[100% Off] Certified Data Wrangling &Amp; Cleaning
Pandas Data Mastery: Clean Data, Handle Missing Values, Feature Engineering, and Build Scalable Preparation Pipelines.
What you’ll learn
- Efficiently load
- merge
- and reshape complex datasets using the robust features of the Pandas library.
- Implement effective strategies for identifying and imputing various types of missing data (NaN
- null
- custom placeholders
- mechanism identification).
- Detect and handle statistical outliers using Z-scores
- IQR
- and visual diagnostic tools suitable for modeling.
- Transform categorical variables into suitable numerical formats using best practices like one-hot encoding and target encoding.
- Clean and parse unstructured text data and time series features efficiently using regular expressions and datetime operations.
- Apply normalization and scaling techniques (MinMax
- standardization) essential for preparing data for machine learning models.
Requirements
- Basic operational knowledge of Python programming (variables
- loops
- functions)
- Familiarity with the Jupyter Notebook or similar interactive Python environment
- A foundational understanding of basic descriptive statistics (mean
- median
- standard deviation)
Description
The Foundation of Data Science Success: Certified Data Preparation80% of a Data Scientist’s time is spent cleaning and preparing data. This course is designed to equip you with professional-level skills to dramatically reduce that time, ensuring your analytical models are built upon high-quality, reliable datasets. We move beyond basic tutorials, focusing heavily on efficiency, scalability, and certification-level preparedness in data wrangling.
Comprehensive Skill Mastery: Wrangling & CleaningYou will achieve deep mastery of the core Python data stack, primarily Pandas and NumPy, applied directly to messy, real-world data scenarios. This course covers the entire lifecycle of data preparation: from initial ingestion and exploration (profiling) to advanced imputation, transformation, and feature creation. Learn how to systematically identify and correct common data quality issues such as inconsistent formatting, statistical outliers, duplicated entries, and temporal inconsistencies.
Advanced Techniques and PipelinesThis specialization ensures you can build robust and repeatable data cleaning pipelines. You will learn how to integrate tools like scikit-learn’s ColumnTransformer to handle heterogeneous data types efficiently, allowing you to deploy preprocessing steps reliably across multiple datasets. This structured approach is essential for any modern machine learning or data engineering workflow.
What Makes This Course Unique?Unlike theoretical courses, this certification focuses on practical application, using real, dirty datasets that mimic industry challenges. We emphasize vectorized operations and efficient memory usage, crucial for handling big data. Completing this course will not just provide knowledge, but a demonstrable portfolio of certified data preparation techniques, making you a top candidate for Data Analyst and Data Scientist roles.
