This
Statistics for Data Science course is designed to introduce you to the
basic principles of statistical methods and procedures used for data
analysis. After completing this course you will have practical knowledge
of crucial topics in statistics including - data gathering, summarizing
data using descriptive statistics, displaying and visualizing data,
examining relationships between variables, probability distributions,
expected values, hypothesis testing, introduction to ANOVA (analysis of
variance), regression and correlation analysis. You will take a hands-on
approach to statistical analysis using Python and Jupyter Notebooks –
the tools of choice for Data Scientists and Data Analysts.
At the end of the course, you will complete a project to apply
various concepts in the course to a Data Science problem involving a
real-life inspired scenario and demonstrate an understanding of the
foundational statistical thinking and reasoning. The focus is on
developing a clear understanding of the different approaches for
different data types, developing an intuitive understanding, making
appropriate assessments of the proposed methods, using Python to analyze
our data, and interpreting the output accurately. This course is
suitable for a variety of professionals and students intending to start
their journey in data and statistics-driven roles such as Data
Scientists, Data Analysts, Business Analysts, Statisticians, and
Researchers. It does not require any computer science or statistics
background. We strongly recommend taking the Python for Data Science
course before starting this course to get familiar with the Python
programming language, Jupyter notebooks, and libraries. An optional
refresher on Python is also provided.
