DATATWEETS
Courses
  • Python for Data Analytics
    • Python Basics
      • Lesson 1 - Programming in Python
      • Lesson 2 - Variables and Data Types
      • Lesson 3 - For Loops and Iteration
      • Lesson 4 - Working with Lists
      • Lesson 5 - Conditional Statements
      • Lesson 6 - Python Dictionaries
      • Lesson 7 - Advanced Dictionaries and Frequency Tables
      • Lesson 8 - Python Functions
      • Lesson 9 - Python Functions: Arguments, Parameters, and Debugging
      • Lesson 10 - Working with Files
      • Lesson 11 - Exception Handling
      • Lesson 12 - List Comprehension
      • Lesson 13 - Lambda Functions
      • Lesson 14 - Filter and Map Functions
    • Python Advanced
      • Lesson 1 - Introduction to Object-Oriented Programming
      • Lesson 2 - Class Methods, Properties, and Encapsulation
      • Lesson 3 - Inheritance and Polymorphism
      • Lesson 4 - Special Methods and Python's Data Model
      • Lesson 5 - Advanced Function Concepts
      • Lesson 6 - Decorators and Metaprogramming
      • Lesson 7 - Iterators and the Iterator Protocol
      • Lesson 8 - Generators and Memory-Efficient Processing
      • Lesson 9 - Context Managers and Resource Management
      • Lesson 10 - Regular Expressions for Text Processing
      • Lesson 11 - Advanced Collections and Data Structures
      • Lesson 12 - Working with Dates, Times, and Timezones
      • Lesson 13 - Modules and Packages
      • Lesson 14 - Virtual Environments and Dependency Management
    • NumPy Fundamentals
      • Lesson 1 - NumPy Essentials and 1D Arrays
      • Lesson 2 - 2D Arrays and Working with CSV Data
      • Lesson 3 - Selecting and Slicing Data
      • Lesson 4 - Vector Operations and Calculations
      • Lesson 5 - Boolean Indexing and Data Filtering
      • Lesson 6 - Modifying Data and Assignment
    • Pandas Data Analysis
      • Lesson 1 - Introduction to Pandas and Series
      • Lesson 2 - DataFrames and Reading Data
      • Lesson 3 - Selecting Data with .loc[]
      • Lesson 4 - Selecting Data with .iloc[]
      • Lesson 5 - Series Operations and Value Counts
      • Lesson 6 - DateTime Fundamentals
      • Lesson 7 - Boolean Filtering in Pandas
      • Lesson 8 - Sorting and Ranking
      • Lesson 9 - Adding and Modifying Columns
      • Lesson 10 - Apply, Map, and Transform Functions
      • Lesson 11 - Handling Missing Data
      • Lesson 12 - Data Type Conversion and Cleaning
      • Lesson 13 - Removing Duplicates and Handling Outliers
      • Lesson 14 - Working with String Data
      • Lesson 15 - GroupBy and Aggregation
      • Lesson 16 - Pivot Tables
      • Lesson 17 - Concatenating DataFrames
      • Lesson 18 - Merging and Joining DataFrames
      • Lesson 19 - MultiIndex and Hierarchical Data
      • Lesson 20 - Window Functions and Rolling Operations
      • Lesson 21 - Final Project: Real-World Data Analysis
    • Data Visualization with Python
      • Lesson 1 - Understanding Graphs and Coordinates
      • Lesson 2 - Introduction to Matplotlib
      • Lesson 3 - Customizing Plots
      • Lesson 4 - Multiple Lines and Series
      • Lesson 5 - Scatter Plots Basics
      • Lesson 6 - Creating Scatter Plots
      • Lesson 7 - Understanding Correlation
      • Lesson 8 - Comparing Correlations
      • Lesson 9 - Understanding Distributions
      • Lesson 10 - Creating Bar Plots
      • Lesson 11 - Creating Histograms
      • Lesson 12 - Comparing Distributions
      • Lesson 13 - Pandas Plot Method
      • Lesson 14 - Creating Subplots
      • Lesson 15 - Grid Charts
      • Lesson 16 - Final Project - Traffic Analysis
  • SQL & Databases
    • Getting Started with SQL
      • Lesson 1 - Exploring Databases and Schemas
      • Lesson 2 - Columns, Data Types, and Functions
      • Lesson 3 - Filtering Rows with Numbers
      • Lesson 4 - Filtering with Text and Categories
      • Lesson 5 - Sorting and Limiting Results
