2026 Python Data Analysis & Visualization Masterclass

Explore Jupyter Notebook, a web-based environment for interactive notebook documents that combine code, output, and visualizations. Discover multi-language support, including Python, and easy installation on Windows and Mac.

Learn to install Python data science tools on Mac using Anaconda, set up Jupyter Notebook, launch via Anaconda Navigator, and work in your browser with a focused notebook server.

Install Windows for Python data analysis by installing Anaconda and launching Jupyter Notebook via Anaconda Navigator, using a modern browser.

Verify pandas and matplotlib are installed in your Anaconda environment by launching Jupyter notebook, creating a Python 3 notebook, and importing pandas and matplotlib without errors.

Launch and navigate Jupyter notebooks, create new Python 3 notebooks, populate and run code cells, view outputs, and manage cell execution with run, control enter, and control return.

Learn how to manage Jupyter notebooks: use auto-save and checkpoints, shut down individual notebooks, quit the server, and restart to resume work.

Explore how notebook cells execute code, showing output only from the last evaluated expression, while all code runs and variables can be defined and later referenced.

Master command mode in notebooks by switching between command and editing modes, and using shortcuts like a, b, d d, z, c, x, v, and shift v to manage cells.

Explore markdown and code cells in notebooks. Learn markdown syntax for headings, bullets, links, and images, and switch between cell types to blend Python analysis with rich text.

Restart the kernel to recover from infinite loops and unresponsive code in notebooks, restoring execution without losing notebook. Understand the server–kernel interaction, and how to use run all and restart.

Leverage tab completion and shift-tab to view function signatures and full documentation directly in a notebook, from standard library modules like statistics to pandas read.csv, without leaving your code.

Practice hands-on with a Jupyter notebook: run and manage cells, insert and delete cells, restart the kernel, and swap or convert cells while debugging a simple Python task.

Master practical Jupiter notebook techniques: run and edit cells, switch to code or markdown, insert or delete cells, reorder them, and handle infinite loops with interrupts.

Explore how pandas analyzes, manipulates, and visualizes data, focusing on CSV basics—columns, rows, comma separated values—and handling variations like pipe delimiters in real datasets such as housing and Pokemon.

Master reading csv data into pandas data frames using read_csv. Explore two-dimensional data structures with rows and columns, demonstrated with states.csv and cake house data.

Inspect pandas data frames by examining columns, shape, and size in the King County housing dataset. Preview the first and last rows with head and tail, and explore display options.

Discover how pandas infers data types, from integers and floats to date objects and strings, using the info method and dtypes, and learn how data types affect memory usage.

Walk through the housing sales data set you'll use in this course, exploring sale price, bedrooms, living space, lot size, date sold, year built, and neighborhood 15-neighbor averages.

Explore the Titanic passenger CSV using pandas read_csv, inspect 14 columns and 1309 rows, and practice handling question marks as missing values and data types during data frame exploration.

Explore reading a pipe-separated Netflix titles dataset with pandas read_csv, specifying sep or delimiter, and handling index columns and missing values.

Learn how to import a csv with pandas read_csv using a custom names list and header=0 to override existing headers, then inspect the resulting dataframe's info, shape, and columns.

Practice pandas dataframes by reading multiple datasets, inspecting shapes and missing values, and handling a tab-separated movie titles dataset with specified columns.

Explore pandas dataframes by reading csv and tab separated data, inspect shape, head, tail, and missing values, and analyze data types and index columns for a movie titles dataset.

Explore sum and count on dataframes, using numeric_only to sum numeric columns, inspect totals such as total sales, total bedrooms, and Titanic survival counts, and count present values across datasets.

Learn how to compute mean, median, and mode with data frame methods that calculate these values for every column in a data frame, with examples from houses and Titanic.

Describe returns a data frame of descriptive statistics for numeric columns, listing count, mean, median, min, max, standard deviation, and percentiles all at once for datasets like Titanic and houses.

Describe with objects values by specifying include to reveal descriptive stats for non numeric columns, e.g., object data in Titanic and Netflix datasets, including count, unique, top, and frequency.

Apply basic dataframe methods to the best sellers dataset to compute min, max, mean, and describe; find lowest user rating, highest price, author with most books, and sum of reviews.

Explore basic DataFrame methods in pandas, including min, max, mean, head, mode, and describe, to analyze bestseller data and identify top authors like Jeff Kinney.

Learn to extract a single column from a pandas data frame, introduce the Series structure, and compare dot notation with square brackets for dynamic names and spaces in column labels.

Explore how a pandas series, a one-dimensional array with axis labels, differs from a data frame and use common methods like sum, min, and max on a column.

Explore key pandas series methods like head, tail, and describe, and see how a series returns a smaller series while describe yields numeric or object stats, including dtypes.

Explore unique and n unique on a pandas series to obtain unique values and their count, with dropna handling and practical examples from bedrooms, ratings, and zip codes.

Explore value_counts to count unique values in a series or data frame, yielding a labeled counts series you can sort, ascending, top-n, and plot to reveal distributions.

Explore how to plot with pandas using dot plots, value counts, and various kinds such as bar, pie, and scatter, including series versus data frame plots.

