Data Science with Julia

Data Preprocessing you own this product

This free project is part of the liveProject series Hands-on Data Science with Julia
prerequisites
basics of Julia • intermediate data wrangling • intermediate data visualization
skills learned
tabular data ingestion and integrity validation • exploratory data analysis using descriptive and graphical techniques • feature selection and feature engineering
Łukasz Kraiński and Bogumił Kamiński
1 week · 4-6 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll test your data wrangling and data processing skills using the Julia language. You’ll step into the role of a data scientist for a real estate company with a new task from your boss—analyze and clean housing and census data for the marketing and sales teams. You’ll employ the popular Julia package DataFrame.jl as well as powerful statistics related libraries to successfully explore these datasets, and prepare them for machine learning.

project authors

Bogumil Kaminski
Bogumił Kamiński is Head of the Decision Analysis and Support Unit and Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He also holds a position of adjunct professor at the Data Science Laboratory at Ryerson University and is affiliated with Fields Institute (Computational Methods in Industrial Mathematics Laboratory). In the Julia community, he is the owner of the JuliaData organization and a member of JuliaStats and JuliaLang organizations on GitHub. He also contributes to the community as the top answerer for the [julia] tag on Stack Overflow.
Lukasz Krainski
Łukasz Kraiński is a research assistant at the Decision Analysis and Support Unit at SGH Warsaw School of Economics. He is a certified cloud engineer with expertise in Azure and GCP cloud platforms. You can find him at tech conferences speaking about MLOps and AI (MLinPL 2019, PositivTech 2020, Data Driven Innovation 2020). Łukasz is also an active developer and maintainer of Julia packages (CGE.jl, SmartTransitionSim.jl).

prerequisites

This liveProject is for experienced data scientists and data analysts who are interested in building their skills in Julia. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of Jupyter notebook
  • Basics of Julia and intermediate knowledge of another high-level programming language such as Python or R
TECHNIQUES
  • Intermediate data wrangling
  • Intermediate data visualization
  • Basics of bootstrapping
  • Basic usage of command pipelines
  • Basic usage of functions and control flow
  • Basic errors and correlation analysis

you will learn

In this liveProject, you’ll learn to use the powerful Julia language and its rapidly developing ecosystem to perform essential data preprocessing tasks.

  • Tabular data ingestion and integrity validation
  • Exploratory data analysis using descriptive and graphical techniques
  • Feature selection and feature engineering
  • Data cleaning and preprocessing

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