      • Lesson 6 - Conditional Logic with CASE and Clean Style
      • Lesson 7 - Guided Project: Analyzing Kickstarter Campaigns
    • Summarizing Data with SQL
      • Lesson 1 - Aggregate Functions: COUNT, SUM, AVG, and More
      • Lesson 2 - Building Summary Statistics
      • Lesson 3 - Grouping Data with GROUP BY
      • Lesson 4 - Grouping by Multiple Columns and Filtering Groups
    • Combining Tables with Joins
      • Lesson 1 - Introduction to Joins
      • Lesson 2 - Joins with Filtering, Grouping, and Sorting
      • Lesson 3 - Self Joins, Outer Joins, and Cross Joins
      • Lesson 4 - Combining Results with Set Operators
    • Subqueries, CTEs, and Views
      • Lesson 1 - Scalar Subqueries
      • Lesson 2 - Multi-Row and Multi-Column Subqueries
      • Lesson 3 - Nested and Correlated Subqueries
      • Lesson 4 - Common Table Expressions (CTEs)
      • Lesson 5 - Creating and Using Views
      • Lesson 6 - Guided Project: Customer and Product Analysis
    • Window Functions
      • Lesson 1 - Introduction to Window Functions
      • Lesson 2 - Window Frames: ROWS and RANGE
      • Lesson 3 - Window Aggregate Functions
      • Lesson 4 - Ranking Functions: ROW_NUMBER, RANK, and NTILE
      • Lesson 5 - Offset Functions: LAG, LEAD, and FIRST_VALUE
      • Lesson 6 - Distribution and Percentile Functions
      • Lesson 7 - Guided Project: Sales Analytics for Northwind Traders
    • Querying Databases from Python
      • Lesson 1 - Running SQL Queries with sqlite3
      • Lesson 2 - From SQL Results to pandas DataFrames
    • PostgreSQL for Data Engineers
      • Lesson 1 - Introduction to PostgreSQL
      • Lesson 2 - Designing Tables and Choosing Data Types
      • Lesson 3 - Prepared Statements and Preventing SQL Injection
      • Lesson 4 - Loading and Copying Data
      • Lesson 5 - Managing Users and Databases
      • Lesson 6 - Project: Installing PostgreSQL Locally
      • Lesson 7 - Guided Project: Building a Crime Reports Database
    • Optimizing PostgreSQL
      • Lesson 1 - Exploring PostgreSQL Internals
      • Lesson 2 - Reading Query Plans with EXPLAIN
      • Lesson 3 - Speeding Up Queries with Indexes
      • Lesson 4 - Advanced Indexing Strategies
      • Lesson 5 - Vacuuming, Transactions, and ACID
    • Production Database Tools
      • Lesson 1 - Cloud Data Warehousing with Snowflake
      • Lesson 2 - Introduction to NoSQL Databases
      • Lesson 3 - Hands-On with MongoDB
  • Statistics & Probability
    • Statistics Fundamentals
      • Lesson 1 - Populations and Samples
      • Lesson 2 - Variables and Measurement Scales
      • Lesson 3 - Frequency Distributions
      • Lesson 4 - Visualizing Distributions
      • Lesson 5 - Comparing Distributions
      • Lesson 6 - Guided Project: Telling the Species Apart
    • Measures of Center & Variability
      • Lesson 1 - The Mean
      • Lesson 2 - The Weighted Mean and the Median
      • Lesson 3 - The Mode
      • Lesson 4 - Measures of Variability
      • Lesson 5 - Z-scores
      • Lesson 6 - Guided Project: Profiling Fuel Economy
    • Probability Fundamentals
      • Lesson 1 - Estimating Probabilities
      • Lesson 2 - Probability Rules
      • Lesson 3 - Solving Complex Probability Problems
      • Lesson 4 - Permutations and Combinations
      • Lesson 5 - Guided Project: The Odds of the Lottery
    • Conditional Probability & Bayes
      • Lesson 1 - Conditional Probability Fundamentals
      • Lesson 2 - Conditional Probability: Intermediate
      • Lesson 3 - Bayes' Theorem
      • Lesson 4 - The Naive Bayes Algorithm
      • Lesson 5 - Guided Project: Building a Clickbait Detector
    • Statistical Inference
      • Lesson 1 - Sampling Distributions and Confidence Intervals
      • Lesson 2 - Hypothesis Testing
      • Lesson 3 - Chi-Squared Goodness of Fit
      • Lesson 4 - Chi-Squared Test of Independence
      • Lesson 5 - Guided Project: Do Japanese and American Cars Really Differ?
