Beginner's Guide to Python Arrays

Learn about the power of Python Arrays and how they apply as Data Structures
English
English
Develop understanding of how Python Arrays work and what advantages they offer over other Data Structures
Create Arrays of Different Dimensions
Arrays Visualization - 2dD, 3D, 4D and higher dimensional Arrays
Array Attributes and how to use them to know more about Arrays
Use Arrays as Data containers for Common Data Operations

Requirements

  • Basic knowledge of Python (including Data Types and Structures, For Loops, List Comprehension, etc.)

Description

Arrays are a powerful means of storing variables of the same data type (Integer, Float, String, etc.). Compared to their counterpart Data Structures, they provide many benefits, be it:

  • Faster processing

  • Compact memory usage

  • Easy access to data elements, or

  • Simpler operations with less coding effort

To give you some context, if you have worked on Pandas DataFrames, which is a special case of 2 Dimensional Arrays, you would know what different operations you can perform and how you can handle datasets more effectively. Well with Arrays you can do most of that and much more and for that very reason they are used as the preferred Data Containers to run Machine Learning algorithms (in Modules such as Scipy and Scikit-learn).

To simply put, "A good command on Arrays will take your understanding of Data Structures and their use to the next level", and this is exactly where this course comes in. Even if you've not worked on Arrays earlier, you can use this course to develop your understanding grounds-up.

Here we cover, "Arrays as Data Structures and how they get applied in Python". Below are the key areas that this course addresses :

  1. Intuition of Arrays as Data Containers

  2. Visualizing 2D/3D and higher dimensional Arrays

  3. Array Indexing and Slicing - 2D/3D Arrays

  4. Performing basic operations using Numpy Arrays

By the end of this course, you will be able to use Arrays in common data operations and data analysis. This will also give you a platform and confidence to quickly scale up to learn more advanced topics related to Numpy.

Who this course is for:

  • Anyone who wants to learn in more depth, about Numpy Arrays and put them to practical use

Course content

4 sections8 lectures1h 14m total length
  • Array Basics : Array creation, Attributes, Dtypes
    12:10
  • Array Basics : Array Reshaping and few tips
    10:39

Instructor

Data Science Professional - BFSI and Life Sciences
Gaurav Singh
  • 4.6 Instructor Rating
  • 64 Reviews
  • 1,421 Students
  • 3 Courses

Data Science Professional with 15 Years of Industry Experience in BFSI and Life Sciences. Currently engaged full-time as Online/Corporate Trainer.

My motivation is to create "Application oriented training content that learners can intuitively understand and also apply to their day-to-day work to solve a real-world problem". There is no dearth of training content today, be it Udemy or elsewhere, and i totally understand that. Thus I am working in the direction to create and share more useful and relatable content which has a high degree of practical applicability and not just mere Theoretical existence.