Hands On Training

Feature Engineering with PySpark

DataCamp

(3)

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

Your job is to find the meaning in chaos. Careful curation is required to create toys datasets like MTCars and Iris. The data must be transformed in order to make them useful for machine-learning algorithms that can predict, extract, classify, cluster, etc. This course will cover the details that data scientists spend between 70 and 80% of their time dealing, such as feature engineering and data wrangling. Let's use PySpark Big Data to reduce these datasets which are becoming increasingly complex.

Highlights

Pedagogy

Top 30 Percentile

Rating & Reviews

Top 30 Percentile

Parameters

Pedagogy

Acquire all major Python Programming skills in this course for seamless integration into your daily life. Develop a versatile skill set, allowing you to confidently apply what you've learned in various practical scenarios, enhancing your daily experiences and overall proficiency. An exceptional course in Python Programming, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.

Rating & Reviews

This highly acclaimed course is among the top-rated in Python Programming, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in Python Programming.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Supervised Learning with scikit-learn

Introduction to PySpark

What You Will Learn

This course will cover the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering

In this chapter learn to remove unneeded information, handle missing values and add additional data to your analysis

In this chapter learn how to create new features for your machine learning model to learn from

In this chapter we'll learn how to choose which type of model we want

Course Content

Expand all sections

Module 1: Exploratory Data Analysis

Module 2: Wrangling with Spark Functions

Module 3: Feature Engineering

Module 4: Building a Model

Course Instructors

John Hogue

Lead Data Scientist, General Mills

I have a strong drive for innovation and giving back. Through my work I enjoy building out a career path and center of excellence for those in data science at General Mills. I have a passion for taki...

Course Reviews

Average Rating Based on 3 reviews

5.0

100%