Table of Contents
- Introduction
- Day 1: Introduction to Data Science
- Importance of Data Science
- Types of Data
- Data science strategies
- Programming language
- Review Quiz
- Day 2: Data Science Lifecycle
- Infrastructure and resources for data science projects
- Stage I – Business understanding
- Stage II – Data acquisition and understanding
- Stage III – Modeling
- Stage IV – Deployment
- Stage V – Customer Acceptance
- Review Quiz
- Day 3: Big Data 101
- Importance of big data
- The functioning of big data
- Big Data Analytics
- Applications of Big Data Analytics
- Big Data Analysis Vs. Data Visualization
- Review Quiz
- Day 4: Basics of Data Mining
- Applications of data mining
- The data mining process
- Pros of data mining
- Challenges of data mining
- Data Mining Trends
- Data mining tools
- Day 5: Data Analysis Frameworks
- Ensemble Learning
- Decision Trees
- Random Forest
- Day 6: Data Analysis Libraries
- Scikit-Learn
- SciPy (Fundamental library for scientific computing)
- SymPy (Symbolic mathematics)
- NumPy (Base n-dimensional array package)
- Matplotlib (Comprehensive 2D/3D plotting)
- Pandas (Data structures and analysis)
- IPython (Enhanced interactive console)
- Jupyter Notebook
- Day 7: Predictive Analytics
- Importance of Customer Analytics
- Marketing and Sales Funnel Analytics
- Predictive Analytics Marketing
- Personalized marketing
- Extra content
- Python programming
- Python Machine Learning
