Data Science with Julia: Data Analysis & Visualization

Name: Data Science with Julia: Data Analysis & Visualization
Rating: 4.6 (43 reviews)

Learn data analysis, data manipulation, and data visualization in Julia

Created byDr. İlker Arslan

Last updated 3/2026

English

What you'll learn

Having a strong grasp of data frames in Julia
Importing data with Julia
Analyzing and manipulating data with Julia
Data visualization with Julia

Course content

5 sections • 50 lectures • 5h 6m total length

Why use Julia for Data Science?8:04
Discover why Julia blends the ease of Python with the speed of C++, using native libraries to boost data science, machine learning, and numerical computing workflows.
Two-Language Problem3:37
Discover how Julia addresses the two language problem by streamlining model development from data exploration to deployment. Avoid refactoring, boost speed, and integrate with production platforms.
Julia is Fast: Why Does it Matter?2:01
Is Julia Really Fast?3:00
Showcases Julia's speed in benchmarks, outperforming Matlab, Mathematica, Python, and R, rivaling C, Lua, and Rust, and achieving near petaflop-per-second performance in Celeste.
Julia Data Ecosystem4:19
Codes and Resources0:13

Creating Data Frames10:52
Creating Data Frames
Indexing and Slicing Data Frames8:50
Indexing and Slicing
Conditional Filtering9:35
Conditional Filtering
Selecting and Transforming Columns I11:15
Select and transform columns in a data frame using mutating and non-mutating forms, including renaming, computing new columns, and applying ByRow, All, and AsTable functions.
Selecting and Transforming Columns II8:35
Selecting and Transforming Columns
Summarizing Data with Split Apply Combine Strategy10:29
Split-Apply-Combine
Joining Data Frames11:00
Explore how to join data frames in Julia using inner, left, right, outer, semi, anti, and cross joins, with practical library database examples and key-column handling.
Joining Data Frames
DataFrames: Additional Resources0:06

Introduction1:18
Explore importing and exporting data in formats from txt and csv to excel, xml, json, Stata, SAS, SPSS, and hdf5, using Julia libraries and custom functions for data workflows.
Flat Files6:14
Flat Files
Delimited Files4:44
Learn to read and save delimited files in Julia using the CSV package, handle comma or semicolon delimiters, and export the first 12 columns of a data frame to CSV.
Delimited Files
Spreadsheets8:54
Master reading and writing excel files in Julia using XLSX packages, including readxlsx and openxlsx lazy loading, converting sheets to data frames, and saving multiple sheets with writetable.
Spreadsheets
HDF5 Files5:30
Learn how hdf5, an open source hierarchical data format, manages large, complex data with groups and datasets, and use Julia's hdf5 package to create, save, and read arrays and attributes.
HDF5 files
JSON Files3:30
Json explains the JavaScript object notation used in web apps and APIs, and demonstrates importing, parsing to dictionaries, and converting to a data frame.
JSON Files
XML Files8:03
XML Files
Relational Databases7:37
Relational Databases
Statistical Programs2:20
Learn to import SAS, SPSS, and Stata data into Julia using StatFiles package. Use the load function to obtain a StatFile, then convert to a DataFrame with the pipe operator.
Statistical Programs
Web Scraping9:15
Web Scraping

Introduction2:41
Project Description1:53
Import Project Data9:15
Remove Duplicates2:59
Merge Input & Output Data5:20
Summarize Data7:29
Nonnumerical Data14:00
Learn how to handle non-numerical data by converting categorical variables to numerical formats via one-hot and label encoding, so statistical and machine learning models can use these features.
Missing Data6:16
Outliers7:08
Explore how outliers influence central tendency and spread, and learn univariate outlier detection and treatment using histograms, box plots, iqr thresholds, and context-driven decisions.
Standardization & Scaling6:55
Correlation Analysis9:07
Creating Categorical Variables from Numbers (Optional)4:38
Convert a numeric input into a nominal category by binning into adjustable bins, compute and plot default rates per bin, and assess monotonic relations to the target variable.

Introduction4:21
Preparing Data4:18
Line Plot5:10
Scatter Plot3:45
Bar Plot2:50
Histogram3:35
Explore how histograms reveal distribution by binning a continuous variable. Use the plots package for auto or manual bins with Freedman-Diaconis or Scott, and compare frequency, probability, and density.
Box, Dot, Violin Plots5:15
Three Dimensional Plots9:04
Explore three dimensional plots, including heatmaps and surface plots, to visualize relationships with inflation data across years and countries, using filtering and unstacking.
Interactive Statsplot2:42
Explore quick interactive visualizations in Julia using StatsPlots with Interact and Blink to create an interactive data viewer for small data sets.
Makie Package4:11
Dashboards with Makie12:58
Observables2:55
Explore observables in Julia, where variables update automatically through on blocks, lift functions, and the lift macro, enabling reactive updates and chained changes for interactive dashboards.
Interactive Dashboards with Makie17:53
What's Next?0:21

Requirements

I did my best to make this course self-contained, but still I strongly recommend studying the basics of Julia before enrolling. You can take my 'Programming with Julia' course or explore any other online training or book that suits your preferences.

Description

Do you want to learn data analysis, data science, machine learning, deep learning, and AI, but you are not sure about the programming language to choose? Or perhaps you are using Python and R, but you are tired of their slow performance.

You can accomplish everything, and even more, with Julia compared to what you can do with Python or R, all with the same level of ease. Moreover, Julia offers significantly greater speed than both of them.

Julia is a modern programming language developed for data science, machine learning, AI, and numerical computing. It is a dynamically typed language that is easy to learn and use and moreover has the speed of C.

Julia combines the best features of dynamic languages like Python and R with low-level languages like C, C#, and Java. You can develop a machine learning model or an algorithm in Julia and use that code in a production environment. You don't have to use different languages for development and production.

This is my second course about Julia. In this course, you will learn how to accomplish essential data science tasks with Julia: importing, analyzing, manipulating, and visualizing data. Having these foundations you will be ready for machine learning and deep learning with Julia which will be in my upcoming lectures. Please stay tuned.

Who this course is for:

You may be an adept data scientist well-versed in Python or R, or you might be embarking on your learning journey, grappling with the choice of a programming language. I will try to convince you that, you can accomplish everything, and even more, with Julia compared to what you can do with Python or R, all with the same level of ease. Moreover, Julia offers significantly greater speed than both of them.

Data Science with Julia: Data Analysis & Visualization

What you'll learn

Explore related topics

Course content

Introduction6 lectures • 21min

Working with Data Frames8 lectures • 1hr 11min

Importing Data10 lectures • 57min

Data Analysis & Manipulation12 lectures • 1hr 18min

Data Visualization14 lectures • 1hr 19min

Requirements

Description

Who this course is for: