Data Science & ML Made Simple: Beginner to Job-Ready 2026

Learn to install Anaconda and Jupyter Notebook on Windows or Mac OS, download from Anaconda site, verify email, install, and launch Jupyter with Python 3 and numpy, pandas, and matplotlib.

Install or verify Python from the Python website, check the version in the command line, and run hello world in a Jupyter notebook to confirm Python is ready.

Explore how strings are defined in Python using single or double quotes, and learn to display string literals with the print function.

Discover how Python lists store multiple items in a single variable, create them with square brackets, and check length with len.

Explore Python for loops by iterating over lists, strings, and other sequences, printing elements, and using break to stop the loop when a condition is met.

Learn how to define and call Python functions, pass parameters, and return values, then print results using function calls.

Learn python classes and objects by building a person class with name and age properties, using init and self to create and print instances.

Explore NumPy, a Python library for arrays, linear algebra, and matrices. Learn why this open-source NumPy project, Numerical Python, created in 2005 by Travis Oliphant, offers 50x faster operations.

Learn how to install and import numpy with Python or pip. In a Jupyter notebook, import numpy as np and print the version with np.__version__.

Master numpy array indexing by accessing elements in 1d, 2d, and 3d arrays using zero-based indices and negative indexing.

Learn how NumPy handles data types, including strings, integers, floats, booleans, and complex numbers, and how to check, define, and convert dtypes in NumPy arrays using dtype and astype.

Explore numpy array reshaping, converting 1d arrays to 2d and 3d, understanding copy versus view, handling unknown dimensions with -1, and flattening arrays for analysis.

Learn how to join NumPy arrays using concatenate, stack, x stack, and vstack, including 1D and 2D arrays and axis handling for rows, columns, and depth.

Learn to split arrays with numpy using the array_split function to divide 1d and 2d arrays into multiple parts, then access each split by index.

Learn to search numpy arrays with the where method to locate indexes of specific values and even/odd patterns, and use searchsorted to determine insertion points from left or right.

Sort numpy arrays with the sort function to arrange numbers and strings in ascending or descending order, including 1D and 2D arrays, while preserving the original data.

Explore pandas, a Python library for analyzing and cleaning data. See how pandas lets you analyze big data, remove empty or null values, and reveal correlations, averages, maxima, and minima.

Understand installing pandas with Anaconda, importing pandas and the pd alias, creating a data frame from a dataset, printing it, and checking the pandas version.

Learn how to clean empty cells in pandas by dropping rows with dropna, replacing nulls with fillna options, and applying mean, median, or mode to a specified column.

Learn how to identify and remove duplicate rows in pandas using the duplicated method and drop_duplicates, including using inplace to preserve the original data and create a clean data frame.

Explore plotting in pandas with the plot method, including pie plots, scatter plots for duration versus calories, and histograms, highlighting correlation from data.csv read via read_csv.

Explore Matplotlib, a Python data visualization library, to create charts and plots that reveal trends and distributions for analysis and reporting; learn how to install Matplotlib.

Explore matplotlib plotting with numpy arrays, learning to plot x and y points and markers, draw lines with plt.plot, plot multiple points, and understand default x coordinates.

Explore how to customize Matplotlib markers, including format string fmt, marker size (ms), edge color (mec), and face color (mfc), with star, circle, and diamond examples.

Learn to add x and y axis labels, create a plot title, and customize font properties in matplotlib. Adjust font family, color, and size, and align title to the left.

learn to add grid lines to matplotlib plots with the grid function, choose which axis to display (x axis or y axis), and customize line style and width.

Master how to display multiple plots with matplotlib using subplot, which takes rows, columns, and index. Learn to stack or overlay plots by adjusting these arguments and plotting with plt.plot.

Learn to create bar graphs in matplotlib with the bar function using x and y data. Explore horizontal bars, color and width options, and a four-bar example with fruit prices.

Learn to create pie charts in Matplotlib using the pie function, and apply labels, shadows, colors, and legends to clearly present data.