What you will learn in Python for Data Science, AI Development By IBM Course
- Understand Python basics, including data types, variables, expressions, and string operations.
- Utilize Python data structures such as lists and tuples, and apply logic concepts like conditions and branching.
- Work with Python libraries like Pandas, NumPy, and Beautiful Soup for data manipulation and web scraping.
- Perform tasks such as data collection and automation using Python.
- Develop and test Python code using Jupyter Notebooks.
Program Overview
Python Basics
⏱️2 hours
Introduction to Python and Jupyter Notebooks.
Understanding data types, expressions, variables, and string operations.
Python Data Structures
⏱️4 hours
Exploring lists, tuples, dictionaries, and sets.
Manipulating data structures and understanding their applications.
Python Programming Fundamentals
⏱️ 5 hours
Implementing conditions, branching, loops, and functions.
Understanding objects and classes in Python.
Working with Data in Python
⏱️ 7 hours
Using Pandas and NumPy for data analysis.
Reading and writing files, and working with APIs.
Python Project
⏱️ 7 hours
- Applying learned skills in a hands-on project.
- Demonstrating proficiency in Python for data science tasks.
Get certificate
Job Outlook
- Proficiency in Python is essential for roles such as Data Analyst, Data Scientist, and AI Developer.
- Skills acquired in this course are applicable across various industries, including technology, healthcare, finance, and more.
- Completing this course can enhance your qualifications for entry-level positions in data science and AI development.
Specification: Python for Data Science, AI & Development By IBM
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FAQs
You’ll develop practical Python capabilities, including:
- Core programming fundamentals like variables, strings, loops, functions, OOP, and exception handling.
- Essential data structures including lists, tuples, dictionaries, and sets.
- Data manipulation with NumPy and Pandas, and file handling (CSV, JSON, etc.).
- API interaction and web scraping techniques using libraries like
requestsand BeautifulSoup.
Strengths:
- Developed by IBM, with practical, hands-on labs in Jupyter Notebooks.
- Strong learner reviews (e.g., 4.7/5 rating).
Limitations:
- Focuses on Python fundamentals—does not teach advanced AI, ML, or data visualization applications.
- Better suited as an introductory course rather than a deep dive.
- Ideal for aspiring data scientists, AI developers, analysts, or software professionals, especially those entering data-focused fields.
- Teaches real-world Python skills applicable across careers in tech, AI, data engineering, and automation.
- You’ll earn a shareable certificate from IBM, which can enhance your portfolio or resume.
