Data Science and Data Analytics
Course Curriculum
Module 1: Build a Data Analysis Pipeline
Create a comprehensive data analysis pipeline using real-world datasets and tools like Pandas, NumPy, and Matplotlib
Load and Clean Data
Learn to load and clean datasets using Pandas and NumPy, and understand data quality issues
Visualize Data Insights
Create informative visualizations using Matplotlib and Seaborn to communicate data insights
Perform Statistical Analysis
Apply statistical concepts like mean, median, and correlation to real-world datasets
Integrate Data from Multiple Sources
Combine data from different sources using SQL and Pandas, and handle data inconsistencies
Deploy a Data Analysis Dashboard
Create an interactive dashboard using Dash and Plotly to showcase data insights
📝 Recall - Module 1
Assess your understanding of data analysis pipelines and statistical concepts
Module 2: Design a Data Visualization Story
Create a compelling data visualization story using Tableau and Power BI, and learn to communicate insights effectively
Choose the Right Visualization
Select the most suitable visualization type for different data types and insights
Create Interactive Dashboards
Build interactive dashboards using Tableau and Power BI, and add filters and drill-down capabilities
Apply Color Theory and Design Principles
Use color theory and design principles to create visually appealing and effective visualizations
Add Context and Annotations
Add context and annotations to visualizations to make them more informative and engaging
Present Data Insights Effectively
Learn to present data insights effectively to different audiences, including stakeholders and business leaders
📝 Recall - Module 2
Assess your understanding of data visualization principles and effective communication
Module 3: Develop a Predictive Modeling Workflow
Create a predictive modeling workflow using scikit-learn and TensorFlow, and learn to evaluate model performance
Prepare Data for Modeling
Learn to prepare data for modeling, including data preprocessing and feature engineering
Choose the Right Algorithm
Select the most suitable algorithm for different problem types, including regression and classification
Train and Evaluate Models
Train and evaluate models using scikit-learn and TensorFlow, and learn to tune hyperparameters
Use Cross-Validation and Bootstrapping
Apply cross-validation and bootstrapping techniques to evaluate model performance and reduce overfitting
Deploy Models to Production
Learn to deploy models to production using TensorFlow Serving and AWS SageMaker
📝 Recall - Module 3
Assess your understanding of predictive modeling workflows and model evaluation
Module 4: Optimize Database Performance
Optimize database performance using indexing, caching, and query optimization techniques
Design Efficient Database Schemas
Learn to design efficient database schemas, including data normalization and denormalization
Use Indexing and Caching
Apply indexing and caching techniques to improve query performance and reduce latency
Optimize Queries and Transactions
Optimize queries and transactions using SQL and NoSQL databases, and learn to avoid common pitfalls
Monitor and Analyze Database Performance
Monitor and analyze database performance using metrics and logging tools, and identify bottlenecks
Scale Databases for High Availability
Learn to scale databases for high availability, including replication, sharding, and load balancing
📝 Recall - Module 4
Assess your understanding of database performance optimization and query optimization
Module 5: Build a Data Warehouse and ETL Pipeline
Create a data warehouse and ETL pipeline using AWS Redshift and Apache Beam, and learn to integrate data from multiple sources
Design a Data Warehouse Architecture
Learn to design a data warehouse architecture, including data modeling and schema design
Build an ETL Pipeline
Create an ETL pipeline using Apache Beam and AWS Glue, and learn to handle data transformations and loading
Integrate Data from Multiple Sources
Integrate data from multiple sources, including databases, APIs, and files, using AWS Glue and Apache NiFi
Optimize Data Warehouse Performance
Optimize data warehouse performance, including query optimization and data partitioning
Monitor and Analyze Data Warehouse Metrics
Monitor and analyze data warehouse metrics, including query performance and data quality
📝 Recall - Module 5
Assess your understanding of data warehouse design and ETL pipelines
Module 6: Develop a Machine Learning Model Deployment Strategy
Create a machine learning model deployment strategy, including model serving, monitoring, and maintenance
Choose a Model Serving Platform
Select a model serving platform, including TensorFlow Serving, AWS SageMaker, and Azure Machine Learning
Deploy Models to Production
Deploy models to production, including model deployment, monitoring, and logging
Monitor Model Performance and Data Drift
Monitor model performance and data drift, including metrics and alerts
Maintain and Update Models
Maintain and update models, including model retraining and redeployment
Use Model Interpretability Techniques
Use model interpretability techniques, including feature importance and partial dependence plots
📝 Recall - Module 6
Assess your understanding of machine learning model deployment and maintenance