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
Conclusion