Data Analysis, Simulation, Visualization and Exploration

Explore conditional statements using if and else, including nested if-else logic and the and/or operators. See how comparison and logical operators drive true or false branches in code.

Learn to index and slice NumPy arrays, from 1D vectors to 4x4 2D matrices, extracting elements, ranges, last items with negative indices, and submatrices.

Learn to deal with missing and null values in a pandas data frame, cleaning data and imputing with mean, median, or standard deviation, and applying replace and dropna techniques.

Learn to filter Pandas data frames using single- and multi-column conditions, including and, or, and isin, to extract relevant subsets such as age over 25 or Sundays.

Learn to combine data frames in Python using concatenation and merge. Explore concatenating along rows or columns and performing inner, left, right, and outer joins based on shared keys.

Learn how to use pandas pivot and pivot table to reorganize data by index and columns, compare with groupby, and apply aggregates.

Learn to adjust line thickness in Matplotlib by passing the line width parameter in plt.plot, with examples from 2 to 3, 5, and 15, noting the default width of two.

Plot y against x with a red line and label the curve as y is equals to x square. Use plt.legend() and plt.title() to add a legend and title.

Master matplotlib plotting by applying a dark background, customizing line style, marker size and colors, and adding markers, grid, and legends to create clearer visualizations.

Explore advanced matplotlib techniques: annotate text on plots, arrange diverse subplots with subplot to grid, and render 3d surface plots using meshgrid and plot_surface.

Utilize Seaborn lm plots to fit a linear model on scatter data. Explore relationship between salaries and expenditure, with hue by degree offered and optional confidence intervals.

Deliver the first part of the project solution by importing data, checking shape and nulls, cleaning the Titanic dataset, and beginning survival analysis by sex and embarkation.

Explore Monte Carlo simulations and visualization by implementing repeated sampling in Python to predict outcomes of uncertain events, such as coin flips and dice rolls, and visualize results with histograms.

Explores Monte Carlo simulations to compute the probability of two heads when flipping a pair of coins, showing 10,000 trials approximate 0.25 and reveal a distribution around that value.

Explore the sample space of a single die and a pair of dice, and use Monte Carlo simulations to estimate probabilities like 1/6 and 1/36.

Explore Monte Carlo simulations by rolling a six-sided die to estimate the probability of sixes, using numpy to generate 1–6 outcomes and observe convergence to 1/6.

Use a Monte Carlo simulation to count how many coin flips are needed to reach 10,000 heads, and plot the distribution with numpy, matplotlib, and seaborn.

Explore time series generation and visualization by creating impulse, square, triangular, sinusoidal, exponential, chirp, and synthetic time series. Learn how equally spaced data points and time order affect the meaning.

Explore impulse signals and impulse time series, from the unit impulse at t=0 to discrete and continuous forms, highlighting sharp transients and how multiple impulses form an impulse train.

Generate and visualize unit impulse signals in Python, using discrete stems and continuous plots to illustrate impulse series. Build impulse trains with SciPy's signal.unit_impulse at specified times.

Explore the unit exponential time series, a decaying, not periodic signal with magnitude one at t=0, governed by e^{-t}. Learn its features and how to implement the decay in Python.

Learn to generate and visualize a chirp time series, a non-stationary signal whose frequency increases over time, using Python with numpy and matplotlib to create and plot a sine-based chirp.

Learn to read and display images with numpy and matplotlib, explore color images (225x225x3) and grayscale images (gray cmap), and understand 8-bit unsigned integer and float32 formats.

Explore edge, line, and corner concepts in images: an edge is a two-sided intensity transition, a line joins two close edges, and a corner forms at their intersection.

Demonstrate image creation and visualization with OpenCV and numpy by drawing a red line on white, filling a green corner, black edge, and rendering a blue ring and pink circle.