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Complete Data Preprocessing Tutorial | Data Wrangling with Python | Data Cleaning @SCALER
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In this video, Roshni Tayal (Lead Data Scientist at Yum! Brands) is explaining what is Data Wrangling in detail. Check out our free masterclasses by industry-leading experts here:
Data wrangling and data preprocessing are crucial stages in the data analysis pipeline, playing a pivotal role in transforming raw data into a format suitable for analysis and modeling. Data wrangling involves the process of cleaning, structuring, and enriching raw datasets to ensure they meet the requirements for analysis. This may include handling missing values, correcting errors, and restructuring data to facilitate better insights.
Topics Covered
00:00:00-Introduction
00:00:35-What is Data Wrangling?
00:00:35-Methods to merge dataframes
00:11:15-How to get unique values?
00:15:14-Creating Subsets from Dataset
00:28:02-The Concept of Joins
On the other hand, data preprocessing involves a broader set of tasks aimed at preparing data for machine learning algorithms. This includes scaling, normalization, and feature engineering to enhance the performance of models. Both data wrangling and preprocessing are essential for ensuring the quality and reliability of data, laying the foundation for meaningful and accurate analytical outcomes.
Effective execution of these processes contributes significantly to the success of data-driven projects, enabling researchers, analysts, and data scientists to derive valuable insights from diverse and often messy datasets.
What is Data?
Data refers to raw facts, statistics, or information that can be in various forms such as numbers, text, images, etc., and holds significance when processed to extract meaningful insights.
What is Python?
Python is a versatile high-level programming language known for its readability and simplicity. It is widely used for various applications, including web development, data analysis, machine learning, and more.
What is Data Wrangling?
Data wrangling involves the process of cleaning, organizing, and transforming raw data into a usable format for analysis. It includes tasks such as handling missing values, removing duplicates, and restructuring data.
What is Data Preprocessing?
Data preprocessing involves a broader set of tasks aimed at preparing raw data for analysis. It includes cleaning, transforming, and organizing data to enhance its quality and suitability for machine learning or statistical analysis.
What are Joins in Python?
Joins in Python typically refer to combining data from different sources based on common columns. In pandas, a popular Python library for data manipulation, you can use functions like merge() to perform various types of joins (e.g., inner join, outer join) on DataFrames.
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Data wrangling and data preprocessing are crucial stages in the data analysis pipeline, playing a pivotal role in transforming raw data into a format suitable for analysis and modeling. Data wrangling involves the process of cleaning, structuring, and enriching raw datasets to ensure they meet the requirements for analysis. This may include handling missing values, correcting errors, and restructuring data to facilitate better insights.
Topics Covered
00:00:00-Introduction
00:00:35-What is Data Wrangling?
00:00:35-Methods to merge dataframes
00:11:15-How to get unique values?
00:15:14-Creating Subsets from Dataset
00:28:02-The Concept of Joins
On the other hand, data preprocessing involves a broader set of tasks aimed at preparing data for machine learning algorithms. This includes scaling, normalization, and feature engineering to enhance the performance of models. Both data wrangling and preprocessing are essential for ensuring the quality and reliability of data, laying the foundation for meaningful and accurate analytical outcomes.
Effective execution of these processes contributes significantly to the success of data-driven projects, enabling researchers, analysts, and data scientists to derive valuable insights from diverse and often messy datasets.
What is Data?
Data refers to raw facts, statistics, or information that can be in various forms such as numbers, text, images, etc., and holds significance when processed to extract meaningful insights.
What is Python?
Python is a versatile high-level programming language known for its readability and simplicity. It is widely used for various applications, including web development, data analysis, machine learning, and more.
What is Data Wrangling?
Data wrangling involves the process of cleaning, organizing, and transforming raw data into a usable format for analysis. It includes tasks such as handling missing values, removing duplicates, and restructuring data.
What is Data Preprocessing?
Data preprocessing involves a broader set of tasks aimed at preparing raw data for analysis. It includes cleaning, transforming, and organizing data to enhance its quality and suitability for machine learning or statistical analysis.
What are Joins in Python?
Joins in Python typically refer to combining data from different sources based on common columns. In pandas, a popular Python library for data manipulation, you can use functions like merge() to perform various types of joins (e.g., inner join, outer join) on DataFrames.
______________________________________________________________________________
About SCALER:
A transformative tech school, creating talent with impeccable skills. Upskill and Create Impact.
📌 Follow us on Social and be a part of an amazing tech community📌
🔔 Hit that bell icon to get notified of all our new videos 🔔
If you liked this video, please don't forget to like and comment. Never miss out on our exclusive videos to help boost your coding career! Subscribe to Scaler now!
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