  • Generative AI & LLM Engineering
    • Working with LLMs in Python
      • Lesson 1 - How Large Language Models Work
      • Lesson 2 - Your First Claude Call
      • Lesson 3 - System Prompts and Roles
      • Lesson 4 - Multi-Turn Conversations
      • Lesson 5 - Tokens, Cost, and Streaming
      • Lesson 6 - Controlling the Output
      • Lesson 7 - Guided Project: A Command-Line Assistant
    • Prompt Engineering
      • Lesson 1 - The Anatomy of a Strong Prompt
      • Lesson 2 - Sharpening Techniques
      • Lesson 3 - Few-Shot Prompting and Roles
      • Lesson 4 - Structured Outputs You Can Trust
      • Lesson 5 - Prompting for Data Tasks
      • Lesson 6 - Evaluating and Improving Prompts
      • Lesson 7 - Reducing Hallucinations and Unsafe Output
      • Lesson 8 - Guided Project: A Reusable Prompt Toolkit
    • Tool Use & Function Calling
      • Lesson 1 - Why LLMs Need Tools
      • Lesson 2 - Defining Tools and Schemas
      • Lesson 3 - The Tool-Use Loop
      • Lesson 4 - Parallel Tools, Errors, and Strict Schemas
      • Lesson 5 - Guided Project: A Multi-Tool Workflow Agent
    • Model Context Protocol (MCP)
      • Lesson 1 - What MCP Is and Why It Matters
      • Lesson 2 - Connecting to MCP Servers
      • Lesson 3 - Building Tools Over MCP
      • Lesson 4 - Guided Project: Connect Claude to an MCP Service
    • Embeddings & Semantic Search
      • Lesson 1 - What Embeddings Are
      • Lesson 2 - Generating Embeddings
      • Lesson 3 - Measuring Similarity and Distance
      • Lesson 4 - Guided Project: Semantic Search
    • Vector Databases
      • Lesson 1 - Why Vector Databases
      • Lesson 2 - Getting Started with Chroma
      • Lesson 3 - Metadata and Filtering
      • Lesson 4 - Guided Project: Searchable Knowledge Base
    • Retrieval-Augmented Generation
      • Lesson 1 - What RAG Is
      • Lesson 2 - Building a RAG Pipeline
      • Lesson 3 - Chunking Documents
      • Lesson 4 - Grounding and Citations
      • Lesson 5 - Guided Project: Documentation Q&A Bot
    • Building AI Agents
      • Lesson 1 - What Makes an Agent
      • Lesson 2 - The Agent Loop
      • Lesson 3 - Giving Agents Memory and Tools
      • Lesson 4 - Planning and Multi-Step Tasks
      • Lesson 5 - Guided Project: Research Assistant
    • LangChain & LangGraph
      • Lesson 1 - Why Frameworks
      • Lesson 2 - LangChain Basics
      • Lesson 3 - RAG with LangChain
      • Lesson 4 - Agents with LangGraph
      • Lesson 5 - Guided Project: LangGraph Agent
    • Shipping AI Applications
      • Lesson 1 - From Prototype to Production
      • Lesson 2 - Streaming and Error Handling
      • Lesson 3 - Managing Cost and Tokens
      • Lesson 4 - Securing and Serving
      • Lesson 5 - Guided Project: Deploying an AI App
  • FastAPI: Build Production APIs in Python
    • Getting Started with FastAPI
      • Lesson 1 - What FastAPI Is and Why It's Fast
      • Lesson 2 - Your First API
      • Lesson 3 - Path Parameters
      • Lesson 4 - Query Parameters
      • Lesson 5 - Guided Project: Read-Only Task Manager API
    • Request Bodies and Pydantic
      • Lesson 1 - Request Bodies with Pydantic
      • Lesson 2 - Field Validation and Constraints
      • Lesson 3 - Nested and Complex Models
      • Lesson 4 - Response Models
      • Lesson 5 - Guided Project: Validated Tasks API
    • HTTP Done Right: Status, Errors, Forms & Files
      • Lesson 1 - Status Codes and Response Types
      • Lesson 2 - Handling Errors
      • Lesson 3 - Headers, Cookies, and Forms
      • Lesson 4 - Working with Files
      • Lesson 5 - Guided Project: Robust Task Endpoints
    • Structure, Dependencies, and Middleware
      • Lesson 1 - Dependency Injection
      • Lesson 2 - Reusable and Sub-Dependencies
      • Lesson 3 - Bigger Applications with Routers
      • Lesson 4 - Middleware and CORS
      • Lesson 5 - Guided Project: Modular App
    • Databases with SQLModel
      • Lesson 1 - Connecting a Database
      • Lesson 2 - Models and Tables
