1. Python Basics: Introduction to Python programming, covering fundamental concepts like data types, variables, loops, and conditional statements.

2. Data Structures in Python: Understanding lists, dictionaries, sets, and tuples for data organization and manipulation.

3. Python Libraries for Data Science:

   • Pandas: For data manipulation and analysis, especially with tabular data.
   • NumPy: For numerical computations and working with arrays.
   • Matplotlib and Seaborn: For data visualization and graphical plotting.

4. Data Analysis and Visualization: Hands-on experience with real datasets to perform exploratory data analysis (EDA), data cleaning, and visualization.

5. Introduction to Machine Learning: Basics of machine learning algorithms and how to implement them using Python, particularly with the Scikit-Learn library.

Course Format and Resources

• Online Learning Modules: Typically includes video lectures, readings, and interactive coding exercises.
• Hands-on Projects: Practical projects that allow you to apply what you’ve learned to real-world data scenarios.
• Quizzes and Assessments: To test your understanding and help solidify your knowledge.
• Discussion Forums: For interacting with peers and instructors, which is beneficial for collaborative learning and networking.

Benefits of the IBM Course

1. Industry-Relevant Curriculum: The content is designed to be in line with current industry needs and trends.

2. Expert Instructors: Courses are often taught by experienced professionals with deep knowledge of the subject.

3. Flexibility: Being online, it allows you to learn at your own pace and schedule.

4. Certification: Upon completion, learners usually receive a certificate which can be added to your resume and LinkedIn profile.

Who Can Benefit

• Beginners in Data Science: Ideal for those starting their journey in data science and looking to build a strong foundation.
• Professionals Looking to Upskill: Beneficial for professionals in other fields who want to learn data science skills to enhance their work or transition into a data science role.