Data Wrangling and Ingestion using Python

Do you want to expand your data analytics skills and streamline your analysis processes? Having a solid understanding of Python can help to boost your career and make data analysis and manipulation considerably more efficient.

This course builds on the previous two within this Data Analytics Using Python ExpertTrack to give you a better understanding of the possibilities of using Python.

Deepen your understanding of Python functions

On this course, you’ll familiarise yourself with the data wrangling, manipulation and exploration steps of the CRISP-DM methodology, and perform data ingestion using Python.

You’ll learn how to demonstrate transformation activities in Python to further your data analysis.

You’ll also explore SQL in relation to data analytics, including the fundamentals of databases using SQL to perform various data analysis operations.

By the end of this course, you’ll be better equipped to create database tables using the data definition language and add data records using data manipulation language.

You’ll have also practised how to query the data stored in those database tables, and demonstrate various data processing activities using data query language and data manipulation language.

This course is suitable for:

• business analysis or junior data analysts looking to develop advanced data analytics skills and programming capabilities to progress their careers in data analytics
• someone with existing programming capabilities looking to enter the data analytics field
• an individual lacking the programming capabilities to conduct more advanced data analysis and modelling

During the ExpertTrack we’ll be using Jupyter notebook. We recommend you use a computer to access these elements.

• Data ingestion using Python I
• Welcome to the course
• Introduction to data ingestion using Python
• Reading data: Text and csv formats
• Missing data: Text and csv formats
• Wrap-up
• Data ingestion using Python II
• Introduction to advanced data ingestion using Python
• Reading data: JSON, HTML, XML, and SQL
• Wrap-up
• Data wrangling using Python I
• Introduction to data wrangling using Python
• What is Data Wrangling?
• Data wrangling: concat ()
• Data wrangling: merge ()
• Manipulating and transforming data in Python
• Wrap-up
• Data wrangling using Python II
• Introduction to advanced data wrangling using Python
• Data Wrangling: GroupBy mechanics
• Wrap-up