Below is the outline for the creation of videos and course content. Each check should have a video + a reading component.
- Visual Studio Code
- Git and GitHub
- Overview of JavaScript: History, importance, applications in business and data science.
- Setting Up the Environment: Installing Node.js, relevant packages, and code editors.
- Basic Syntax: Variables, data types, operators.
- Control Structures: Conditionals, loops.
- Functions: Declaration, invocation, and scope.
- OOP: Classes
- Objects and Arrays: Data structures and their manipulation.
- Asynchronous JavaScript: Promises, async/await, and callbacks.
- Error Handling: Try-catch blocks, error objects.
- DOM Manipulation: How to interact with web pages.
- Event Handling: User interactions and event-driven programming.
- AJAX and Fetch API: Fetching data from servers.
- Data Visualization: Using libraries like D3.js and Chart.js.
- Loading and Parsing Data: Reading data from various sources (CSV, JSON, APIs).
- Data Cleaning: Techniques for handling missing values, duplicates, and data transformation.
- Data Transformation: Aggregation, merging, and reshaping data.
- Introduction to Data Visualization: Importance and principles of data visualization.
- Creating Visualizations with D3.js: Basic charts (bar, line, scatter plots), interactive visualizations.
- Advanced Visualizations: Network graphs, heatmaps, geospatial visualizations.
- Data Manipulation with D3.js: Creating, modifying, and visualizing data.
- Data Analysis with Danfo.js: Data frames, series, and data manipulation.
- Statistical Analysis with Simple-statistics: Common statistical functions and analysis.
- Machine Learning with TensorFlow.js: Introduction to TensorFlow.js, building and training models.
- Descriptive Statistics: Measures of central tendency and variability.
- Inferential Statistics: Hypothesis testing, confidence intervals.
- Regression Analysis: Linear regression, multiple regression, logistic regression.
- Supervised Learning: Classification, regression, decision trees, random forests.
- Unsupervised Learning: Clustering, dimensionality reduction (PCA).
- Deep Learning with TensorFlow.js: Neural networks, training and evaluation, use cases.
- Form Validation: Ensuring data integrity.
- User Authentication: Basics of secure login systems.
- APIs and Microservices: Interfacing with other systems.
- Introduction to Frameworks: Why use them? Overview of popular ones (React, Angular, Vue.js).
- Choosing the Right Framework: Criteria for selection based on business needs.
- Using Libraries for Common Tasks: jQuery, Lodash, Moment.js.
- CRM Systems: Building and maintaining customer relationship management tools.
- E-commerce Solutions: Shopping carts, payment gateways.
- Data Analytics Dashboards: Real-time data tracking and visualization.
- Real-World Data Science Projects: End-to-end projects from data collection to model deployment.
- Case Studies: Success stories and practical applications of JavaScript in data science.
- Code Organization and Clean Code: Modular programming, code readability.
- Testing and Debugging: Unit tests, integration tests, debugging tools.
- Performance Optimization: Efficient coding practices, memory management.
- Common Vulnerabilities: XSS, CSRF, etc.
- Best Practices for Security: Protecting data, secure coding standards.
- Deploying Data Science Models: Using JavaScript for deployment, serverless functions.
- Deploying JavaScript Applications: Server setups, CI/CD pipelines.
- Maintaining Business and Data Science Applications: Version control, updating dependencies, managing data pipelines and user feedback.
- AI and Machine Learning Trends: What's next in JavaScript for data science.
- The Role of JavaScript in Big Data: Hadoop, Spark, and JavaScript integration.
- Exploring New Libraries and Tools: Keeping up with the evolving landscape.
- Useful Resources: Books, websites, forums, and communities.
- Glossary of Terms: Definitions of common terms used in the book.
- Sample Datasets and Projects: Example code, datasets, and projects for practice.