Popular
Applications of Mathematics in Data Science
Businesses across all industries
need data scientists to help them function and be successful on a daily basis.
Understanding how you can use math in practical scenarios can help you
understand why businesses need data scientists and how mathematics comes into
play.
Let’s look at some practical uses of
mathematics in popular data science and machine
learning applications and technologies being utilized by leading
organizations today:
Natural
Language Processing (NLP)
Linear algebra is used in NLP for word embeddings, and unsupervised
learning techniques like topic modeling and predictive analytics. Examples of
uses of NLP include chatbots, language translation, speech recognition, and
sentiment analysis.
Data is increasing at an alarming rate. A large
portion of the data available today is in the form of text. Natural Language
Processing is a popular branch of AI which helps Data Science in extracting
insights from the textual data. Following this, Industry experts have predicted
that there will be a huge demand for Natural Language Processing professionals
in the near future. In this tutorial, we will discuss some of the important NLP Techniques used in the field of Data
Science.
Natural Language Processing or NLP is a branch that focuses on teaching
computers how to read and interpret the text in the same way as humans do. It
is a field that is developing methodologies for filling the gap between Data
Science and human languages.
Everything we speak or express holds great information and can be useful in
making valuable decisions. But extracting this information is not that easy as
humans can use a number of languages, words, tones, etc. All these data that we
are generating through our conversations, tweets, etc is highly unstructured.
The traditional techniques are not capable of extracting insights from this
data. But thanks to the advanced technologies like machine learning and NLP
that have brought a revolution in the field of Data Science.
Many areas like Healthcare, Finance, Media, Human Resources, etc are using
NLP for utilizing the data available in the form of text and speech. Many text
and speech recognition applications are built using NLP. For example, personal
voice assistants like Siri, Cortana, Alexa, etc.
NLP techniques in Data Science
Let us see some of the most widely used NLP techniques in Data Science.
1. Bag of Words
This model counts the number of words in a piece of text. This model works
by generating an occurrence matrix for the sentences. The underlying grammar
and the order of words are not considered while generating the matrix.
These occurrences or counts are then fed into a classifier as features.
A compound sentence is a sentence formed with two or main clauses joined by
a coordinating conjunction.
A simple sentence is a sentence formed with one main clause.
Now let’s generate the occurrence matrix for this:
This approach is very simple to understand but it has several drawbacks
also. This model gives no idea about the semantics and the context in which the
words are used. Also, some words like “a” or “the” which appear frequently but
are not that important may create noise during analysis. Another problem is
that in the above example, the word “then” holds more weight than the word
“universe” i.e words are not weighted according to their importance.
To overcome these issues, we use an approach called Term
Frequency-Inverse Document Frequency (TF-IDF).
2. Term Frequency-Inverse Document Frequency
(TF-IDF)
Term Frequency-Inverse Document Frequency or TF-IDF overcomes the drawbacks
of Bag of Words by using a weighting factor. It uses statistics for calculating
the importance of a word in a document. Let us understand the statistics of
TF-IDF.
TF or Term frequency: It measures the frequency of a word
in a document. This is calculated by counting the total number of occurrences
of the word and dividing it by the total length of the document.
IDF or Inverse Document Frequency: It measures the
importance of a word in a document. For example, words such as is, a, of, etc
which occur frequently in the document but they do not hold much importance as
they are not adjectives or verbs. Thus this technique assigns a weight to any
string according to its importance. It is calculated by taking the log of the
total number of documents in the dataset divided by the number of documents
containing that particular word (also 1 is added to the denominator so that it
is never 0).
TF-IDF: Finally it calculates the importance of any word by
multiplying the TF and IDF terms i.e TF*IDF.
Thus the words having more importance are assigned higher weights by using
these statistics. This technique is mostly used by search engines for scoring
and ranking the relevance of any document according to the given input
keywords.
Popular
Applications of Mathematics in Data Science
Businesses across all industries
need data scientists to help them function and be successful on a daily basis.
Understanding how you can use math in practical scenarios can help you
understand why businesses need data scientists and how mathematics comes into
play.
Let’s look at some practical uses of
mathematics in popular data science and machine
learning applications and technologies being utilized by leading
organizations today:
Natural
Language Processing (NLP)
Linear algebra is used in NLP for word embeddings, and unsupervised
learning techniques like topic modeling and predictive analytics. Examples of
uses of NLP include chatbots, language translation, speech recognition, and
sentiment analysis.
Data is increasing at an alarming rate. A large
portion of the data available today is in the form of text. Natural Language
Processing is a popular branch of AI which helps Data Science in extracting
insights from the textual data. Following this, Industry experts have predicted
that there will be a huge demand for Natural Language Processing professionals
in the near future. In this tutorial, we will discuss some of the important NLP Techniques used in the field of Data
Science.
Natural Language Processing or NLP is a branch that focuses on teaching
computers how to read and interpret the text in the same way as humans do. It
is a field that is developing methodologies for filling the gap between Data
Science and human languages.
