Beginners Guide to Data Transformations in Python

Overview:

Data rarely comes in a perfect format for analysis. CSV files, APIs, databases, and spreadsheets often contain missing values, inconsistent formats, and structural issues that make reporting and modeling difficult. Python, with its rich ecosystem of libraries, provides powerful tools to transform and prepare this data for analysis.

This beginner-friendly session introduces essential techniques for data transformation in Python. You’ll learn how to clean data, handle missing or inconsistent values, reshape tables, merge datasets, and prepare them for reporting or advanced analytics. Using practical examples with libraries like Pandas, you’ll gain hands-on experience in creating reproducible, reliable, and efficient data pipelines. By the end of the session, you’ll be ready to take raw datasets and turn them into analysis-ready data.

Why you should Attend:

Raw data is messy, inconsistent, and full of hidden pitfalls. Without proper transformation skills, even small datasets can produce inaccurate insights, broken analyses, or slow workflows. In “Beginner’s Guide to Data Transformations in Python”, you’ll learn how to clean, reshape, and prepare your data efficiently using Python - before bad practices become technical debt that’s hard to fix.

Areas Covered in the Session:

• Introduction to Python libraries for data transformation (Pandas, NumPy)
• Importing data from CSV, Excel, and databases
• Handling missing or inconsistent values
• Renaming, reordering, and changing data types
• Filtering data
• Merging, joining, and concatenating datasets
• Writing reproducible and maintainable transformation scripts
• Step-by-step demonstration: from raw data to analysis-ready datasets

Who Will Benefit:

• People who (start to) work with Power BI