Description
A to Z Data Science Preprocessing Course in Python This course focuses on the topic of Data Preprocessing. Mastering data cleaning is an essential skill for anyone entering the world of data science. Imagine this: you want to extract valuable information and build models based on a new data set, but you discover that the data set is full of missing values, outliers, and inconsistencies. This is where data preprocessing skills come into play. By learning to sort through cluttered data, you’ll set yourself up for success. Clean data means accurate analytics, reliable models and, ultimately, more actionable information. Additionally, this skill shows that you are serious about 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’ll show you the limitations of common approaches and the strengths of real-world applied methods. This course is a unique combination of theory and practical exercises in Python that will help increase your confidence in working with any type of data. In addition, we will help you learn the basics of Python programming and learn how to use popular libraries such as NumPy, Pandas, and Matplotlib for efficient data preprocessing.
What you will learn:
- Learn how to properly clean data for data science and machine learning projects.
- For each topic, explore several approaches to data preprocessing—general and applied approaches.
- Learn to handle missing values, handle outliers, feature scaling, feature selection, handle multiple correlation, detect outliers, handle imbalanced data.
- In-depth theory coupled with practical exercises on all topics related to data preparation for data science and machine learning.
- Overview of fundamental Python modules such as working with NumPy arrays, Pandas data frames, 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 science
- Data science professionals who want to learn practical industry practices for data preprocessing, preparation, and storage.
Course Specifications Data Preprocessing A to Z for Data Science in Python
- Publisher: Udemy
- Lecturer: Nash J.
- Level of training: from beginner to advanced
- Duration of training: 10 hours 47 minutes
- Number of courses: 167
Course Headings 2/2024
Prerequisites for the course “Data Preprocessing A to Z for Data Science in Python”
- Access Python using Google Colab, Jupyter Notebook or any other IDE.
- Familiarity with Python libraries such as numpy and pandas, although not required, is a plus.
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