Certificate in Python for Data Wrangling and Preprocessing

This course is designed for data analysts, researchers, and IT professionals looking to enhance their data handling skills. It provides a solid foundation in Python for data wrangling and preprocessing, covering essential libraries like pandas and NumPy. Participants will learn to clean, transform, and prepare data for analysis.

Upon completion, learners will be proficient in using Python for data manipulation tasks, capable of handling messy datasets, and well-prepared to support data-driven decision-making processes.

1. 1. Introduction to Python: Learners will be introduced to the Python programming language, its syntax, and basic programming concepts. They will gain foundational skills in writing, running, and debugging Python code.
2. 2. Data Types and Structures: This module covers Python’s data types, such as strings, lists, tuples, and dictionaries, and their practical applications in data manipulation. Learners will learn to manipulate and transform data using these structures.
3. 3. Data Input and Output: Learners will study how to read and write data to various file formats, including CSV, JSON, and Excel files. They will also learn to handle data from web APIs and databases.
4. 4. Data Wrangling with Pandas: This module focuses on using the Pandas library for data wrangling tasks such as cleaning, transforming, and merging datasets. Learners will gain practical skills in preparing data for analysis.
5. 5. Data Cleaning Techniques: Learners will explore techniques for handling missing data, removing duplicates, and correcting errors in datasets. They will learn how to apply these techniques to real-world data to ensure data quality.
6. 6. Data Transformation and Manipulation: This module covers advanced data transformation techniques, including sorting, filtering, and applying functions to data frames. Learners will learn how to efficiently manipulate data for analysis.
7. 7. Feature Engineering: Learners will study how to create new features from existing data to improve the performance of machine learning models. They will gain skills in feature selection, creation, and validation.
8. 8. Data Preprocessing: This module focuses on preparing data for machine learning models, including scaling, normalization, and encoding categorical variables. Learners will learn how to preprocess data to enhance model performance.
9. 9. Introduction to Machine Learning: Learners will be introduced to basic machine learning concepts and algorithms. They will learn how to preprocess data for machine learning tasks and understand the importance of data preprocessing in model performance.
10. 10. Capstone Project: In this final module, learners will apply their skills from previous modules to a real-world dataset. They will choose a project, perform data wrangling and preprocessing, and prepare the data for machine learning analysis.