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Nuclio Learning

Advanced Data Science: Key Tools

  • Intermediate

Transform data into decisions with the Advanced Data Science: Key Tools certification course. Gain industry best practices for structuring ML projects and mastering scikit-learn for efficient data cleaning and processing.

  • Python Virtual Environments
  • ML Project Structuring
  • sklearn Package
  • Data Cleaning
  • Data Processing Automation

Overview

This course covers key concepts such as Python virtual environments, proper structuring of ML projects, and the appropriate use of the sklearn package, focusing on Pipelines, Transformers, and ColumnTransformers to automate data cleaning and processing. Learn to develop Python modules to promote code reuse and improve efficiency in ML project development.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    Spanish
    course language
  • Professional Certification
    upon course completion
  • Self-paced
    course format
  • Pre-recorded classes
    delivered online

Who is this course for?

Junior Data Science Professionals

Professionals in the world of Data Science with 1-2 years of experience who wish to improve their skills to make the leap to senior.

Data Analysts

Data Analysts who want to expand their knowledge to switch to a Data Scientist position.

Nuclio Alumni

Former students of Nuclio who need to refresh their knowledge.

Why should you take this course?

Data

Nico Popescul

Instructor

By taking this course, you will be able to carry out a Supervised Machine Learning project applying industry best practices, structure ML projects correctly, and master the effective use of scikit-learn. This course is ideal for junior data science professionals, data analysts, and Nuclio alumni looking to advance their careers.

Pre-Requisites

1 / 3

  • Installation of Python and other necessary tools.

  • Creation of Python virtual environments with pip and anaconda.

  • Creation of the necessary folder structure for an ML project.

What will you learn?

Module 1 - Introduction and Prerequisites
Introduction of the project and the instructor, installation of Python and other necessary tools, creation of Python virtual environments, and necessary folder structure for an ML project.
Module 2 - EDA and First Contact with the Dataset
EDA and first contact with the dataset.
Module 3 - sklearn and Best Practice of Data Treatment
Introduction to sklearn, Train Test Split, sklearn Transformers, sklearn ColumnTransformers, sklearn Pipelines, sklearn FunctionTransformer, object-oriented programming, CustomTransformers, and final Pipeline.
Module 4 - Code Refactoring, Wrap Up and Conclusions
Code refactoring, wrap up, and final conclusion.

Meet your instructor

  • Nico Popescul

    Instructor, Nuclio Learning

    Nico Popescul is a passionate instructor at Nuclio Learning, specializing in Python and data science.

Upcoming cohorts

  • Dates

    start now

€280