دوره Rust and Data Science: Cleaning, Analyzing, and Visualizing Data همراه با فلش‌کارت آموزشی — ارائه روی فلش مموری

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره Rust and Data Science: Cleaning, Analyzing, and Visualizing Data

موضوع کلی: برنامه نویسی

موضوع میانی: راست (Rust)

📋 سرفصل‌های دوره

• 1. Introduction to Rust for Data Science: Why Rust?
• 2. Setting Up Your Rust Development Environment
• 3. Basic Rust Syntax: Variables, Constants, and Mutability
• 4. Primitive Data Types: Integers, Floats, Booleans, Characters
• 5. Compound Data Types: Tuples and Arrays
• 6. Understanding Strings in Rust: `String` vs. `&str`
• 7. Control Flow: Conditional Statements (`if/else`)
• 8. Control Flow: Loops (`loop`, `while`, `for`)
• 9. Functions: Defining, Calling, and Parameters
• 10. Ownership: The Core Concept of Rust's Memory Safety
• 11. Borrowing: References and Immutable/Mutable Access
• 12. Lifetimes: Ensuring Reference Validity
• 13. Structs: Custom Data Structures
• 14. Enums: Defining Custom Types with Variants
• 15. Pattern Matching with `match` Expressions
• 16. Methods: Associated Functions for Structs and Enums
• 17. Traits: Defining Shared Behavior for Types
• 18. Generics: Writing Flexible and Reusable Code
• 19. Error Handling with `Result<T, E>`
• 20. Handling Absence of Values with `Option<T>`
• 21. Introduction to Rust Crates for Data Science
• 22. Project Structure with Cargo and Dependencies
• 23. Reading CSV Files Efficiently with the `csv` Crate
• 24. Parsing JSON Data with `serde_json`
• 25. Working with Parquet Files for Columnar Data
• 26. Interacting with SQL Databases: `sqlx` Setup
• 27. Executing Queries and Fetching Data from SQL
• 28. Inserting and Updating Data in SQL Databases
• 29. Reading and Writing Other File Formats (e.g., YAML, TOML)
• 30. Basic File System Operations in Rust
• 31. Error Handling Strategies for Data Ingestion
• 32. Introduction to `Polars`: A DataFrame Library for Rust
• 33. Creating DataFrames from Various Sources
• 34. Inspect-ing DataFrame Schemas and Data Types
• 35. Efficient Data Loading Techniques
• 36. Understanding Data Quality and Common Issues
• 37. Handling Missing Values: Detection and Strategies
• 38. Imputation Techniques for Numeric Data
• 39. Dropping Rows or Columns with Missing Data
• 40. Detecting and Managing Outliers
• 41. Data Type Conversion and Coercion in DataFrames
• 42. String Manipulation for Text Cleaning (Case, Trim, Replace)
• 43. Regular Expressions for Advanced Text Processing
• 44. Removing Duplicate Records
• 45. Data Validation Rules and Constraint Enforcement
• 46. Working with Categorical Data: Encoding Approaches
• 47. One-Hot Encoding and Label Encoding
• 48. Parsing and Manipulating Date and Time Data
• 49. Handling Time Zones and Durations
• 50. Merging and Joining DataFrames Based on Keys
• 51. Concatenating and Appending DataFrames
• 52. Reshaping Data: Pivoting and Melting
• 53. Applying Custom Functions to DataFrame Columns
• 54. Data Scaling and Normalization Techniques
• 55. Binning and Discretization of Continuous Data
• 56. Selecting and Filtering Data in DataFrames
• 57. Adding, Modifying, and Renaming Columns
• 58. Sorting and Ordering Data
• 59. Grouping Data for Aggregations
• 60. Performing Basic Statistical Aggregations (Sum, Mean, Count)
• 61. Advanced Aggregations and Custom Aggregates
• 62. Introduction to Exploratory Data Analysis (EDA) Principles
• 63. Calculating Basic Descriptive Statistics (Mean, Median, Mode, Std Dev)
• 64. Frequency Distributions and Value Counts
• 65. Quantiles and Percentiles
• 66. Correlation Analysis: Pearson, Spearman Coefficients
• 67. Covariance Analysis
• 68. Introduction to Probability and Random Sampling
• 69. Generating Random Numbers and Distributions
• 70. Data Sampling Techniques for Large Datasets
• 71. Introduction to Basic Hypothesis Testing
• 72. Performing T-Tests with Statistical Crates
• 73. Chi-Squared Tests for Categorical Data
• 74. A/B Testing Fundamentals and Implementation
• 75. Anomaly Detection Basics in Data
• 76. Introduction to Machine Learning Concepts in Rust
• 77. Overview of Supervised vs. Unsupervised Learning
• 78. Regression Models: Linear Regression with `linfa`
• 79. Classification Models: Logistic Regression Basics
• 80. Clustering Algorithms: K-Means with `linfa` or `smartcore`
• 81. Model Training and Prediction Workflow
• 82. Model Evaluation Metrics for Regression (MAE, MSE, R-squared)
• 83. Model Evaluation Metrics for Classification (Accuracy, Precision, Recall, F1)
• 84. Cross-Validation and Hyperparameter Tuning Basics
• 85. Feature Engineering Principles in Rust
• 86. Introduction to Data Visualization Principles
• 87. Preparing Data for Visualization
• 88. Introduction to Rust Plotting Libraries (e.g., `plotters`, `egui_plot`)
• 89. Creating Basic Line Charts with `plotters`
• 90. Generating Scatter Plots and Histograms
• 91. Bar Charts and Pie Charts for Categorical Data
• 92. Customizing Plot Appearance and Layout
• 93. Exporting Plots to Image Files
• 94. Interfacing with Python for Advanced Visualization (PyO3, Matplotlib)
• 95. Performance Optimization: Benchmarking and Profiling Rust Code
• 96. Concurrency for Data Processing with `rayon`
• 97. Asynchronous Programming for I/O-Bound Tasks with `tokio`
• 98. Building Data Pipelines and Workflows
• 99. Deploying Rust Data Science Applications
• 100. Advanced Topics and Future of Rust in Data Science