Statistical analysis, time-series forecasting, and financial modelling using R.
📖 R Language Projects – Learning Statistics with R
As part of my Finance Data Scientist roadmap, R is where I’m building a deep statistical foundation. While Python powers my machine learning, SQL handles my databases, and Power BI delivers my dashboards, R fills the gap by sharpening my statistical thinking — ensuring I can interpret models, validate assumptions, and explain uncertainty with confidence.
💡 Why this matters?
Finance is full of risk, variance, and uncertainty. R is the best environment to explore probability, hypothesis testing, and time-series forecasting — tools that make financial analysis not just descriptive, but statistically robust and predictive.
My Learning Tracks (GitHub Repos)
- 📘 BooK_Learn_R_For_Applied_Statisitcs
- 📝 R_Note_R_Bootcamp
- 📚 R_for_Data_Science_Hadley
Applied stats in practice — hypothesis testing, ANOVA, regression, bootstrap resampling.
Bootcamp notes covering syntax, tidyverse basics, and statistical utilities for finance.
Structured learning of the tidyverse workflow (import → tidy → model → visualise).
How R Complements My Roadmap
- 🧮 R + Statistics → solid foundations in distributions, inference, and forecasting.
- 🐍 Python + ML → applied machine learning (classification, regression, deployment).
- 🗄️ SQL + Data Engineering → query optimisation, data pipelines, financial integration.
- 📊 Power BI + Reporting → dashboards, KPIs, and executive insights.
Together, these tools create a full-stack finance analytics pipeline:
- SQL brings the data → R tests hypotheses & statistical models → Python builds predictive ML → Power BIcommunicates insights.
Focus Areas in R Learning
- 📊 Statistical Modelling & Inference — regression, GLMs, ANOVA, significance testing.
- ⏱️ Time-Series Forecasting — ARIMA, ETS, seasonality, anomaly detection.
- 💼 Financial Applications — risk-return analysis, factor models, variance decomposition.
- 🎨 Visualisation — ggplot2 dashboards and clean, layered storytelling.
- 🔗 Hybrid Workflows — Excel/SQL integration, reticulate for Python bridges.
What I’m Building
- Statistical mindset — learning how assumptions, tests, and uncertainty guide real finance decisions.
- Reusable R templates for forecasting, variance analysis, and reporting.
- R Markdown/Quarto reports that explain code, results, and insight in one narrative.
- Case-driven projects — applying stats to financial data, not just textbook datasets.
🛠️ Tools & Packages
- 🧰 Core: tidyverse (dplyr, tidyr, purrr, readr, stringr)
- 🎨 Visualisation: ggplot2, plotly
- 📈 Time-series: forecast, fable, tsibble, prophet
- 📚 Stats: broom, infer, boot, effectsize, car
- ♻️ Workflow: targets, renv, quarto
Next Steps
- Apply R statistical methods to real finance projects (e.g., stock returns decomposition, liquidity stress tests, forecasting cash flows).
- Build an internal R package of helper functions for finance KPIs and plotting themes.
- Use R + Python together (reticulate) for hybrid statistical + ML projects.
- Share progress through GitHub updates, making my repos a learning-in-public portfolio.
📌 R is my “statistics lab” within the Finance Data Scientist roadmap. By combining R with Python, SQL, and Power BI, I’m building a complete skillset — from raw data to statistical validation, predictive modelling, and business storytelling.
