Scientific Python: Data Science Visualization Bundle 18 Hrs!

Explore core Python data types—int, float, string, boolean—along with printing, variables, input, arithmetic, and type conversion; master lists, dictionaries, sets, tuples, and basic control flow.

Explore NumPy's high-performance, homogeneous, multidimensional arrays and built-in tools for algebra, transforms, and interfacing with C, C++, and Fortran, highlighting memory efficiency over Python lists.

Create NumPy arrays, understand homogeneous data types, axis, shapes, and rank, and convert lists into arrays while specifying complex types using NumPy as np.

Learn to create NumPy arrays from lists, initialize with zeros or fixed values, generate random numbers and random integers, and produce sequences of equally spaced numbers.

Learn to reshape numpy arrays while preserving element counts, forming 2x4 or 4x2 shapes, and flatten multidimensional arrays to one dimension.

Explore NumPy indexing techniques, including positive indexing across axes, slicing to extract rows and columns, and advanced boolean and integer indexing for efficient data access.

Explore numpy operations on an area, including element-wise, unary, and binary operations, and universal functions like sine and exponential applied to each element.

Explore numpy sorting techniques by demonstrating array sorting with axis options, column-wise sorts, and a global sort of all elements, showing how to produce ordered data efficiently.

Learn how to stack and split arrays with numpy, using vertical and horizontal stacking, column and row stacking, and concatenate for multidimensional data along specified axes.

Learn how NumPy broadcasting handles arithmetic with arrays of different shapes, extending smaller arrays to match larger ones along an axis to enable efficient binary operations and memory-efficient computations.

Learn to work with daytime 64 in NumPy, convert strings to daytime objects, extract year, month, generate date ranges, and sort dates to analyze time data.

learn how to solve linear equations with a matrix method in numpy, constructing coefficient and constants matrices, and applying linear algebra to compute rank, determinant, inverse, and matrix exponentiation.

Explore saving and loading numpy arrays to files, using the npz zip format for multiple arrays, and load them back with numpy.

Explore pandas, an open source high-performance library that lets you operate on tabular data with data frames built from rows, columns, and a unique index, including installation and import verification.

Read data from csv files with pandas, load into a data frame, and preview the first rows to understand columns, index, and data types.

Create a pandas data frame from two lists by zipping them into columns, assign a custom index, and save the frame to a file, illustrating default versus custom indexing.

Explore how pandas data frames organize data into columns, retrieve column names, and select single or multiple columns, distinguishing series from data frames and understanding indices.

Apply a function to every value in the title column of a dataframe using pandas apply, turning titles into uppercase and updating the column accordingly, demonstrated on imdb movie data.

Learn to plot a series and a data frame in pandas with the plot function, using matplotlib and datetime-aware axes for visualizing Montreal cycling data.

Use pandas value_counts to count unique values in a column, create a frequency table for genres, and visualize results with bar and pie charts.

Explore Pandas indexes and indexing, including time index and integer location based indexing, and learn how to slice and access rows by label or position.

Learn to filter a pandas DataFrame using boolean indexing and queries to keep rows by value, such as genre equals 'adventure' and star rating greater than eight.

Delete rows and columns in pandas data frames using drop, with axis and in-place options. Remove unused columns, drop rows by index, and filter by conditions like star rating.

Explore the group by operation in pandas: split data by a key, apply aggregation, transformation, or filtration, and combine results to summarize and compare genres and durations.

Matplotlib is a Python plotting library for publication quality figures in various formats, usable from scripts or notebooks, covering bar charts, histograms, scatterplots, pie charts, subplots, and customization.

Learn to create a basic line plot in python with matplotlib, customize axes, labels, title, and markers, apply styles and colors, and show the plot.

Plot bar charts in Python using Matplotlib to visualize data with vertical bars and labeled x axes. Explore horizontal bars, side-by-side comparisons, colors, labels, and legends to compare datasets.

visualize data with histograms in python using matplotlib, showing frequency as bar heights and class intervals, with range, bins, and color options.

Explore how to create a two-dimensional scatterplot with Matplotlib, plotting data points on the x and y axes, and customize markers with size, color, shape, and legend labels.

Learn to create a pie chart in Python using the matplotlib library, detailing slices, labels, colors, explode effects, and optional percentage formatting with a legend and start angle adjustments.

Generate x values from 0 to 2 in 0.1 steps, compute y with a universal function, and plot a smooth curve and optional axes lines in matplotlib.

Learn to create subplots in a single figure with Matplotlib using add_subplot for uniform plots, then use a grid approach with coordinates, spans, and spacing.

Define regular expressions as concise patterns that specify a set of strings for searching, matching, and extracting text in Python.

Learn how to import the Python re module, compile patterns into pattern objects, and use match, search, findall, and finditer with flags and escapes for reliable text pattern matching.

Learn to handle backslashes in Python regex, using raw strings and re.escape to match Windows paths such as C Windows System 32, with proper escaping.

Explore how Python regular expressions use character classes to match multiple spellings, define character sets and ranges, negate characters, and leverage predefined classes to highlight matches.

Master regular expression alteration by using the pipe symbol to match multiple patterns at a position, compare it with character classes, and group options with parentheses for precise matches.

