Data science on the palms

Table of contents

*– Additional material

• Motivation
• Tools
  • Python
    • Basic knowledge (syntax, variables, loops, I/O…)
    • Data Structures
    • OOP (Object Oriented Programming)
    • Standard libraries
    • Exceptions*
    • Tests*
  • Mathematics knowledge
    • Probability theory
    • Statistics
  • Libraries
    • Pandas
    • Matplotlib
    • NumPy
    • Scikit-learn
  • Databases
    • SQL (language)
    • MySQL, PostgreSQL
• Data Collection
  • Think before you start
  • Work with different data types
  • Reading from the common data storage systems
  • Types of secondary data sources
• Data Preprocessing
  • Data cleaning
  • Data integration
  • Data transformation
  • Data reduction
  • Data discretization
• Model Training
• Model Evaluation

Motivation

↑ back to table of contents

Tools

↑ back to table of contents

Python

↑ back to table of contents

Mathematics knowledge

↑ back to table of contents

Libraries

↑ back to table of contents

Databases

↑ back to table of contents

Data Collection

↑ back to table of contents

Data Preprocessing

↑ back to table of contents

Model Training

↑ back to table of contents

Model Evaluation

↑ back to table of contents