Speakers
Analyzing Data with Pandas
Learning Outcomes
Understand the fundamentals of Pandas
Explain what pandas is, its purpose, core concepts and basic usage
Read different data types
Import data from a variety of sources, including CSV, Excel and SQL databases to a DataFrame
Clean and manipulate datasets
Handle missing data, filter and selecting data, sorting and aggregating data and combining datasets
Analyze and explore data
Generate summary statistics and visualizing the dataset
Gain hands-on experience
Successfully complete practical exercises to reinforce theoretical knowledge and apply it to real-world scenarios
Modules
Introducing Pandas
- What is Pandas?
- Using pandas through Jupyter notebook
- Core Concepts and Essential Functionality
Data Loading and Storage
- Functions for reading data (read_csv)
- Data loading function arguments (delimiter, header)
- Methods for writing data (to_csv)
Data Cleaning and Preparation
- Handling missing data (drop, fill)
- Data transformation (drop duplicates, map, apply)
- String manipulation
Data Analysis
- Calculating summary statistics
- Aggregating data
- Combining and merging datasets
- Pivoting
Data Visualization
- Creating visualizations with pandas
- Matplotlib and seaborn
Hands-on Exercise
Requirements
Basic knowledge in Python
Basic knowledge with Jupyter Notebooks is preferred
Abdul-Rashid Sampaco III
Lead Engineer,
Samsung R&D Institute Philippines
Rashid is a data engineer, academician, and science researcher whose expertise span a multitude of industries. He finished his bachelor’s degree in Biochemistry and Master of Science in Chemistry at the University of the Philippines Diliman. Before his current role, he worked as a data scientist, developing ML models for monitoring subsea internet cables. Throughout his career, Rashid has extensively utilized Pandas for data analytics. What makes his perspective unique is that his journey with data began in the academe. As a science researcher in computational biochemistry, he first honed his data wrangling skills by using pandas to navigate the complexity of cheminformatics and molecular simulations. This diverse journey from pure science to applied engineering makes him the perfect guide to show us the practical power of Pandas today. Currently, he is a data engineer at Samsung R&D Institute Philippines (SRPH), where he develops and maintains critical data solutions and backend software for Machine Learning Operations (MLOps) projects, enabling teams to leverage data more effectively.