      • Lesson 3 - CRUD: Create and Read
      • Lesson 4 - CRUD: Update and Delete
      • Lesson 5 - Guided Project: Persistent Tasks API
    • Authentication and Security
      • Lesson 1 - Security First Steps
      • Lesson 2 - Password Hashing and the Current User
      • Lesson 3 - JWT Access Tokens
      • Lesson 4 - Protecting Routes and Scopes
      • Lesson 5 - Guided Project: Auth-Protected API
    • Async, Background Work, and Streaming
      • Lesson 1 - async and Concurrency
      • Lesson 2 - Background Tasks
      • Lesson 3 - Streaming and Server-Sent Events
      • Lesson 4 - WebSockets
      • Lesson 5 - Guided Project: Streaming Endpoint
    • Testing, Settings, Deployment, and Capstone
      • Lesson 1 - Testing with pytest
      • Lesson 2 - Configuration and Secrets
      • Lesson 3 - Docs, Metadata, and Deployment
      • Lesson 4 - Capstone: Build the Complete API
      • Lesson 5 - Capstone: Test and Deploy It
  • Version Control with Git & GitHub
    • Version Control Foundations
      • Lesson 1 - What Version Control Is
      • Lesson 2 - Installing and Configuring Git
      • Lesson 3 - Your First Repository
      • Lesson 4 - Tracking Changes
      • Lesson 5 - Guided Project: Version-Control SkyLog
  • Software Engineering Fundamentals
    • Lesson 1: Introduction to Software Engineering
    • Lesson 2: Implementing SDLC in Real-World Projects
    • Lesson 3: Software Development Methodologies
    • Lesson 4: Software Design and Architecture
    • Lesson 5: Design Patterns
    • Lesson 6: Object-Oriented Programming
    • Lesson 7: Clean Code and Best Practices
    • Lesson 8: Version Control with Git
    • Lesson 9: Software Testing
    • Lesson 10: Behaviour-Driven Development
    • Lesson 11: Gherkin Language
    • Lesson 12: CI/CD and DevOps
    • Lesson 13: Security Best Practices
  • Machine Learning
    • Math Foundations for ML
      • Lesson 1 - Understanding Linear and Nonlinear Functions
      • Lesson 2 - Understanding Limits
      • Lesson 3 - Derivatives and Finding Extreme Points
      • Lesson 4 - Linear Systems
      • Lesson 5 - Vectors
      • Lesson 6 - Matrix Algebra
      • Lesson 7 - Solution Sets and Linear Independence
    • Machine Learning Foundations
      • Lesson 1 - The Machine Learning Workflow
      • Lesson 2 - Introduction to K-Nearest Neighbors
      • Lesson 3 - Evaluating Model Performance
      • Lesson 4 - Hyperparameter Optimization
      • Lesson 5 - Guided Project: Predicting Breast Cancer Diagnosis
    • Regression
      • Lesson 1 - Introduction to Linear Regression
      • Lesson 2 - Interpreting Regression Parameters
      • Lesson 3 - Checking Linear Regression Fit
      • Lesson 4 - Applying Linear Regression Models
      • Lesson 5 - Understanding Gradient Descent
      • Lesson 6 - Implementing Gradient Descent in Python
      • Lesson 7 - Gradient Descent with Scikit-Learn
      • Lesson 8 - Guided Project: Predicting Insurance Costs
    • Classification
      • Lesson 1 - Introduction to Logistic Regression
      • Lesson 2 - Interpreting the Regression Parameters
      • Lesson 3 - Evaluating Logistic Regression Models
      • Lesson 4 - Applying Logistic Regression Models
      • Lesson 5 - Guided Project: Classifying Heart Disease
    • Trees & Ensembles
      • Lesson 1 - Foundations of Decision Trees
      • Lesson 2 - Building Decision Trees with Scikit-Learn
      • Lesson 3 - Evaluating and Optimizing Decision Trees
      • Lesson 4 - Cross-Validation and Ensemble Methods
      • Lesson 5 - Guided Project: Predicting Employee Productivity
    • Model Optimization & Feature Engineering
      • Lesson 1 - Feature Engineering
      • Lesson 2 - Model Selection
      • Lesson 3 - Cross-Validation
      • Lesson 4 - Regularization
      • Lesson 5 - Going Beyond Linear Models
      • Lesson 6 - Guided Project: Optimizing Model Prediction
    • Unsupervised Learning
      • Lesson 1 - Introduction to Unsupervised Learning
