Location

Community Services & Continuing Education - Alexandria

Objectives

• • Gain foundational and advanced skills in data processing and manipulation using NumPy and Pandas.• Learn to clean, organize, and transform data effectively for analysis and machine learning tasks.• Develop skills in data visualization with Matplotlib and Seaborn to communicate insights effectively.• Master feature engineering techniques to enhance data quality and extract meaningful patterns for predictive models.

Outcomes

• Proficiency in NumPy: Use NumPy for efficient data processing, including array manipulation, data cleaning, and handling complex numerical operations.• Data Cleaning Expertise: Apply advanced data cleaning techniques to prepare high-quality datasets.• Mastery of Pandas for Data Manipulation: Organize, filter, and aggregate data effectively using Pandas, performing both basic and advanced manipulations.• Feature Engineering Skills: Develop and transform features using Pandas for better predictive modeling and data analysis.• Data Visualization Skills: Create informative visualizations with Matplotlib and Seaborn, effectively communicating data insights and patterns.• Advanced Visualization Techniques: Use Seaborn to produce complex visualizations that provide deep insights into data distributions and relationships.

Course Contents

• Introduction to NumPy for Data Processing: Covers the basics of NumPy, including arrays, indexing, slicing, and basic operations. Focuses on leveraging NumPy’s computational power for efficient data processing.• Advanced Data Cleaning with NumPy: Explores advanced techniques for handling missing values, outliers, and cleaning data using NumPy. Prepares data for further analysis by addressing common data quality issues.• Data Manipulation Basics with Pandas: Introduces data manipulation using Pandas, including DataFrames, basic filtering, grouping, and aggregation. Provides tools for organizing and exploring datasets.• Advanced Data Processing and Feature Engineering with Pandas: Covers advanced Pandas techniques, including multi-indexing, merging, and feature engineering. Focuses on creating and transforming features to improve data analysis and machine learning models.• Data Visualization with Matplotlib: Introduces data visualization using Matplotlib, covering essential plots