Enterprise Data Wrangling with SQL and Python

Overview

AI, Data Science & Cloud Certificates from Google, IBM & Meta — 50% Off

One plan covers every Professional Certificate on Coursera. 50% off Coursera Plus Annual for 10 days only — price increases June 17.

Unlock All Certificates

Welcome to Enterprise Data Wrangling with SQL and Python. This course will cover data wrangling principles and techniques for business. Key topics include data extraction, profiling, cleansing, integration, transformation, and automating data processes for business purposes. In the course, you will apply principles and techniques using data transformation tools, programming languages, and data process automation tools. The course offers you an opportunity to learn how to embed appropriate communication mechanisms for collaboration to identify and resolve real-world data challenges revealed in datasets and business processes, creating business value in today’s disparate computing and dynamic business environment.

Syllabus

Introduction to Data Wrangling and Relational Databases

In this module, you will learn about the structure of relational databases and how to use SQL queries for information retrieval, focusing on single-row and group functions. In the next module, you will build upon this foundation by exploring data manipulation and data joining techniques.

Managing and Retrieving Data with SQL

The module also highlights how effective data manipulation and joining contribute to the broader goals of data wrangling and preparation, ensuring that data is both well-organized and ready for analysis.

Data Profiling and Discovery

In this module, you will learn how to explore datasets using Python. You’ll practice techniques to inspect dataset structure (rows, columns, and data types), and detect missing, invalid, or inconsistent data. You will also learn how to generate descriptive statistics and distribution summaries, as well as interpret profiling results to guide data cleansing and improve overall data quality.

Data Cleansing

By the end of this module, you will be able to apply practical data cleansing techniques to improve data quality and make your analysis more accurate and trustworthy.