Learn Python + Pandas for data cleaning, profiling & EDA, and prep data for machine learning & data science with Python
InstructorMaven Analytics • 1,500,000 Learners
CouponThe curriculum covers the full lifecycle of data preparation, including scoping, gathering, cleaning, and preparing datasets for modeling.
Lessons utilize a pattern of assignments followed by solution videos to reinforce concepts like data type conversion and handling missing values.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
A presentation slide introducing a course project for Maven Music, detailing the situation, assignment, and five specific data science objectives.
Course slide illustrating the data science workflow with a numbered six-step diagram covering scoping, data preparation, modeling, and sharing insights.
Presentation slide titled Data Issues Overview displaying a sample table with highlighted rows illustrating duplicates, outliers, typos, and missing data.
A Jupyter notebook interface displaying Python code cells for calculating customer cancellation rates and a data table output.
One signal suggests that datasets used in final modules may occasionally diverge from the data shown during video demonstrations. This signal predates the displayed update date, and while an update has occurred, it does not prove this issue was resolved.
A signal indicates that certain Pandas functions, such as get_dummies and pd.to_datetime, may require updated handling in current environments. This signal predates the displayed update date; an update may have addressed this, but the label does not prove a correction.
The curriculum aligns with the goal of preparing data for machine learning through Python and Pandas.