Q. what are Statistics?

~ Statistics is a branch of applied mathematics that involves the collection, description, analysis, and inference of conclusions from quantitative data. The mathematical theories behind statistics rely heavily on differential and integral calculus, linear algebra, and probability theory.

                       People who do statistics are referred to as statisticians. They’re particularly concerned with determining how to draw reliable conclusions about large groups and general events from the behavior and other observable characteristics of small samples. These small samples represent a portion of the large group or a limited number of instances of a general phenomenon.

Key Takeaways

• Statistics is the study and manipulation of data, including ways to gather, review, analyze, and draw conclusions from data.

• The two major areas of statistics are descriptive and inferential statistics.

• Statistics can be communicated at different levels ranging from non-numerical descriptor (nominal-level) to numerical in reference to a zero-point (ratio-level).

• Several sampling techniques can be used to compile statistical data, including simple random, systematic, stratified, or cluster sampling.

• Statistics are present in almost every department of every company and are an integral part of investing.

If we ever meet, don’t go all shy or awkward — I’m not a viva examiner 😅

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About Me:

Hi! , I'm Pankaj Kumar. 

I'm a First Year student of IIT-M  BS Data Science Student.

Currently I'm in Foundation Level Last Term.

About Course : Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied.

Statistics is a fundamental pillar for extracting meaningful insights from complex datasets. It makes acquiring inferences from enormous amounts of data easier. Dealing day and night with data, data scientists need powerful tools and methodologies that would ease the work while performing accurate analysis. Offering the right insights into patterns, trends, predictions, and decision-making, statistics for data science is valuable for testing hypotheses, quantifying uncertainties, and contributing to robustness and analysis reliability.