      • Lesson 2 - The Iterative K-Means Algorithm
      • Lesson 3 - Choosing the Number of Clusters with the Elbow Method
      • Lesson 4 - K-Means with Scikit-Learn and Interpreting Results
      • Lesson 5 - Guided Project: Wholesale Customer Segmentation
    • Deep Learning Foundations
      • Lesson 1 - Introduction to Neural Networks
      • Lesson 2 - Math and NumPy Foundations for Neural Networks
      • Lesson 3 - Gradient Descent for Neural Networks
      • Lesson 4 - Backpropagation
      • Lesson 5 - Optimizers: SGD, RMSprop, and Adam
      • Lesson 6 - Regularizing Neural Networks
    • Deep Learning with PyTorch
      • Lesson 1 - Deep Learning Fundamentals
      • Lesson 2 - Tensors and Autograd in PyTorch
      • Lesson 3 - Building Neural Networks with nn.Sequential
      • Lesson 4 - Training Neural Networks
      • Lesson 5 - Deep Networks and Regularization
      • Lesson 6 - Guided Project: Predicting IPO Listing Gains with PyTorch
    • Deep Learning with TensorFlow
      • Lesson 1 - Deep Learning Fundamentals
      • Lesson 2 - Introduction to TensorFlow Operations
      • Lesson 3 - Building a Shallow Neural Network with the Sequential API
      • Lesson 4 - Multi-Layer Deep Learning Models
      • Lesson 5 - Deep Learning with the Keras Functional API
      • Lesson 6 - Guided Project: Predicting IPO Listing Gains with TensorFlow
    • Computer Vision with CNNs
      • Lesson 1 - Introduction to Convolutional Neural Networks
      • Lesson 2 - CNN Architecture
      • Lesson 3 - Regularization in Deep Learning
      • Lesson 4 - Advanced CNN Architectures
      • Lesson 5 - Transfer Learning
      • Lesson 6 - Guided Project: Detecting Pneumonia from X-Ray Images
    • Sequence Models (RNNs & LSTMs)
      • Lesson 1 - Introduction to Recurrent Neural Networks
      • Lesson 2 - Basic RNN Architecture
      • Lesson 3 - Advanced RNN Architecture: GRU and LSTM
      • Lesson 4 - Adding Convolutional Layers to RNNs
      • Lesson 5 - Time-Series Forecasting with RNNs
      • Lesson 6 - Guided Project: Forecasting the S&P 500
    • NLP with Deep Learning
      • Lesson 1 - Introduction to Natural Language Processing
      • Lesson 2 - Text Vectorization and Word Embeddings
      • Lesson 3 - Building Text Classification Models
      • Lesson 4 - Building Sequence Models for Text
      • Lesson 5 - Building Text Models with Transformers
      • Lesson 6 - Guided Project: Classifying Disaster Tweets
  • How-To Guides
    • Installing Python on macOS
    • Installing Python on Windows
    • How to Set Up Visual Studio Code
    • Install PostgreSQL on macOS
    • Install PostgreSQL on Windows
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How-To

How-To Guides

Step-by-step how-to guides for your data and development environment — install Python, PostgreSQL, and configure VS Code.

Short, practical how-to guides to get your environment ready for data work — install Python, PostgreSQL, and Visual Studio Code, step by step, on Mac and Windows.

Python Installing Python on macOS Learn how to install Python 3 on macOS using Command Line Developer Tools or the official installer, with step-by-step instructions 5 min read Read guide Python Installing Python on Windows Learn how to install Python 3 on Windows with step-by-step instructions, including environment setup and verification 3 min read Read guide VS Code How to Set Up Visual Studio Code Learn how to install and configure VS Code with essential extensions for Python, including linting, debugging, and formatting 7 min read Read guide PostgreSQL Install PostgreSQL on macOS Learn how to install PostgreSQL 14 on macOS with Homebrew or the official installer, including configuration and verification 6 min read Read guide PostgreSQL Install PostgreSQL on Windows Learn how to install PostgreSQL 14 on Windows with the official installer, including configuration, pgAdmin setup, and verification 11 min read Read guide
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