📍 Module 1: Introduction to Julia

• Why Julia for Data Science?

• Julia vs Python vs R

• Installing Julia & Jupyter (IJulia)

• Julia REPL, IDEs (VS Code, Pluto.jl)

• Julia Packages & Package Manager

📍 Module 2: Julia Language Fundamentals

• Data Types, Variables, and Operators

• Control Structures: if-else, loops, try-catch

• Functions & Scoping Rules

• Multiple Dispatch & Performance Tips

• Working with Arrays, Tuples, Dicts, Sets

📍 Module 3: Data Handling with Julia

• Reading/Writing CSV, Excel, JSON

• DataFrames.jl: Manipulating Tabular Data

• Missing Data Handling

• Filtering, Grouping, Sorting, Aggregation

• Working with Dates & Times

📍 Module 4: Data Visualization

• Using Plots.jl, Gadfly.jl, and Makie.jl

• Line Charts, Bar Graphs, Histograms, Boxplots

• Scatter Plots and Heatmaps

• Customizing Charts (colors, labels, legends)

📍 Module 5: Statistics & Probability

• Descriptive Statistics (mean, median, std, var)

• Probability Distributions

• Hypothesis Testing

• Correlation & Covariance

• T-tests, ANOVA (HypothesisTests.jl)

📍 Module 6: Machine Learning with Julia

• Introduction to MLJ.jl Framework

• Supervised Learning: Linear/Logistic Regression, Decision Trees

• Unsupervised Learning: Clustering (KMeans, PCA)

• Feature Engineering and Scaling

• Model Evaluation: Accuracy, Precision, Recall, ROC

📍 Module 7: Advanced Topics (Optional)

• Time Series Forecasting

• Neural Networks with Flux.jl (Basic Deep Learning)

• Working with Big Data: JuliaDB, Dagger.jl

• Interoperability with Python and R

• Performance Optimization Tips

📍 Module 8: Real-Time Projects

• EDA on Open Datasets (CSV, APIs)

• Predictive Analytics Project (e.g., Sales or Demand Forecasting)

• Clustering and Customer Segmentation

• Time Series Forecasting (optional)

• Visualization Dashboards with Pluto.jl