Julia Programming For Data Science & Machine Learning: Julia

Explore numbers and variables in Julia, including integers, floats, and basic arithmetic. Learn to write single-line and multi-line comments and to store and print values in a Jupyter notebook.

Explore strings in Julia: define with double and triple quotes, handle quotes, use string interpolation with the dollar sign, and perform indexing, slicing, concatenation, and repetition.

Discover arrays in Julia, the first Julia collection type that stores homogeneous or non homogeneous elements. Learn to define, index from 1, slice ranges, and perform in-place and copy operations.

Explore how tuples differ from arrays by using parentheses to define immutable collections, access elements by index, and note that modification is not allowed.

Explore dictionaries in Julia by creating a phonebook with Dict, using string keys like John and values, retrieving keys and values, and iterating entries.

Learn how Julia handles sets as unique collections by discarding duplicates, define a set with the set constructor, and perform union, intersection, and subset checks.

Explore decision making and control flow in Julia's high-performance environment through if, else, and else if statements, using examples that classify numbers as positive, negative, or between 0 and 25.

Explore looping in Julia, using for and while loops to print ranges and array elements, control flow with end, and combine looping with decisions for data science tasks.

Learn to define and call Julia functions, parameterized or not, using println and string interpolation, explore various definitions, implicit returns, and map over arrays.

Discover how to manage Julia packages with the built-in pkg system, install and remove packages, verify installs, and use them for tasks like calculating means.

Explore the random package in Julia to generate uniformly and normally distributed data, random strings, and reshape vectors into 2d matrices using rand, randn, randstring, and reshape.

Generate and manipulate multi-dimensional matrices in Julia, from ones and zeros to 2d and 3d arrays, and apply linear algebra operations such as transpose, inverse, determinant, trace, eigenvectors, and eigenvalues.

Learn to install and use Julia data frames for fast tabular data analysis, create and populate frames, and access columns like tb.x1.

Explore inspecting and manipulating a 10-by-3 data frame in Julia, including retrieving column names, size, and data types, and using describe; practice slicing rows and columns and adding new columns.

This video demonstrates reading and writing a data frame in Julia using CSV, serialization, and JSON tables, including csv.read, csv.write, df.bin, and df.json workflows.

Explore row and column operations on Julia data frames, including renaming with and without in-place updates, horizontal and vertical concatenation, inserting and deleting columns, filtering, sorting, and copying.

Explore how the plots package in Julia provides a unified interface to backends like GR, PyPlot, and Unicode plots, enabling quick switch between line, scatter, and labeled plots.

Learn to create multiple plots in a single Julia figure, arranging line, scatter, sine and cosine graphs in layouts like 2x2 or 4x1, and switch backends GR and Unicode.

Learn how linear regression relates promotion spend to sales in a simple supervised learning setup. Visualize data and estimate the slope and intercept of y = mx + c.

Students derive the simple linear regression model by calculating slope and intercept from data, implement it in Julia with statistics and the glenn library, and predict sales from promotion.

Learn to fit a multiple linear regression model with multiple predictors in Julia, interpreting the intercept and coefficients, handling categorical data, and predicting salary while exploring gender and education effects.