Python 101 for Data Science – IBM
Python 101 for Data Science by IBM (via Cognitive Class) is an entry-level course designed to introduce learners to programming fundamentals and the practical use of Python for data analysis. It laid the foundation for my journey into data science, helping me understand core programming concepts and hands-on implementation using real-world datasets.
Through an interactive Jupyter Notebook environment, I gained the ability to write Python scripts, manipulate data structures, and perform exploratory data analysis — essential skills for anyone working in data-driven fields.
Category
Programming
Issued By
IBM (via Cognitive Class)
Issue Date
June 10th, 2020
No Expiry
July 14th, 2026
Certification ID
dda715ce5710442785475bb2cca826a0
Verification Links
Course Curriculum
The course was divided into 4 structured modules, providing a progressive learning path from Python fundamentals to applied data handling.
Module 1 – Python Basics
Types, expressions, variables, and string operations.Module 2 – Python Data Structures
Lists, tuples, sets, and dictionaries.Module 3 – Python Programming Fundamentals
Conditional statements, loops, functions, objects, and classes.Module 4 – Working with Data in Python
Reading and writing files, loading data with Pandas, manipulating and saving datasets using Pandas.
Key Learning Outcomes
Understood and applied Python syntax and programming logic effectively.
Manipulated and analyzed datasets using Pandas within Jupyter Notebook.
Built confidence to automate basic data tasks and write reusable Python code.
Gained exposure to the workflow used in data science and analytics projects.
Practical Application
The course provided a strong technical foundation that I’ve since applied in multiple professional contexts — from automating repetitive audit procedures to analyzing structured data during control testing and GRC reviews.
The understanding of data handling, file operations, and scripting logic continues to help me translate raw data into actionable insights during IT audit and risk assessment engagements.
Conclusion
This certification served as my first major step into the programming and data science domain.
It not only strengthened my analytical and problem-solving skills but also built a bridge between data analytics and cybersecurity/audit, shaping a multidisciplinary approach I continue to apply in my professional work.