Preparing your learning workspace

Module 19: Introduction to R | STATLAB Academy

A First Analysis in R

Move from a business question to an auditable result with objects, functions, inspection, and clear interpretation.

45 min4 outcomes

Open

Vectors, Types, and Subsetting

Understand atomic vectors, coercion, vectorized operations, missing values, and safe subsetting.

45 min4 outcomes

Open

Data Frames, Factors, and Dates

Build rectangular data, encode categories, parse dates, and validate keys before joins.

45 min4 outcomes

Open

Functions, Pipes, and Project Workflow

Turn repeated calculations into validated functions and portable, reproducible workflows.

45 min4 outcomes

Open

Importing, Cleaning, and Validating Data

Treat cleaning as an auditable chain of schema, type, missingness, range, and key checks.

45 min4 outcomes

Open

Transforming Data with dplyr

Use a small grammar of verbs for readable row, column, grouping, and join operations.

45 min4 outcomes

Open

Exploratory Data Analysis

Describe centre, spread, shape, relationships, and unusual observations without turning discovery into confirmation.

45 min4 outcomes

Open

Data Visualization with ggplot2

Use layers, scales, facets, labels, and accessible encodings to make defensible statistical graphics.

45 min4 outcomes

Open

Statistical Foundations and Linear Regression

Connect estimands, uncertainty, intervals, tests, regression, diagnostics, and causal restraint.

45 min4 outcomes

Open

Time-Series Objects, Trend, and Decomposition

Represent ordered data correctly and separate levels, changes, trend, seasonality, and remainder.

55 min4 outcomes

Open

Stationarity, Autocorrelation, AR, and MA Models

Use stationarity, ACF, PACF, and simple AR/MA models to describe persistence.

55 min4 outcomes

Open

ARIMA Estimation, Diagnostics, and Forecasting

Follow an auditable specify–estimate–diagnose–forecast–evaluate loop.

55 min4 outcomes

Open

Seasonality and Dynamic Regression

Combine calendar structure and predictors with time-series errors while respecting future availability.

55 min4 outcomes

Open

Financial Data and Return Series

Document provenance, align calendars, define returns, and interpret quote direction before modelling.

65 min4 outcomes

Open

ARCH and GARCH Volatility Models

Advanced Topic – Optional. Model volatility clustering with conditional variance recursions and distribution-aware diagnostics.

65 min4 outcomes

Open

Asymmetric and Extended Volatility Models

Advanced Topic – Optional. Compare sign-sensitive GJR, EGARCH, APARCH, GARCH-M, and stochastic-volatility ideas.

65 min4 outcomes

Open

Measuring Volatility and Realized Variation

Advanced Topic – Optional. Compare rolling, annualized, realized, and range-based measures with explicit units and sampling choices.

65 min4 outcomes

Open

Nonlinear Dynamics and Discrete Outcomes

Advanced Topic – Optional. Study threshold regimes, neural forecasts, event durations, and ordered category probabilities.

65 min4 outcomes

Open

Continuous-Time Models and Option Pricing

Advanced Topic – Optional. Simulate Brownian motion and GBM, then interpret Black–Scholes price and sensitivities under explicit assumptions.

65 min4 outcomes

Open

Market Risk: VaR, Expected Shortfall, and EVT

Advanced Topic – Optional. Define loss consistently, estimate tail risk, backtest prior-only forecasts, and qualify extreme-value extrapolation.

65 min4 outcomes

Open

Multivariate Time Series and Cointegration

Advanced Topic – Optional. Model joint dynamics while separating predictive content, shock identification, long-run relations, and causal claims.

65 min4 outcomes

Open

Case Study: Forecasting Canadian Inflation

Assemble provenance, validation, exploration, benchmarks, ARIMA diagnostics, holdout scoring, and responsible communication.

65 min4 outcomes

Open

Preparing your learning workspace

Introduction to R

From Data Foundations to Time Series and Financial Analytics

Prerequisites

Intended audience

Three-part course map

Lessons

A First Analysis in R

Vectors, Types, and Subsetting

Data Frames, Factors, and Dates

Functions, Pipes, and Project Workflow

Importing, Cleaning, and Validating Data

Transforming Data with dplyr

Exploratory Data Analysis

Data Visualization with ggplot2

Statistical Foundations and Linear Regression

Time-Series Objects, Trend, and Decomposition

Stationarity, Autocorrelation, AR, and MA Models

ARIMA Estimation, Diagnostics, and Forecasting

Seasonality and Dynamic Regression

Financial Data and Return Series

ARCH and GARCH Volatility Models

Asymmetric and Extended Volatility Models

Measuring Volatility and Realized Variation

Nonlinear Dynamics and Discrete Outcomes

Continuous-Time Models and Option Pricing

Market Risk: VaR, Expected Shortfall, and EVT

Multivariate Time Series and Cointegration

Case Study: Forecasting Canadian Inflation