Create a comprehensive data analysis pipeline using real-world datasets and tools like Pandas, NumPy, and Matplotlib

Learn to load and clean datasets using Pandas and NumPy, and understand data quality issues

Load and Clean Data

Create informative visualizations using Matplotlib and Seaborn to communicate data insights

Visualize Data Insights

Apply statistical concepts like mean, median, and correlation to real-world datasets

Perform Statistical Analysis

Combine data from different sources using SQL and Pandas, and handle data inconsistencies

Integrate Data from Multiple Sources

Create an interactive dashboard using Dash and Plotly to showcase data insights

Deploy a Data Analysis Dashboard

Assess your understanding of data analysis pipelines and statistical concepts

📝 Recall - Module 1

Build a Data Analysis Pipeline

Create a compelling data visualization story using Tableau and Power BI, and learn to communicate insights effectively

Select the most suitable visualization type for different data types and insights

Choose the Right Visualization

Build interactive dashboards using Tableau and Power BI, and add filters and drill-down capabilities

Create Interactive Dashboards

Use color theory and design principles to create visually appealing and effective visualizations

Apply Color Theory and Design Principles

Add context and annotations to visualizations to make them more informative and engaging

Add Context and Annotations

Learn to present data insights effectively to different audiences, including stakeholders and business leaders

Present Data Insights Effectively

Assess your understanding of data visualization principles and effective communication

📝 Recall - Module 2

Design a Data Visualization Story

Create a predictive modeling workflow using scikit-learn and TensorFlow, and learn to evaluate model performance

Learn to prepare data for modeling, including data preprocessing and feature engineering

Prepare Data for Modeling

Select the most suitable algorithm for different problem types, including regression and classification

Choose the Right Algorithm

Train and evaluate models using scikit-learn and TensorFlow, and learn to tune hyperparameters

Train and Evaluate Models

Apply cross-validation and bootstrapping techniques to evaluate model performance and reduce overfitting

Use Cross-Validation and Bootstrapping

Learn to deploy models to production using TensorFlow Serving and AWS SageMaker

Deploy Models to Production

Assess your understanding of predictive modeling workflows and model evaluation

📝 Recall - Module 3

Develop a Predictive Modeling Workflow

Optimize database performance using indexing, caching, and query optimization techniques

Learn to design efficient database schemas, including data normalization and denormalization

Design Efficient Database Schemas

Apply indexing and caching techniques to improve query performance and reduce latency

Use Indexing and Caching

Optimize queries and transactions using SQL and NoSQL databases, and learn to avoid common pitfalls

Optimize Queries and Transactions

Monitor and analyze database performance using metrics and logging tools, and identify bottlenecks

Monitor and Analyze Database Performance

Learn to scale databases for high availability, including replication, sharding, and load balancing

Scale Databases for High Availability

Assess your understanding of database performance optimization and query optimization

📝 Recall - Module 4

Optimize Database Performance

Create a data warehouse and ETL pipeline using AWS Redshift and Apache Beam, and learn to integrate data from multiple sources

Learn to design a data warehouse architecture, including data modeling and schema design

Design a Data Warehouse Architecture

Create an ETL pipeline using Apache Beam and AWS Glue, and learn to handle data transformations and loading

Build an ETL Pipeline

Integrate data from multiple sources, including databases, APIs, and files, using AWS Glue and Apache NiFi

Optimize data warehouse performance, including query optimization and data partitioning

Optimize Data Warehouse Performance

Monitor and analyze data warehouse metrics, including query performance and data quality

Monitor and Analyze Data Warehouse Metrics

Assess your understanding of data warehouse design and ETL pipelines

📝 Recall - Module 5

Build a Data Warehouse and ETL Pipeline

Create a machine learning model deployment strategy, including model serving, monitoring, and maintenance

Select a model serving platform, including TensorFlow Serving, AWS SageMaker, and Azure Machine Learning

Choose a Model Serving Platform

Deploy models to production, including model deployment, monitoring, and logging

Monitor model performance and data drift, including metrics and alerts

Monitor Model Performance and Data Drift

Maintain and update models, including model retraining and redeployment

Maintain and Update Models

Use model interpretability techniques, including feature importance and partial dependence plots

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