Everything we speak or express holds great information and can be useful in
making valuable decisions. But extracting this information is not that easy as
humans can use a number of languages, words, tones, etc. All these data that we
are generating through our conversations, tweets, etc is highly unstructured.
The traditional techniques are not capable of extracting insights from this
data. But thanks to the advanced technologies like machine learning and NLP
that have brought a revolution in the field of Data Science.
Many areas like Healthcare, Finance, Media, Human Resources, etc are using
NLP for utilizing the data available in the form of text and speech. Many text
and speech recognition applications are built using NLP. For example, personal
voice assistants like Siri, Cortana, Alexa, etc.
NLP techniques in Data Science
Let us see some of the most widely used NLP techniques in Data Science.
1. Bag of Words
This model counts the number of words in a piece of text. This model works
by generating an occurrence matrix for the sentences. The underlying grammar
and the order of words are not considered while generating the matrix.
These occurrences or counts are then fed into a classifier as features.
A compound sentence is a sentence formed with two or main clauses joined by
a coordinating conjunction.
A simple sentence is a sentence formed with one main clause.
Now let’s generate the occurrence matrix for this:
This approach is very simple to understand but it has several drawbacks
also. This model gives no idea about the semantics and the context in which the
words are used. Also, some words like “a” or “the” which appear frequently but
are not that important may create noise during analysis. Another problem is
that in the above example, the word “then” holds more weight than the word
“universe” i.e words are not weighted according to their importance.
To overcome these issues, we use an approach called Term
Frequency-Inverse Document Frequency (TF-IDF).
2. Term Frequency-Inverse Document Frequency
(TF-IDF)
Term Frequency-Inverse Document Frequency or TF-IDF overcomes the drawbacks
of Bag of Words by using a weighting factor. It uses statistics for calculating
the importance of a word in a document. Let us understand the statistics of
TF-IDF.
TF or Term frequency: It measures the frequency of a word
in a document. This is calculated by counting the total number of occurrences
of the word and dividing it by the total length of the document.
IDF or Inverse Document Frequency: It measures the
importance of a word in a document. For example, words such as is, a, of, etc
which occur frequently in the document but they do not hold much importance as
they are not adjectives or verbs. Thus this technique assigns a weight to any
string according to its importance. It is calculated by taking the log of the
total number of documents in the dataset divided by the number of documents
containing that particular word (also 1 is added to the denominator so that it
is never 0).
TF-IDF: Finally it calculates the importance of any word by
multiplying the TF and IDF terms i.e TF*IDF.
Thus the words having more importance are assigned higher weights by using
these statistics. This technique is mostly used by search engines for scoring
and ranking the relevance of any document according to the given input
keywords.
Computer
Vision
Linear algebra is also used for computer vision such as image representation and
image processing. When people think about computer vision, companies like Tesla
come to mind for their self-driving cars. Computer vision is also frequently
used in industries like agriculture to improve yields, or healthcare to
classify illnesses and improve diagnoses.
Marketing
and Sales
Statistics is useful for testing the
effectiveness of marketing campaigns such as hypothesis testing. It’s also used
to understand consumer behavior, such as why consumers are purchasing from a
specific brand, in techniques like causal effect analysis or survey design, and
personalization recommendations via predictive modeling or clustering.
Pursue
Your Math and Data Science Education
Math is a core educational pillar
for data scientists, regardless of your future industry career path. It ensures
you can help an organization solve problems and innovate more quickly, optimize
model performance, and effectively apply complex data towards business
challenges.
Ensure that you’re building the
right skill sets and mathematical capabilities through a leading online
bootcamp provider like Simplilearn. They offer Data
Science Certification Courses that guide you through everything you
need to know in pursuit of your data science career—including courses dedicated
to mathematics.
Computer
Vision
Linear algebra is also used for computer vision such as image representation and
image processing. When people think about computer vision, companies like Tesla
come to mind for their self-driving cars. Computer vision is also frequently
used in industries like agriculture to improve yields, or healthcare to
classify illnesses and improve diagnoses.
Marketing
and Sales
Statistics is useful for testing the
effectiveness of marketing campaigns such as hypothesis testing. It’s also used
to understand consumer behavior, such as why consumers are purchasing from a
specific brand, in techniques like causal effect analysis or survey design, and
personalization recommendations via predictive modeling or clustering.
Pursue
Your Math and Data Science Education
Math is a core educational pillar
for data scientists, regardless of your future industry career path. It ensures
you can help an organization solve problems and innovate more quickly, optimize
model performance, and effectively apply complex data towards business
challenges.
Ensure that you’re building the
right skill sets and mathematical capabilities through a leading online
bootcamp provider like Simplilearn. They offer Data
Science Certification Courses that guide you through everything you
need to know in pursuit of your data science career—including courses dedicated
to mathematics.
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