Data Science Full Stack Course Syllabus
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Our in-depth full stack data science course syllabus is intended to provide you the highly sought-after abilities of a Full Stack Data Scientist. Delve into the useful facets of data engineering, data visualization, and implementing data science solutions and acquire a solid foundation in fundamental data science principles, programming languages, and machine learning techniques. Our full stack data science syllabus covers core data science concepts, machine learning algorithms, data engineering concepts, big data technologies and tools like Hadoop, Spark, data visualization tools like Tableau, DevOps technologies like Docker and Kubernetes, and so on.
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.
