The A to Z of Data Preprocessing for Data Science in Python
The A to Z of Data Preprocessing for Data Science in Python, Master practical methods to handle outliers, multicollinearity, scaling, encoding, transformation, anomalies, and more!
Course Description
This The A to Z of Data Preprocessing for Data Science in Python course focuses on Data Preprocessing. Mastering data cleaning is an absolute must for anyone venturing into the world of data science. Picture this: you’re diving into a new dataset, eager to extract insights and build models, only to find it’s riddled with missing values, outliers, and inconsistencies. Sound familiar? That’s where data preprocessing skills come in handy. By learning how to wrangle messy data into shape, you’re setting yourself up for success. Clean data means accurate analyses, reliable models, and ultimately, more impactful insights.
Plus, it shows you’re serious about your craft, which can go a long way in a competitive field like data science. So, embrace the data cleaning process—this Data Preprocessing course helps you unlock the true potential of your data! What sets this course apart is our unique approach. We don’t just teach you the standard methods. We show you the limitations of common approaches and the strengths of practical, real-world techniques.
This Data Preprocessing for Data Science in Python course provides you a unique blend of theory and hands-on exercises in Python which will help boost your confidence while dealing with any type of data. In addition, we’ll help you refresh Python programming basics and learn to leverage popular libraries like NumPy, Pandas, and Matplotlib for efficient data preprocessing.
What you’ll learn
- Learn how to clean your data the right way for Data Science and Machine Learning Projects
- For each topic learn multiple approaches to perform Data Pre-processing – Common Approaches vs Practical Approaches
- Learn Missing Value Treatment, Outlier Treatment, Feature Scaling, Feature Selection, Multicollinearity Treatment, Anomaly Detection, Imbalanced Data Treatment
- In-depth Theory plus Hands-on exercises for all topics related to Data Preparation for Data Science and Machine Learning
- Refresh the foundation Python modules like working with Numpy arrays, Pandas data frames, Data Visualization using Matplotlib, Seaborn, and Basic Statistics
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Who this course is for:
- Data Science students who are interested in Data Preprocessing, Data Preparation, Data Wrangling
- Data Science practitioners who want to learn the practical industry level practices for Data Preprocessing, Data Preparation, Data Wrangling
