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Python for Data Science Intermediate: Fundamentals of Data Manipulation

Learn how to manipulate data
Preview the first lesson free — get full access to all 4 lessons.
Course: On-Demand
Intermediate Provider Nizar Dajani  4 Lessons ·  29m  in English 

Course Description

In these lessons you will learn how to work with different data collections. Python includes four built-in collection data types: lists, dictionaries, tuples, and sets. We will explore each one and use them in data science examples.

Data is the cornerstone of any system, and having the skills to understand and manipulate any data type is vital to succeed in data science. Here we dive further into the different collection data types and when to use them. You will gain an understanding on how each one serves a unique purpose and how it can be used to manipulate data using the given methods. These lessons will also cover the different methods to add data to the different collections where applicable, as well as update the data, delete the data, and clear the entire dataset. Such data manipulation tasks are common and essential for working with Python.

What You'll Learn

  • Describe Python's four built-in collection data types: lists, dictionaries, tuples, and sets
  • Recognize the methods available for each collection type
  • Identify how to manipulate data within each collection
  • Explain when to use each data collection type for a given task
  • Add, update, delete, and clear data in collections where applicable

Key Takeaways

  • Python includes four built-in collection data types: lists, dictionaries, tuples, and sets.
  • Each collection type serves a unique purpose and can be used to manipulate data using its given methods.
  • The lessons cover methods to add, update, delete, and clear data across the different collections where applicable.
  • Understanding and manipulating data types is presented as vital to succeeding in data science.
  • Data manipulation tasks like these are described as common and essential for working with Python.

Frequently Asked Questions

What does this course cover?

It covers Python's four built-in collection data types — lists, dictionaries, tuples, and sets — exploring each one and using them in data science examples, including methods to add, update, delete, and clear data where applicable.

What lessons are included?

The course includes four lessons: Lists, Dictionaries, Tuples, and Sets.

What skills will I gain?

You will gain skills in data manipulation, data classification, working with data dictionaries, the Data Manipulation Language, and applying these in data science.

Who is this course for?

It is an intermediate Python for Data Science course aimed at learners who want the skills to understand and manipulate different data types, which the course presents as vital to succeed in data science.

What will I be able to do after these lessons?

You will be able to describe the different data collection types, recognize their methods, identify how to manipulate data, and explain when to use each collection type.