Statistics for Data Science

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Statistics for Data Science
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Introduction-
The processing of data is the most critical part
of any Data Science method. When we speak about
gaining information from results, we're simply
talking about exploring the possibilities.
Statistical Analysis is the term for these
possibilities in Data Science. The majority of
us are perplexed as to how Machine Learning
models can process data in the form of text,
photographs, videos, and other highly
unstructured formats. But the fact is that we
transform the data into a numerical type that
isn't exactly our data, but it's near. As a
result, we've arrived at a critical feature of
Data Science.
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Using different statistical measures, determine
the value of features.
Importance of Statistics for Data Science
To remove the risk of duplicate features, find
the connection between features.
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Data normalization and scaling This move often
entails determining the distribution of data as
well as the nature of data.
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Taking the data for further analysis and making
the required corrections.
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Key Concept of Statistics
This are the core principles for learning and
speeding up the foundations of statistics for
data science.
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Probability
Understanding the possibilities requires a basic
understanding of probability. To begin, consider
the following scenario What are the chances that
Team A would win the football match against Team
B? To arrive at this conclusion, we will need 100
people to cast their ballots Number of
Samples. We may predict which team to win the
game based on certain votes. Sampling As we saw
in the previous example, sampling is the process
of identifying the appropriate group of people.
The question is, who are the appropriate
individuals? To continue our previous example,
we will need 100 people who are knowledgeable
about football, are familiar with Team A and B's
history, and are not biassed towards a team
because of their personal preference. As a
result, various statistical approaches can be
used to identify the appropriate sample. Simple
random sampling, systematic sampling,
Stratified sampling, Clustered sampling, and
other sampling methods exist.
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Tendency and Distribution of Data
The distribution of data is a crucial factor. The
significance of a well-known distribution such
as the Normal Distribution is enormous. When we
talk about the world's height and weight
distribution, for example, we're talking about
normally distributed data that demonstrates
nature's symmetry. At the central peak of the
normal distribution, the Mean, Mode, and Median
must all coincide. These are supposed to be
extremely precise numbers. As a result,
determining the distribution and skewness of data
is a crucial concept. Hypotheses Testing If we
know whether or not to take a certain action.
Will those actions produce a positive or
negative outcome, and if so, we will have the
added benefit of doing the right thing.
Hypotheses Testing identifies situations in which
action should be taken or not taken based on the
expected outcomes. Other tests with similar
relevance include A/B Testing, Z Test, T-Test,
and Null Hypothesis.
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