Python for Data Science Immersive

Python Fundamentals

Python Fundamentals: Variables & Data Types

• Declare variables of basic types: integers, floats, strings, booleans
• Perform input/output with print() and input()
• Apply arithmetic, relational, and logical operators

Control Flow I: Conditional Logic

• Use comparison operators ==, !=, <, >, <=, >=
• Write if/else and nested conditionals
• Combine conditions with and/or for complex logic

Control Flow II: Loops & Iteration

• Implement for loops over ranges and lists; understand iterables
• Understand map and filter operations.
• Use list comprehensions to simplify operations.

DataFrames & Data Manipulation with Pandas

• Construct DataFrames from various data formats via pd.DataFrame()
• Concatenate multiple DataFrames using pd.concat()
• Inspect DataFrame shape and handle missing values (NaN)
• Perform Pandas data analysis operations to glean insight

Data Visualization: Charting Basics

• Plot time series with plt.plot() for line charts
• Create scatter plots using plt.scatter() to reveal correlations
• Decide between line vs. scatter based on data context and purpose

Trend Analysis with Regression Lines

• Understand least-squares regression concept and its interpretation
• Compute a best-fit line via numpy.polyfit()
• Overlay regression lines on scatter plots and make predictions

Advanced Plot Customization

• Annotate charts with titles, axis labels, and legends
• Highlight key data points (e.g., min/max) directly on plots
• Use stacked bar charts, pie charts, and animated charts to visualize data

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• Students may cancel up to 11 business days before the class/program start date and receive a refund, less the registration fee.
• Cancellations within 11 business days are not permitted; however, students may reschedule up to 4 business days before the class start date.

Note: Any refunds must be requested within 180 days from the original payment date; courses rescheduled within 11 business days of the start date are not eligible for refunds.