Analyze the bestsellers dot csv data set by retrieving book names and user ratings, then compute stats on authors, genres, and prices, and plot a genre pie chart and histogram.

Analyze the bestsellers dataset with pandas, extracting name, user rating, and author, and visualize with a pie chart, a top-ten authors bar chart, and a histogram of ratings.

Learn to manage indexes in pandas, including range indices and using set_index to label data frames and series by a date or other key.

Learn how to set the index of the World Happiness Report 2021 dataset to the country name after importing with read csv, using in place indexing.

Import the World Happiness Report with read_csv, using index_col to set country name as the index, producing a DataFrame indexed by country and ready for ladder score analysis.

Explore sort_values to order dataframe rows by a column and sort by index. Use ascending or descending options and in-place updates, including multi-column sorting.

learn to sort by multiple columns using a list of column names, for example sorting by price descending, then by bedrooms and bathrooms as secondary keys.

Demonstrate how sorting text columns in Pandas handles string comparisons, revealing case sensitivity, and use a key function (often a lambda) to lowercase values before sorting for consistent order.

Master sorting a data frame by index labels with sort_index, using in place and ascending options, and contrast this with sort_values for column-based sorting.

Sort values and sort index on series and value counts to shape bar plots. Use Titanic class data and bedroom counts to practice ascending and descending orders.

Learn how to use loc to access data by row labels in a dataframe, returning rows as series or dataframes, selecting single or multiple rows, and slicing by index.

Learn to access data by numeric position using iloc, select rows by index position, use slices for ranges, and contrast with label-based loc in a data frame.

Use loc and iloc to retrieve targeted rows and specific columns from dataframes and series, such as price and bedrooms, and optimize performance by narrowing columns upfront.

Practice indexing, sorting, and retrieval on a Pokemon dataset to compute averages, identify top attackers, and plot attack values, using in-place operations and name-based indexing.

Master indexing and sorting with pandas on the pokemon dataset: set name as index, sort by attack and num, and perform multi-level sorts by total then attack.

Filter dataframes with boolean series in pandas, using comparison operators to select Titanic rows by conditions like sex, survived, or class, via bracket indexing.

Learn to filter pandas data with comparison operators using boolean masks on the Titanic and house datasets, including not equals and greater/less than, with notes on age data types.

Explore how the between method filters data by range, selecting bedrooms 5 to 7 and grades 11 to 13 in the houses dataset, with boolean masks and value counts.

Use the isin method to filter a Netflix country column and create a boolean mask for India, Japan, and South Korea, then apply rating filters Tv-ma, R, and PG 13.

Learn to filter in pandas by combining conditions with the and operator, creating boolean masks from multiple criteria like Titanic sex and survived, or waterfront and price.

Combine conditions with the or operator in Python and pandas, using parentheses to group boolean expressions, with housing data and Netflix films directed by David Fincher or Martin Scorsese.

Apply bitwise negation to boolean series in pandas using the tilde operator to invert masks, with Titanic and homes examples illustrating when to use parentheses for correct negation.

Filter a data frame by missing values with isna() and notna() on a Netflix dataset; identify rows with no director or no cast using booleans and tilde inversion.

Filter Titanic data by sex to compare survival rates and visualize with value counts and bar or pie plots; then filter King County real estate for top multimillion-dollar zip codes.

Explore practical pandas filtering by reading a csv, filtering by author, price, year, genre, and rating, and generating bar plots for top authors and top reviews.

Master how to drop columns and rows in data frames using the drop method, with axis and inplace options, and learn column selection via the columns parameter.

Add static columns to a data frame by assigning a single value to all rows, and insert at a chosen position; demonstrated with the Titanic dataset and the houses dataset.

Learn to create dynamic columns in pandas by combining siblings and spouses with parents and children for Titanic data, then analyze via sorting and simple plots.

Create a new price per square foot column by dividing price by living square footage, then sort to identify the highest values and inspect top zip codes.

Set the Bitcoin price data index to date, compute open and close change and high and low delta, and plot the top ten deltas to visualize largest price changes.

Import Joe Biden tweets dataset, index by tweet id, drop url and a row, add a user column, compute ratio and interactions, rank ten by ratio and eight by interactions.

Master pandas data frame manipulation by importing csv data, setting the id column as the index, dropping the url column, adding user, computing a ratio, and identifying top interactions.

Learn to rename columns and index labels in a data frame using a mapper dictionary with the rename method, applying changes via columns or index and optional in-place updates.

Learn how to update values in a pandas series using the replace() method, including single and multiple mappings, in-place updates, and handling None with list syntax.

Master updating values with loc: select rows by label, update specific columns in place, and apply to multiple countries using a simple, row-wise syntax.

Learn to use loc indexing to create new columns and assign values to columns, updating the regional indicator to Nordic countries and adding an is cold column for selected rows.

Use loc with a boolean mask to select and update rows. Update bedrooms to too many and set luxury level to ultra for houses with a view of four.

Learn pandas data manipulation on a Netflix titles dataset: set index to show id, rename release year to release_year, relabel an index, adjust ratings, and manage is fave flags.

Demonstrates a pandas solution for updating a Netflix titles dataset: read with pipe separators, set index to show_id, update director and duration, rename columns, and mark favorites.