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Data Manipulation in Julia

BasicSkill Level
4.7+
37 reviews
Updated 05/2024
Master the essential skills of data manipulation in Julia. Learn how to inspect, transform, group, and visualize DataFrames using real-world datasets.
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JuliaData Manipulation4 hr15 videos55 Exercises4,650 XPStatement of Accomplishment

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Course Description

Julia is a new and exciting programming language designed for machine learning, scientific computing, and data mining. This course will provide you with the knowledge necessary for starting your own data manipulation journey in Julia.We'll build on your knowledge of DataFrames from the Introduction to Julia and Intermediate Julia courses. By the end of the course, you'll be equipped with core skills for inspecting, transforming, grouping, visualizing DataFrames, and many more.

Prerequisites

Introduction to JuliaIntermediate Julia
1

Inspecting DataFrames

Take your first steps toward complex data manipulation in Julia! Learn how to personalize your experience with the DataFrames package and how to create data visualizations using the Plots package.
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2

Working with columns

Columns are the basic building blocks of DataFrames. Knowing how to handle columns is essential for your data manipulation journey. You'll learn how to reorder and drop columns, as well as how to apply functions to individual rows and whole columns.
Start Chapter
3

Aggregating DataFrames

4

Improving Your Workflow

Data Manipulation in Julia
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*4.7
from 37 reviews
78%
22%
0%
0%
0%
  • Napaporn
    yesterday

  • Sylvia
    3 weeks ago

  • Juan Sebastian
    5 weeks ago

  • Jean
    5 weeks ago

  • Matheus
    2 months ago

  • Elder Mauricio
    3 months ago

Napaporn

Juan Sebastian

Jean

FAQs

What Julia packages are used for data manipulation in this course?

The course primarily uses the DataFrames package and introduces the Chain.jl package for improving code readability, along with the Plots package for basic data visualization.

Does this course cover handling missing values in Julia DataFrames?

Yes, Chapter 4 teaches you how to handle missing values as part of improving your data manipulation workflow, along with loading and joining datasets.

What prior Julia knowledge do I need for this course?

You should complete Introduction to Julia and Intermediate Julia first. The course builds on DataFrame concepts introduced in those earlier courses.

Will I learn to create pivot tables and grouped summary statistics?

Yes, Chapter 3 covers grouping data, calculating grouped summary statistics, and creating pivot tables using Julia DataFrames. You will also use Chain.jl to keep your code readable.

How long does this course typically take to finish?

The course has 55 exercises across 4 chapters with a median completion time of about 4.5 hours and an average of roughly 5 hours.

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