masters’ degree in Business Analytics specializing in Data Science from the University of Texas at Dallas.
Shivangi Saxena is an experienced BI Engineer with proficiency in SQL, Data Visualization, and Statistical Analysis. She holds a master’s degree in Information Technology and Management from the University of Texas at Dallas. She has several years of experience building several BI tools and products using SQL and BI reporting tools which has helped stakeholders to get visibility to the right data points
