Top 10 numpy course for data analytics Best top numpy course

Explains numpy array creation routines such as empty, zeros, and ones; uses shape, dtype, and order parameters; demonstrates 3x2 arrays, default float dtype, and structured dtype examples.

Learn to create numpy arrays from existing data by converting lists, tuples, buffers, and Python objects using dtype control, and one-dimensional array generation.

Learn to generate arrays from numerical ranges in numpy using range, linspace, and logspace, with start, stop, step, endpoint behavior, and data type considerations for data analytics.

Learn to apply advanced indexing in NumPy, using integer indexing to select random elements, boolean array indexing to filter values, and function-based indexing to derive indices from expressions.

Explore numpy broadcasting, which stretches a smaller array to match a larger array during arithmetic operations, with rules for matching shapes and practical 1D and 2D examples.

Explore how to iterate over NumPy arrays with for loops, reshape and transpose arrays, and control memory order (C-order, F-order) to influence element access and broadcasting.

Learn binary and bitwise operations in numpy using numbers like 13 and 17, their binary representations, and results such as 29, 42, and 510 on unsigned integers.

Apply sine, cosine, and tangent to an array of angles in NumPy. Learn rounding with around, floor, and ceil to see how values transform.

Learn NumPy arithmetic on arrays with broadcasting, including addition, subtraction, multiplication, and division. Explore reciprocal, power, remainder, and complex-number operations, including real and imaginary parts, conjugate, and angle in degrees.

Explore computing mean, max, min, percentile, median, range, and standard deviation on NumPy arrays, with axis specifications and weighted averages.

Learn to call NumPy's sort and search functions, control sorting with axis and kind parameters, and reconstruct sorted data using indices, including sorting records by name or age.

Explore endianness in numpy by swapping bytes between little and big endian representations, visualize memory layout, and observe hexadecimal values before and after 16-bit byte swapping.

Understand copies and views in numpy, assignment aliases memory without copying, slicing creates a view, and copy and deep copy yield independent data.

Explore matrix creation in numpy, using functions to generate empty, zeros, ones, identity, eye, and random matrices, and convert lists into matrices with tips on shape, order, and diagonal elements.

Explore NumPy's linear algebra capabilities, including dot product, matrix multiplication, determinant, inverse, and other essential linear algebra operations on small matrices, with emphasis on practical operations and outputs.

Learn to plot with Matplotlib in Python using pyplot to plot 2D data and simple equations, customize titles, labels, and colors, and explore subplots and bar charts.

Learn to plot a histogram with Matplotlib by feeding data and bins, compute frequencies per bin, and visualize the data distribution across defined value ranges.

Save and load NumPy arrays using binary and text file operations, preserving shape and data type to reconstruct the array from disk and share it across machines.