Python for Data Science 101

2 tutorials

Learning NumPy and statistics is essential for anyone interested in Data Science. NumPy is a Python library that provides powerful tools for working with multi-dimensional arrays and matrices, making it a fundamental building block for many Data Science workflows. With NumPy, you can easily manipulate large datasets, perform advanced mathematical operations, and create custom algorithms for your analyses.

Statistics, on the other hand, is the foundation of all data analysis, providing the tools and techniques for making sense of complex data sets. By learning statistics, you can gain a deeper understanding of probability theory, data distributions, hypothesis testing, and much more.

Together, NumPy and statistics provide the foundational skills and knowledge needed to tackle complex Data Science problems, enabling you to derive insights and make data-driven decisions with confidence. Build a strong foundation in both of these with this module!

Helpful prior knowledge


Learning Outcomes

By the end of this module, learners will be able to:

  • Understand how to use the NumPy library
  • Understand the importance of statistics in Data Science
  • Understand how to use NumPy to perform statistical measures
  • Clean dirty data using Python
  • Conduct exploratory data analysis using Python libraries
  • Perform machine learning modelling using Python libraries
Oops, you are not logged in!

Please log in to view this page, and provide additional information required (if any) to unlock the full experience on Learn.