Learn Python for Data Science & Machine Learning from A-Z

Learn how Python serves data science and machine learning with a rich ecosystem of libraries like numpy, pandas, scipy, and matplotlib for data visualization and modeling.

Data science extracts key insights and trends from data to inform decisions and boost the bottom line, using a five-stage lifecycle and skills in data engineering, statistics, computing, and visualizations.

Learn how machine learning programs computers to learn from data, building models that perform tasks without explicit rules. Explore practical applications from web search to fraud detection and robotics.

Understand core machine learning concepts and algorithms, including supervised, unsupervised, and semi supervised methods. Explore how representation, evaluation, and optimization shape models for classification, regression, and clustering tasks.

Discover deep learning as a subset of AI that learns from data through artificial neural networks with many hidden layers, enabling speech recognition, image recognition, and natural language processing.

Compare machine learning and deep learning, showing how neural networks hierarchically learn features; machine learning relies on labeled data, while deep learning works with less structured data.

Explore what programming means, how computers execute instructions from high level languages to binary code, and how problem solving guides writing simple Python code.

Explore why Python is the popular open-source, easy-to-learn, high-level, interpreted language for data science and machine learning, enabling rapid prototyping with libraries for data manipulation, data visualization, and deep learning.

Explore how notebooks let you write and share code, document explanations, and visualize results in a web browser, using multiple languages and text-and-code cells for storytelling in data science.

Explore Google Colab, a cloud-based Jupyter notebook that runs in your browser with no setup or installation, offering GPU access for data science and machine learning.

Understand how Python variables store numbers, characters, or text, and how assignment overwrites values. Explore case-sensitive naming with underscores and efficient printing of multiple variables in Python 3.

Learn Google Colab basics, create and rename notebooks, run code, write text in code and text cells, use keyboard shortcuts, and save notebooks to Google Drive with organized folders.

Explore Python printing of text, numbers, and variables with quotes and newline characters; master mathematical, comparison, and boolean operators, and understand the execution order of operators.

Explore Python numbers, including integers and floats, and learn how Python uses automatic typing, the type() function, and typecasting with int() and float() to convert between types and handle booleans.

Explore Python strings, including printing with apostrophes and escaping, and master string operations like concatenation, newline, and indexing. Learn slicing, length, and dynamic code practices to manipulate and access characters.

Explore how Python conditional statements control program flow, using if, else, and elif, with indentation signaling blocks, and learn how comparisons, modulus, and sequential execution shape outcomes.

Explore how for loops and while loops control program flow, using conditions and if statements to repeat actions and iterate over sequences in Python.

Explore Python lists as a flexible, multi-type data container, then learn to loop with for, break, and continue, and use enumerate to access index and value within lists.

Explore python lists as mutable, ordered collections that can hold diverse objects, learn zero-based indexing and slicing (end index excluded) and contrast with immutable strings.

Explore how Python tuples are immutable, support indexing and slicing like lists, and learn to create empty or single-element tuples with a trailing comma.

Explore Python dictionaries, a key-value data structure with unique keys. Create, access by keys, and check existence with get or in; values may duplicate; use clear and keys.

Learn Python sets, an unordered, unique collection that cannot be indexed. Create sets with curly braces and modify them with add and pop, noting duplicates are removed.

Discover how to combine Python compound data types: lists, dictionaries, tuples, and sets, navigate nested structures, and choose the right type for each data relationship.

Define and use Python functions to encapsulate logic and outputs. Explore built-in and user-defined functions, leverage def, return, optional inputs, and docstrings for clear documentation.

Learn object oriented programming in Python, contrasting it with functional programming, and master class, object, self, init, attributes, methods, the dot operator, and class vs instance attributes.

Explore why statistics underpins data science, distinguishing intuitive and statistical decisions, and show how descriptive and inferential statistics guide data-driven insights in the data science process.

Describe how descriptive statistics use data types to guide operations, distinguishing numerical (continuous and discrete) from categorical (nominal and ordinal) with interval and ratio as subtypes.

Explore how variability describes data dispersion using mean, median, and mode, identify outliers with box plots, and compare measures of central tendency for robust data description.

Explore variance, covariance, and standard deviation, and learn why we divide by n−1, use samples to infer population parameters, and apply the coefficient of variation for relative dispersion.

Explore how covariance reveals the direction of a variable relationship and how correlation measures its strength, using the correlation matrix and standard deviations.

Master inferential statistics by using random samples to infer population insights, avoid bias, and study discrete and continuous distributions, including normal, uniform, binomial, and z-scores.

Explore skewness as a measure of asymmetry in distributions, identify right and left skew, relate tails and outliers to mean and median, and interpret how skewness reflects data shape.

Explore sampling distribution and the central limit theorem, showing how sample means from random samples form a normal distribution with n ≥ 30, enabling inference and confidence intervals.

Explore probability, the likelihood of events, and quantify it with the formula—preferred outcomes over the sample space—using coin flips and dice rolls (1/2 and 1/6) as examples.

Explore the expected value and experimental probability using coin-toss examples, definitions of trials and experiments, and how average outcomes guide predictions from data experiments.

Explore relative frequency as the ratio of observed outcomes to total trials, compare experimental and theoretical probability, and apply the concepts through coin flips and candy draws.

Explore the basics of hypothesis testing, including null and alternate hypotheses, the seven-step process, critical region concepts, and one-tailed or two-tailed tests.

Learn how numpy provides an efficient array data type and alias np for storing and manipulating data in data science workflows, outperforming Python lists for homogeneous data.

Explore NumPy arrays with fixed data types, and compare them to lists. Initialize zeros, ones, and random arrays, and construct 2-D matrices with rows, columns, and an identity matrix.

Master the basics of NumPy arrays by exploring dimension, shape, and size, then learn indexing, slicing, and broadcasting with 2D and 3D arrays such as RGB images.

Learn numpy array indexing and slicing, including start-stop-step, negative indices, and reshaping arrays for different row-column orientations.

Explore numpy array computations with universal functions to perform vectorized operations, including addition, subtraction, multiplication, division, floor division, modulus, power, and aggregations like sum, min, max across axes.

Broadcast constants or smaller arrays to match a target array and perform elementwise operations, following the rules to pad with ones, stretch dimensions, and raise errors for incompatibilities.

Explore pandas for data science: use data frames and series to process tabular data, read csv, inspect with head and tail, and perform feature engineering through new and renamed columns.

Master pandas extraction and indexing to select columns and rows, create focused data frames, and perform feature engineering during exploratory data analysis, including describe, mean, median, value counts, and merges.

Explore data visualization techniques for univariate and multivariate data, including histograms, density plots, box plots, bar plots, scatterplots, and correlation matrices, and frequency tables, to present findings effectively.

Explore data visualization in Python using matplotlib, pandas, and seaborn to reveal patterns, distributions, and correlations through univariate and multivariate plots.

Learn Python data visualization with matplotlib, pandas, and seaborn to plot histograms and box plots from the wine dataset, identify data types, and extract insights.

Explore the basics of machine learning, including supervised and unsupervised learning. Learn how training, datasets, labels, and features drive regression and classification to make predictions.

Walk through the machine learning pipeline, starting with exploratory data analysis to understand data types and outliers, then prepare features, visualize correlations, select and train models, and tune for deployment.