Executive Development Programme in Python Data Wrangling Essentials

This course is designed for professionals seeking to enhance their data wrangling skills using Python. Ideal for data analysts, business intelligence experts, and IT professionals, it equips participants with essential Python libraries like Pandas and NumPy to clean, preprocess, and manipulate data efficiently.

By the end of the program, learners will gain the ability to automate data cleaning tasks, handle missing data, merge datasets, and prepare data for analysis. Practical hands-on sessions ensure that theoretical knowledge is applied effectively in real-world scenarios, enabling participants to deliver high-quality data insights.

1. 1. Introduction to Python for Data Wrangling: Learners will understand the basics of Python programming and its libraries essential for data wrangling. They will gain skills in setting up the Python environment and writing basic scripts for data manipulation.
2. 2. Data Types and Structures in Python: This module covers Python data types and structures like lists, dictionaries, and sets. Learners will learn how to effectively use these structures to store and manipulate data.
3. 3. Data Input and Output: Here, learners will study how to read and write data from various sources including CSV, JSON, and Excel files. They will gain practical skills in handling different file formats and integrating data from multiple sources.
4. 4. Data Cleaning Techniques: This module focuses on cleaning and preprocessing data. Learners will learn how to handle missing values, remove duplicates, and correct inconsistencies using Python.
5. 5. Data Transformation: Learners will explore techniques for transforming data, including sorting, filtering, and applying functions to columns. They will understand how to perform advanced data transformations to prepare data for analysis.
6. 6. Data Aggregation and Grouping: This module covers methods for aggregating and grouping data to extract meaningful insights. Learners will learn to use groupby operations and summarize data effectively.
7. 7. Data Joining and Merging: This module delves into joining and merging datasets from different sources. Learners will gain skills in performing inner, outer, left, and right joins to consolidate data.
8. 8. Data Visualization Basics: Here, learners will learn the fundamentals of data visualization using Python libraries like Matplotlib and Seaborn. They will gain skills in creating basic plots and charts to visualize data.
9. 9. Advanced Data Wrangling Techniques: This module covers more advanced techniques such as handling time series data, dealing with large datasets, and implementing efficient data processing strategies.
10. 10. Project: Comprehensive Data Wrangling: In this final module, learners will apply all the concepts learned in a real-world project. They will work on a comprehensive data wrangling task, from data collection to analysis, to gain hands-on experience in Python data wrangling.