Statistics plays a key role in data science, providing meaningful insights through analysis of raw data and building statistical models. The document discusses the importance of statistics in data science, describing quantitative and qualitative statistical analysis methods. It also outlines three central measures of statistics: mean, median, and mode. The document is an overview of statistics for data science courses provided by Datamites.

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2. Data Science is showing it’s influence across
different industries. Businesses are increasingly
moving towards a data driven approach. It is
known to be the mix of three disciplines,
namely Statistics, Mathematics and Computer
Science. Let’s look at the share of statistics in
the field of Data Science .
Introduction
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3. What is Statistics ?
● Statistics is one of the source from which Data
Science derives its meaning and also the reason
for its existence. Statistics in Data Science is
majorly used to analyse the huge amounts of data
collected.
● Statistics helps in providing meaningful insights
through the analysis of raw data, build a Statistical
Model and infer or predict the result.
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4. Types of Statistical Analysis
Quantitative Analysis
it involves the analysis and interpretation of the data that are
numerical in nature. It is otherwise called as ‘Statistical Analysis’.
Qualitative Analysis
It is the analysis of those data which are non-numerical in nature.
It includes data which are in audio/visual formats.
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5. Measures of Central Tendency
➔ There are known to be three important
measures of central tendency.
➔ Mean
➔ It is used to find the average of the datasets.
➔ Median
➔ It refers to the value in the middle of a given
data set.
➔ Mode
➔ It is the value that is repetitive in the data set,
or the most common value.
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