Udemy – The A to Z of Data Preprocessing for Data Science in Python 2024-1

Udemy – The A to Z of Data Preprocessing for Data Science in Python 2024-1 Downloadly IRSpace

Apr 17, 2025 - 19:55 Updated: Apr 21, 2025 - 09:58

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Udemy – The A to Z of Data Preprocessing for Data Science in Python 2024-1

The A to Z of Data Preprocessing for Data Science in Python course. This course focuses on “Data Preprocessing”. Mastering data cleansing is an essential skill for anyone entering the world of data science. Imagine: you are eager to extract insights and build models on a new data set, but you find that the data set is full of missing values, outliers, and inconsistencies. This is where data preprocessing skills come into play. By learning how to sort through messy data, you set yourself up for success. Clean data means accurate analytics, reliable models, and ultimately, more impactful insights. Also, this skill shows that you are serious in the competitive field of data science. So, embrace the data cleansing process – this course will help you unlock the true potential of your data!

What sets this course apart is our unique approach. We don’t just teach you standard methods. We show you the limitations of common approaches and the strengths of real-world applied techniques. This course provides a unique combination of theory and hands-on Python exercises that will help increase your confidence in dealing with any type of data. Additionally, we’ll help you review the basics of Python programming and learn to use popular libraries like NumPy, Pandas, and Matplotlib for efficient data preprocessing.

What you will learn:

Learn to properly clean your data for data science and machine learning projects.
For each topic, learn several approaches to performing data preprocessing – common versus applied approaches
Learn to handle missing values, handle outliers, feature scaling, feature selection, handle multiple correlation, outlier detection, handle unbalanced data.
In-depth theory along with hands-on exercises for all topics related to data preparation for data science and machine learning
Review of fundamental Python modules such as working with NumPy arrays, Pandas dataframes, data visualization with Matplotlib, Seaborn and basic statistics

Who is this course suitable for?

Data science students interested in data preprocessing, data preparation and data wrangling
Data science professionals who want to learn practical industry-level practices for data preprocessing, data preparation, and data curation.

Course specifications The A to Z of Data Preprocessing for Data Science in Python

Publisher: Udemy
Lecturer: Nash J
Training level: beginner to advanced
Training duration: 10 hours and 47 minutes
Number of courses: 167

Headlines of the course on 2/2024

Prerequisites of The A to Z of Data Preprocessing for Data Science in Python course

Access to Python using Google Colab, Jupyter Notebook or any other IDE
Familiarity with Python libraries like numpy and pandas though not mandatory, will be a plus.