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

Download Here

    3 months later