Course Outline

Module 1: Introduction to Data Science

• Overview of Data Science and its applications.
• Introduction to the Data Science lifecycle.
• Basics of statistical analysis and hypothesis testing.
• Case Study: Analyzing the Impact of Data Science on Healthcare Outcomes.

Module 2: Data Wrangling and Pre-processing

• Data cleaning and handling missing values.
• Data transformation and feature engineering.
• Exploratory Data Analysis (EDA).
• Introduction to data visualization with Matplotlib and Seaborn.
• Case Study: Cleaning and Analyzing a Customer Feedback Dataset for Insights.

Module 3: Big Data Concepts and Tools

• Introduction to big data concepts.
• Overview of Apache Hadoop and Apache Spark.
• Hands-on experience with processing big datasets.
• Using PySpark for distributed computing.
• Case Study: Implementing Apache Spark to Process Social Media Data.

Module 4: Data Engineering and Database Management

• Relational and non-relational databases.
• SQL and database design.
• Building and querying databases.
• Introduction to data warehouses and data lakes.
• Case Study: Designing a Database for an E-commerce Platform.

Module 5: Machine Learning Fundamentals

• Understanding supervised and unsupervised learning.
• Classification and regression algorithms.
• Model evaluation and selection.
• Introduction to scikit-learn for machine learning in Python.
• Case Study: Predicting House Prices Using Regression Techniques.

Module 6: Deep Learning Basics

• Basics of neural networks.
• Introduction to deep learning frameworks (TensorFlow, PyTorch).
• Building and training neural networks.
• Application of deep learning in real-world scenarios.
• Case Study: Developing a Image Recognition Model Using Convolutional Neural Networks.

Module 7: Deployment and Productionization of Models

• Model deployment strategies.
• Building APIs for machine learning models.
• Docker containers for reproducibility.
• Monitoring and maintaining deployed models.
• Case Study: Deploying a Machine Learning Model for Real-Time Fraud Detection.

Module 8: Advanced Data Visualization

• Advanced data visualization techniques.
• Interactive visualizations with Plotly and Bokeh.
• Creating dashboards and reports for business insights.
• Case Study: Building an Interactive Dashboard to Visualize Sales Data Trends.

Module 9: Integrating Data Science Solutions

• Integrating data science solutions with existing business systems.
• Best practices for data management and governance.
• Case studies of successful data science implementations.
• Case Study: Implementing Data Science Solutions in a Retail Business for Inventory Management.

Module 10: Capstone Project and Presentation

• Capstone project: Solving a real-world business problem.
• Project presentation and feedback session.
• Review and wrap-up of key concepts learned throughout the course.
• Case Study: Presenting a Comprehensive Data Science Solution for a Business Challenge.