Data Wrangling with Python Workshop by Tonex
This comprehensive workshop by Tonex equips participants with the essential skills to proficiently handle, clean, and transform raw data using Python. Through hands-on exercises and real-world scenarios, attendees will gain mastery in data wrangling techniques, ensuring data is prepared for analysis and decision-making.
The “Data Wrangling with Python Workshop” by Tonex offers a comprehensive learning experience for data professionals. Participants will master essential skills in data manipulation using Python, focusing on Pandas and NumPy libraries. This hands-on workshop covers data cleaning, preprocessing, and advanced techniques like merging and pivoting.
Attendees will gain proficiency in handling time-series data, optimizing large datasets, and applying learned concepts to real-world scenarios. Tailored for data analysts, scientists, and business professionals, this workshop ensures participants are well-equipped to transform raw data into actionable insights, making informed decisions in their respective domains.
Learning Objectives:
- Understand the importance of data wrangling in the data analysis process.
- Acquire proficiency in using Python libraries for data manipulation, such as Pandas and NumPy.
- Learn how to clean and preprocess data to address missing values and outliers.
- Gain insights into transforming data structures and reshaping datasets for analytical purposes.
- Explore advanced data wrangling techniques, including merging, joining, and pivoting.
- Develop skills in handling time-series data and extracting meaningful features.
- Understand best practices for handling large datasets efficiently.
- Apply data wrangling concepts to real-world scenarios and business use cases.
Audience: This workshop is designed for data analysts, scientists, and professionals seeking to enhance their data manipulation skills using Python. It is also suitable for business analysts, researchers, and anyone working with data for decision-making.
Course Outline:
Introduction to Data Wrangling with Python
- Overview of data wrangling
- Importance in the data analysis pipeline
- Python as a tool for data manipulation
Working with Pandas for Data Manipulation
- Introduction to Pandas library
- Series and DataFrame basics
- Data indexing and slicing
- Handling missing data
Cleaning and Preprocessing Data
- Identifying and handling outliers
- Imputing missing values
- Data normalization and scaling
Transforming Data Structures
- Reshaping datasets using melt and pivot
- Combining and concatenating datasets
- Handling duplicate and redundant data
Advanced Data Wrangling Techniques
- Merging and joining datasets
- Hierarchical indexing
- Handling categorical data
Working with Time-Series Data
- Introduction to time-series data
- Date and time manipulation
- Resampling and aggregating time-series data
Efficient Handling of Large Datasets
- Chunking and processing large datasets
- Memory optimization techniques
- Parallel processing for efficiency
Real-World Applications and Case Studies
- Applying data wrangling skills to practical scenarios
- Business use cases and success stories
- Best practices for maintaining clean and well-prepared datasets.