Data Science
Course in Singapore

1. Data Science Fundamentals
2. The Data Science Lifecycle
3. Key Tools and Technologies
4. Introduction to Data Analytics
5. Career Opportunities in Data Science in Singapore

This module focuses on essential statistical concepts crucial for data science.

1. Descriptive Statistics
2. Probability Theory
3. Inferential Statistics
4. Hypothesis Testing
5. Regression Analysis

This module covers programming languages essential for data science tasks.

1. Python for Data Science
2. R Programming
3. SQL for Data Management
4. Data Structures and Algorithms
5. Version Control with Git

This module explores techniques for cleaning, transforming, and preparing data for analysis.

1. Data Cleaning Techniques
2. Handling Missing Data
3. Feature Engineering
4. Data Normalization and Scaling
5. Working with Different Data Formats

This module teaches techniques for visualizing and understanding data patterns.

1. Data Visualization Techniques
2. Statistical Data Exploration
3. Correlation Analysis
4. Dimensionality Reduction
5. Time Series Analysis

This module covers core machine learning concepts and algorithms.

1. Supervised Learning Algorithms
2. Unsupervised Learning Techniques
3. Model Evaluation and Validation
4. Ensemble Methods
5. Feature Selection and Dimensionality Reduction

This module explores advanced machine learning techniques using neural networks.

1. Neural Network Architectures
2. Convolutional Neural Networks (CNNs)
3. Recurrent Neural Networks (RNNs)
4. Transfer Learning
5. Deep Learning Frameworks (TensorFlow, PyTorch)

This module covers techniques for processing and analyzing text data.

1. Text Preprocessing Techniques
2. Sentiment Analysis
3. Topic Modeling
4. Named Entity Recognition
5. Machine Translation

This module introduces technologies for handling and processing large-scale data.

1. Hadoop Ecosystem
2. Apache Spark
3. Distributed Computing
4. NoSQL Databases
5. Data Streaming and Processing

This module focuses on creating impactful visualizations and presenting data insights effectively.

1. Data Visualization Best Practices
2. Tools for Data Visualization (Tableau, Power BI)
3. Interactive Dashboards
4. Storytelling with Data
5. Presenting Insights to Stakeholders

This module covers ethical issues and best practices in data science.

1. Data Privacy and Security
2. Bias and Fairness in AI
3. Responsible AI Development
4. Data Governance
5. Ethical Decision Making in Data Science

Students will apply their knowledge to a real-world data science project.

1. Project Planning and Scoping
2. Data Collection and Preprocessing
3. Model Development and Evaluation
4. Results Interpretation and Visualization
5. Project Presentation and Defense