Data Science Full Stack Course Syllabus
Course Syllabus
Download SyllabusModule 1: Introduction to Data Science and Full Stack Development
- Understanding Data Science and Its Growing Demand
- Role of a Full Stack Data Scientist
- Key Tools and Technologies in Data Science
- Overview of Data Engineering, Data Analysis, and Data Visualization
- Real-World Applications of Data Science
Module 2: Programming for Data Science
- Introduction to Python for Data Science
- Key Python Libraries: NumPy, Pandas, Matplotlib, and Scikit-learn
- Introduction to R Programming for Statistical Analysis
- Data Structures and Algorithms for Data Science
- Best Practices for Writing Efficient and Scalable Code
Module 3: Data Collection and Preprocessing
- Data Sourcing: APIs, Web Scraping, Databases, and Open Datasets
- Data Cleaning Techniques: Handling Missing Data, Duplicates, and Outliers
- Feature Engineering and Dimensionality Reduction Techniques
- Encoding Categorical Data and Normalization Methods
- Automating Data Preprocessing Tasks
Module 4: Databases and Data Storage
- SQL for Data Science: Queries, Joins, Aggregations, and Subqueries
- NoSQL Databases (MongoDB, Firebase) for Unstructured Data Storage
- Cloud Databases and Data Warehousing Concepts
- Optimizing Database Performance for Large-Scale Applications
Module 5: Data Visualization and Analytics
- Exploratory Data Analysis (EDA) for Insights Extraction
- Visualization with Matplotlib, Seaborn, Plotly, and Tableau
- Dashboard Development for Interactive Data Exploration
- Best Practices for Data Storytelling and Presentation
Module 6: Machine Learning Fundamentals
- Introduction to Supervised and Unsupervised Learning
- Linear and Logistic Regression, Decision Trees, Random Forests
- Clustering Techniques (K-Means, Hierarchical Clustering)
- Model Evaluation, Bias-Variance Tradeoff, and Hyperparameter Tuning
- Hands-on Machine Learning Projects
Module 7: Deep Learning and AI
- Introduction to Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN) for Image Processing
- Recurrent Neural Networks (RNN) for Time Series and NLP
- Transfer Learning and Pretrained Models
- Hands-on Deep Learning Projects with TensorFlow and Keras
Module 8: Web Development for Data Science Applications
- Frontend Development with HTML, CSS, JavaScript
- React.js for Interactive Data Dashboards
- Backend Development with Flask and Django for API Integration
- Deployment of Data Science Applications on the Web
Module 9: API Development and Integration
- Understanding RESTful APIs and Their Role in Data Science
- Creating APIs for Machine Learning Models
- Testing and Securing APIs for Scalable Applications
- Connecting Web and Mobile Apps to AI Models
Module 10: Cloud Computing and Big Data
- Introduction to Cloud Platforms (AWS, Google Cloud, Azure)
- Big Data Technologies: Hadoop, Spark, and Distributed Computing
- Building and Managing Data Pipelines for Large Datasets
- Cloud-Based Model Deployment and Scalability
Module 11: DevOps for Data Science
- Introduction to CI/CD in Data Science
- Docker and Kubernetes for Model Deployment
- Monitoring and Logging for Data Pipelines
- Automation of Machine Learning Workflows
Module 12: Capstone Project and Case Studies
- End-to-End Data Science Project Development
- Real-World Case Studies from Finance, Healthcare, and E-commerce
- Industry Best Practices and Ethical Considerations
- Portfolio Building, Resume Writing, and Job Interview Preparation
Get acquainted with programming concepts, mathematics, statistics, TensorFlow, PyTorch, AWS, analytical and problem-solving, and so on through our full stack data science syllabus.
