Update repo README.md

addition of new badges to repo README.

Author: rmcmillan34

README.md

@@ -6,6 +6,8 @@
 ![Last Commit](https://img.shields.io/github/last-commit/rmcmillan34/algorithmic-trading-learning-roadmap)
 ![Issues](https://img.shields.io/github/issues/rmcmillan34/algorithmic-trading-learning-roadmap)
 ![Stars](https://img.shields.io/github/stars/rmcmillan34/algorithmic-trading-learning-roadmap?style=social)
+![GitHub forks](https://img.shields.io/github/forks/rmcmillan34/algorithmic-trading-learning-roadmap)
+![GitHub contributors](https://img.shields.io/github/contributors/rmcmillan34/algorithmic-trading-learning-roadmap)
 
 Welcome to the Algorithmic Trading Learning Roadmap repository! This repository provides a structured, comprehensive roadmap for developing expertise in the core skills needed to become a proficient algorithmic trader. It includes resources, certifications, and project ideas across various fields that intersect in the world of algorithmic trading, such as AI, data science, finance, software engineering, cloud computing, and more.
 

roadmap/data-science/data-analytics/README.md

@@ -75,7 +75,7 @@ By completing this module, you will:
    A beginner-friendly introduction to data analysis.
 
 #### Intermediate
-2. [Google’s Advanced Data Analysis](resources/courses/google-advanced-data-analysis.md)
+2. [Google’s Advanced Data Analysis](https://www.coursera.org/professional-certificates/google-advanced-data-analytics)
    Covers advanced data manipulation and visualization techniques.
 
 #### Advanced

roadmap/data-science/r-language/README.md

@@ -14,49 +14,6 @@ By completing this module, you will:
 
 ---
 
-## Syllabus Overview
-
-This section is divided into the following topics, arranged in the recommended order of study:
-
-### 1. [Introduction to R Programming](r-introduction/README.md)
-- **Prerequisites**: Familiarity with basic programming concepts.
-- **Topics Covered**:
-  - R environment setup and IDEs (RStudio).
-  - Basic syntax, data types, and structures.
-  - Control flow, functions, and error handling.
-- **Applications**: Setting up your R environment for financial data analysis.
-
-### 2. [Data Manipulation in R](data-manipulation/README.md)
-- **Prerequisites**: Basic understanding of R syntax.
-- **Topics Covered**:
-  - Data frames, tibbles, and lists.
-  - Data wrangling with `dplyr` and `tidyr`.
-  - Reading and writing data in various formats.
-- **Applications**: Preparing financial datasets for analysis.
-
-### 3. [Visualization in R](visualization/README.md)
-- **Prerequisites**: Data Manipulation in R.
-- **Topics Covered**:
-  - Plotting with `ggplot2`.
-  - Creating interactive visualizations with `plotly` and `shiny`.
-  - Advanced visualizations: heatmaps, candlestick charts, and time series plots.
-- **Applications**: Visualizing financial data and trading performance.
-
-### 4. [Financial Analysis with R](financial-analysis/README.md)
-- **Prerequisites**: Data Manipulation in R.
-- **Topics Covered**:
-  - Time series analysis with `xts` and `zoo`.
-  - Risk metrics and portfolio optimization with `PerformanceAnalytics` and `PortfolioAnalytics`.
-  - Pricing derivatives with `quantmod` and `RQuantLib`.
-- **Applications**: Backtesting strategies and evaluating portfolio performance.
-
-### 5. [Machine Learning in R](machine-learning/README.md)
-- **Prerequisites**: Financial Analysis with R.
-- **Topics Covered**:
-  - Supervised learning with `caret` and `mlr`.
-  - Time series forecasting with `forecast` and `prophet`.
-  - Deep learning with `tensorflow` and `keras` in R.
-- **Applications**: Building predictive models for market forecasting.
 
 ---
 

roadmap/mathematics/README.md

@@ -57,7 +57,7 @@ This section is divided into the following topics, arranged in the recommended o
   - Bayesian games and mechanism design.
 - **Applications**: Modeling market interactions, auction-based pricing, and adversarial strategies in trading.
 
-### 6. [Optimization](optimization/README.md)
+### 6. [Optimization](optimisation/README.md)
 - **Prerequisites**: Calculus and Linear Algebra.
 - **Topics Covered**:
   - Linear and quadratic programming.