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PySpark for Data Science - III: Data Cleaning and Analysis
- Dive into the world of big data processing with PySpark, the Python library for Apache Spark.
- Learn how to process, analyze, and derive insights from massive datasets using Python’s user-friendly interface.
- Elevate your data skills with PySpark. Dive deep into distributed data processing, machine learning, streaming, and more to navigate the vast oceans of big data.
Created by Selva Prabhakaran
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English
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English captions
Validity Period: 365 days
Already Subscribed? Click here to access your courses
- Course Certificate
- Code Walkthroughs
- Practice Data
- Money Back if not satisfied
- Algorithms explanation
- 2h 26m of Self-paced videos
- Downloadable resources
- Q&A sessions with experts
What you will learn
01
Identifying Variable Types
02
Outlier Detection and Treatment
03
Identifying and Removing Duplicates
04
Feature Encoding with PySpark
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Missing Value Imputation with PySpark
06
Feature Scaling with PySpark
07
Feature Extraction / Dimensionality Reduction
Course Curriculum
6 Modules | 22 Sessions | 1 hour 15 min Total Time
Introduction to Data PreProcessing
Sessions: 4 | Time: 14 min
Outlier Detection and Treatment
Sessions: 6 | Time: 19 min
Missing Value Imputation
Sessions: 5 | Time: 15 min
Feature Encoding
Sessions: 2 | Time: 11 min
Feature scaling
Sessions: 2 | Time: 5 min
Feature Extraction / Dimensionality Reduction
Sessions: 3 | Time: 11 min
Requirements
- Basics of Python
- Foundational knowledge of Data Science
- High school maths
Who should attend this course?
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Data Science Aspirants
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Data Science Professionals
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Professionals working with large datasets
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Software/Data engineers interested in quantitative analysis
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Data analysts, economists, researchers
Instructor
Selva Prabhakaran Principal Data Scientist
My name is Selva, and I am super excited to mentor you on this project!
I head the Data Science team for a global Fortune 500 company and over the last
10 years of my data science experience I’ve deployed 20+ global products. I’m
also the Founder & Chief Author of Machine Learning Plus, which has over 4M
annual readers.
I specialize in covering the in-depth intuition and maths of any concept or
algorithm. And based on my existing student requests, I’ve put up the series of
courses and projects with detailed explanations – just like an on the job
experience. Hope you love it!
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4.5+Instructor rating
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200+ reviews
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10K+students
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15+ Courses
Validity Period: 365 days
Already Subscribed? Click here to access your courses
- Workshop Certificate
- Code Walkthroughs
- Practice Data
- Money Back if not satisfied
- Algorithms explanation
- 2h 26m of Self-paced videos
- Downloadable resources
- Q&A sessions with experts