Develop essential data science & ai skills with expert instruction and practical examples.
Statistics is a subject like salt, needed in every food. The process of gathering, analysing, and interpreting data is all covered by statistics, which also offers a conceptual framework. The mathematical underpinnings for machine learning and data mining are provided by statistics, which is employed in many fields of scientific and social study as well as in business and industry.
The principles and methods of statistics as they are used in a wide range of fields will be thoroughly introduced to students in this course, especially data shaping, central tendency, data viability, z-scores and most importantly the probability. for the central centendency mean, median, and mode are illustrated along with practice problems; and skewed distributions are explained, as well as how to calculate the weighted mean. For the data viability, this course will help you to understand and explain variability (spread) in a set of numbers, including how to rank data and interpret data such as standardized test scoresThe information and abilities you need to begin data analysis are given to you in this course.
You'll investigate ways to utilise facts and utilise statistics. Each topic is explained with various parameters so that learners can use the command in many practical scenarios. Some images/ contents used in this course are under license: Creative Commons Attribution licence (reuse allowed), presented by Frank H Netter at the Quinnipiac University.
We adapted the material, added more supporting files, and quizzes.
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