• The art of defining problems.
• Setting goals with respect to а problem.
• Finding suitable data with respect to the goals.
• Scientific approach to problem solving.
• Defining hypotheses.
• JN as a research tool.
• What to expect from the Jupyter Notebook Mastery course.
• What is project Jupyter?.
• Initial setup and "Hello, Jupyter!".
• Getting to know JN’s interface. Shortcuts.
• Creating, modifying, and executing notebook cells.
• When to use JN and when not.
• What is NumPy?.
• NumPy arrays vs. Python lists.
• Array data types.
• Array attributes (shape, size and dtype).
• Basic array operations (indexing and slicing).
• Array arithmetic (element-wise operations).
• Aggregation and statistical functions.
• What is Pandas?.
• Pandas Series and DataFrames.
• Basic data exploration (head, tail and info).
• Accessing and indexing data.
• Handling missing data (NaN).
• Removing duplicates.
• Data types and conversion.
• Data aggregation and groupby operations.
• What is Matplotlib?.
• Creating and customizing basic plots (line plots and scatter plots).
• Adding titles, labels, and legends to plots.
• Saving plots as images.
• Customizing plot styles and colors.
• Working with multiple subplots.
• Plotting categorical data (bar charts and pie charts).
• Plotting time series data.
• The importance of data storytelling in decision-making.
• Identifying the story in data.
• Storytelling structure and narrative techniques.
• Storyboarding and structuring а story.
• Adding context and background information.
• Formatting text in а notebook.
• Embedding external media.
• Rise – using JN as a presentation tool.