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Data Science with Julia

New – Explore the world of high-performance data science with Julia in this self-paced course.

GK# 7714

Course Overview


This course will help you get familiarized with Julia's rich ecosystem and contains the essentials of data science, giving you a high-level overview of advanced statistics and techniques. You will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. This course addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.


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What You'll Learn

  • Get to grips with the basic data structures in Julia and learn about different development environments
  • Understanding the process of data munging and data preparation using Julia
  • Perform statistical computations on data from different sources and visualize those using plotting packages
  • Gain some valuable insights into interfacing Julia with an R application
  • Create supervised and unsupervised machine learning systems using Julia
  • Dive into Julia’s deep learning framework and build a system using Mocha.jl


Viewing outline for:

On-Demand Outline

The Groundwork: Julia’s environment

Data Processing and Cleaning

Making sense of data using Visualization

Supervised and Unsupervised Machine Learning

Deep Learning with Mocha.jl

Who Should Attend


This course is for data analysts and aspiring data scientists who are new to Julia. The course also appeals to those competent in R and Python and wish to adopt Julia to improve their skills set in Data Science. It would be beneficial if the readers have a good background in statistics and computational mathematics.

Course Delivery

This course is available in the following formats:


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