Learn how quantifiers in Python regular expressions control repetition, including ? * + and {n,m}, with examples like dog vs dogs, dot escaping, and four-digit numbers.

Explore regex greedy and non-greedy quantifiers in Python, using patterns like <.*> and <.*?> to capture HTML tags as separate matches and understand quantifier behavior.

Explore python regex boundary matches to locate whole words like and or the, using word boundaries and start and end markers (^ and $), with multiline support for line-based matching.

Use Python's re.split to break text into lines and words by patterns, including newline and non-alphanumeric delimiters; remove empty strings with a filter and control splits with maxsplit.

Learn to substitute with regular expressions in Python by creating a pattern to find numbers, replacing matches with a dash, and using sub and subn to count substitutions.

Explore Python regex compilation flags to modify pattern behavior—ignore case, multiline, dotall, unicode and ascii—plus verbose and debug options, and learn to combine flags with bitwise or.

Explore grouping in Python regex, capture subpatterns, apply quantifiers to groups, use alternation within groups, and extract matched subtexts via the match object's groups.

Explore back referencing in Python regex by using capture groups to match and recall earlier content. Learn to find duplicates and reformat dates with substitution, handling escaping and group indexing.

Learn to use named groups in Python regex to extract first and last names, substitute by group names, and backreferences. Explore non-capturing groups and access matches with group and groupdict.

Explore lookaround in regular expressions, including lookahead and lookbehind, and learn how positive lookahead and negative lookahead match patterns before or after without consuming characters, via zero-width assertions.

Explore the iris dataset, featuring sepal and petal measurements across three species from 150 observations with four features, and learn how supervised learning classifies species using features and targets.

Explore the scikit-learn training workflow using the iris dataset for a k-nearest neighbors classifier, including data loading, instantiation, fitting, predicting, and model evaluation.

Compare models using train/test split to estimate out-of-sample accuracy, highlighting logistic regression and k-nearest neighbors, overfitting risk, and tuning model complexity with cross-validation concepts.

Learn to evaluate supervised learning models with train-test split and cross validation, compare logistic regression and K nearest neighbors using accuracy, and guard against overfitting.

Master k-nearest neighbors classification on the iris data set, including choosing K, computing distances, and predicting species. Leverage scikit-learn for training and evaluating models.

Compare train tests split and cross-validation for classifier evaluation, and apply metrics such as accuracy, confusion matrix, sensitivity, specificity, and roc auc to diabetes data.

Learn to prepare data with pandas, visualize relationships with seaborn, and implement linear regression in scikit-learn, including interpreting coefficients, splitting data, and evaluating with MAE and RMSE.

Demonstrate predicting Titanic survival with scikit-learn using a pipeline that applies one-hot encoding to sex and embarked via a column transformer, then logistic regression with cross-validated accuracy.

Learn how to install Seaborn and its mandatory and recommended dependencies, import necessary libraries, and set up for data visualization in Python notebooks using Seaborn and Matplotlib.

Explore seaborn bar plots with the Tepes dataset, selecting x and y, hue, order, and estimator. Learn to adjust confidence interval values, capsize, and colors for horizontal and vertical bars.

Learn to create and customize seaborn box plots from datasets like tips and iris, with horizontal or vertical orient, order control, and swarm plot overlays.

Explore seaborn strip plots from the tips dataset, including horizontal and vertical plots, with alpha, size, outlines, guitar attribute, and combining with box plots.

Explore how to build a Seaborn pair grid with the Iris dataset, mapping a grid of scatter plots with diagonal histograms, hue by species, and customizable colors, legends, and markers.

Explore seaborn violin plots, including single and split violins, hue, order, and horizontal orientation, using the tips dataset to compare total bill, day, smoker status, and gender.

Explore seaborn cluster maps to reveal patterns in flight data by clustering rows and columns, adjusting color maps, and standardizing or normalizing the data.

Learn Seaborn heatmaps by creating a 12 by 15 normal data array, applying annotations, and tuning vmin, vmax, and color maps; visualize flight data with centered scales and optional lines.

Explore seaborn facet grids to visualize data across a two-by-two panel grid. Create histograms, scatter plots, regression lines, and box plots with color and legend.

Explore kernel density estimation plots with seaborn, including univariate and bivariate kde plots, customizing bandwidth, levels, and color maps, and comparing iris species.

Learn how to create seaborn joint plots to explore relationships between variables using tips and iris datasets, adding regression lines, KDE, and marginal distributions.

Learn how to create seaborn regression plots to reveal relationships between variables, customize scatter and regression lines with color, markers, and line properties, and incorporate confidence intervals using example datasets.

Explore seaborn pair plots to visualize relationships among variables using iris and tips datasets, customize with hue, markers, size, and diagonal options, and add regression lines.

Learn how to initialize tensors in pytorch, set dtype and device, convert to and from numpy, and perform core operations - arithmetic, matmul, broadcasting, indexing, and reshaping.

Explore the free interactive playgrounds included with this course, watch a demo of how they work, and learn to run scientific codes directly in your browser, no registration required.

Master interactive shell workflows for scientific Python to streamline data science visualization, enabling rapid exploration, analysis, and visualization of datasets.