Open Library
Free, bite-sized Data Science & Analytics lessons — clear explanations, worked examples, and interactive charts.
Python Basics & Syntax
This foundational topic covers the essential Python syntax, core data types, control flow mechanisms, and functions. Mastering these elements is crucial for professionals in data science and machine learning to write clear, efficient, and maintainable Python code.
Explore lessons →Control Flow
If-else statements, loops (for, while), break/continue
Explore lessons →Functions & Scope
Defining functions, parameters, return values, variable scope
Explore lessons →Data Structures: Lists & Tuples
Creating, indexing, slicing, list comprehensions
Explore lessons →Data Structures: Dictionaries & Sets
Key-value pairs, set operations, when to use each
Explore lessons →String Manipulation
String methods, formatting, regex basics
Explore lessons →File Handling
Reading/writing files, CSV, JSON, context managers
Explore lessons →Object-Oriented Programming
Classes, objects, inheritance, encapsulation
Explore lessons →Error Handling & Debugging
Try-except blocks, raising exceptions, debugging techniques
Explore lessons →Python Best Practices
PEP 8, code organization, virtual environments
Explore lessons →Linear Algebra: Vectors
Vector operations, dot product, magnitude, unit vectors
Explore lessons →Linear Algebra: Matrices
Matrix operations, multiplication, transpose, inverse
Explore lessons →Linear Algebra: Eigenvalues
Eigenvalues, eigenvectors, and their applications
Explore lessons →Calculus: Derivatives
Differentiation, partial derivatives, chain rule
Explore lessons →Descriptive Statistics
This micro-learning path introduces professionals to the core concepts of descriptive statistics, enabling them to summarize, organize, and present data effectively. It covers key measures of central tendency, variability, and distribution, alongside essential visualization techniques for data exploration and communication.
Explore lessons →Data Distributions
Understand the shape of your data - from PDFs and CDFs to the normal, binomial and Poisson distributions.
Explore lessons →Correlation & Covariance
Pearson, Spearman correlation, correlation vs causation
Explore lessons →Sampling Methods
Select representative subsets of data to draw reliable conclusions and build robust models
Explore lessons →Probability Basics
The foundations of probability for data science: defining events, the addition and multiplication rules, conditional probability, independence and Bayes.
Explore lessons →Central Limit Theorem
CLT concept, applications, sample size
Explore lessons →Determinant of a Matrix
What the determinant of a matrix actually measures
Explore lessons →Pandas Basics
DataFrames, Series, reading/writing data
Explore lessons →SQL Basics
SELECT, FROM, WHERE, ORDER BY, LIMIT
Explore lessons →Linear Regression
Simple linear regression, assumptions, interpretation
Explore lessons →Principal Component Analysis
Dimensionality reduction, variance explained
Explore lessons →Word Embeddings
Word2Vec, GloVe, embedding layers
Explore lessons →RAG (Retrieval Augmented Generation)
Vector databases, embeddings, retrieval
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