This course will aim to build the necessary ground in concepts, tools, and techniques required to effectively learn and practice the topics that will be covered in subsequent courses.
The primary goal of this course is to ensure that we are able to work with data and extract observations and insights from that. This course will also focus on some fundamental statistical concepts required to draw meaningful inferences from the data. By the end of this course, we should have good foundations in both python and statistics/mathematics to better appreciate the subsequent courses in the program.
- Python for Data Science Foundations
- NumPy and Pandas: Operations and functions to work with data
- Pandas DataFrames & Series: Operations and applications
- Visualization: Matplotlib, Seaborn
- Fundamentals of Probability
- Introduction to probability
- Rules for computing probability
- Marginal Probability and example
- Inferential Statistics
- Introduction to Inferential Statistics
- Fundamentals of Probability Distributions
- Foundations of Sampling and Inference
- Central Limit Theorem
- Estimation
One hands-on project to be submitted at the end of the course.