Data Wrangling Techniques with Python

Course Syllabus

This course consists of the following modules:

Module 1 – Introduction to Data Wrangling
Learn the fundamentals of data wrangling and why it is critical for effective data analysis, while gaining an overview of the Python tools used in the process.

Module 2 – Data Cleaning and Pre-processing
Explore how to handle missing values, duplicates, and outliers, ensuring that raw data is refined into accurate and reliable formats for analysis.

Module 3 – Data Transformation and Reshaping
Understand how to reshape data into different structures and apply transformations that make datasets more usable for modelling and reporting.

Module 4 – Merging and Combining Datasets
Master techniques to join, merge, and concatenate datasets, enabling you to work with multiple data sources seamlessly.

Module 5 – Data Visualization for Wrangling Insights
Discover how visual exploration can highlight data quality issues and patterns, supporting more effective wrangling strategies.

Module 6 – Automating Data Wrangling Tasks
Learn how to automate repetitive wrangling tasks using Python scripts, saving time and increasing efficiency in your workflow.

Module 7 – Wrangling Complex Data Types
Gain skills in handling unstructured and semi-structured data, such as JSON and text files, often used in real-world projects.

Module 8 – Capstone Project: Real-World Data Wrangling
Apply everything you have learned to a practical project, demonstrating your ability to manage, clean, and prepare data for analysis.

Career Path

Completing this course in Data Wrangling Techniques with Python opens up excellent opportunities in the fields of data science, machine learning, and business analytics. Graduates can pursue roles such as Data Analyst, Data Scientist, Business Intelligence Specialist, or Python Developer. With the growing reliance on big data across industries like finance, healthcare, marketing, and technology, demand for professionals with strong dataset handling skills continues to rise. This course also serves as a strong foundation for learners aiming to advance into machine learning, predictive analytics, or big data engineering, giving you a competitive advantage in the global job market.

Endorsement

Upon successful completion of the course, candidates will have two certification options:

• Option 1: Certificate issued by CPD Courses (cpdcourses.com).
• Option 2: Accredited CPD Certificate issued by the CPD Standards Office, a globally recognised organisation.

Both certificates validate your professional development and demonstrate your expertise in data wrangling to employers worldwide.

FAQs

Course-Specific FAQs

What programming language is used in this course?
The course uses Python as the primary programming language, as it is widely recognised for data analysis and wrangling tasks.

Do I need prior knowledge of machine learning?
No prior experience in machine learning is required. Basic familiarity with Python will be helpful, but beginners can follow the course comfortably.

Can I complete this course at my own pace?
Yes, the course is designed for flexible and self-paced study, allowing you to complete lessons according to your own schedule.

What is data wrangling in Python?
Data wrangling in Python is the process of cleaning, transforming, and preparing raw datasets into structured formats that can be easily analysed.

What is data wrangling and types of data wrangling techniques?
Data wrangling involves refining data by cleaning, merging, transforming, and reshaping it. Common techniques include dataset cleaning, transformation, integration, and validation.

Which tool is used for data wrangling?
Python is one of the most popular tools for data wrangling, particularly using libraries like Pandas and NumPy for efficient data handling.