Introduction to Data Science and Data Pre-Processing

Learning Objectives

By the end of this chapter, you will be able to:

• Use various Python machine learning libraries
• Handle missing data and deal with outliers
• Perform data integration to bring together data from different sources
• Perform data transformation to convert data into a machine-readable form
• Scale data to avoid problems with values of different magnitudes
• Split data into train and test datasets
• Describe the different types of machine learning
• Describe the different performance measures of a machine learning model

This chapter introduces data science and covers the various processes included in the building of machine learning models, with a particular focus on pre-processing.