Exploring Python and Its Significance in Data Science.pptx

Exploring Python and Its Significance
in Data Science
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As the world entered the era of big data in the last few
decades, the need for better and more efficient data storage
became a significant challenge. Organizations that use big data
have primarily focused on developing frameworks that can
store large amounts of data. Then frameworks such as Hadoop
were developed, which aided in the storage of massive
amounts of data.

After resolving the storage issue, the attention turned to processing the data that had been
stored. Data science emerged as the way of the future for data processing and analysis at this
point. Data science is now an essential component of all organizations that work with large
amounts of data. Today’s organizations employ data scientists and professionals to take raw data
and turn it into useful information.

What is Data Science?
Finding and exploring data in the real world and applying that knowledge to
solve an organization’s problems is the epitome of data science. Here are
some examples of the diverse applications of data science:
 Predictions from Customers: A system can be trained to predict the
likelihood of a customer purchasing a product based on the customer’s
behavior patterns.
 Service Planning: Restaurants can forecast how many customers will
visit over the weekend and stock up on food to meet demand.

Now that you understand what data science is, let us discuss the
fundamental skills required for data science before delving into the topic of
data science with Python. Here are the basic skills:
 Programming Language (Python or R)
 Database and Big Data Skills (SQL)
 Machine Learning Skills
 Mathematics
 Data Visualization
 Statistics
 Big Data
 Data Wrangling

Programming Language for Data Science
A successful data science project necessitates some level of programming
language. According to research, Python is the most popular data science
programming language with 87 percent of all languages’ popularity. Python is
particularly popular due to its ease of use and support for a wide range of data
science and machine learning libraries. In this article, we’ll look at Python and how
it can help in data science. Python is a widely-used programming language for the
following reasons:
 Python is an accessible and highly interpretable programming language. It is
open-source software that anyone can use.
 Errors in the code are easy to understand because Python explains the error
statement in detail, highlighting the line number where the error occurs.
 Writing Python programs is similar to writing sentences in English. It also
facilitates debugging and exception handling.
 Scikit-Learn, NumPy, Pandas, and Matplotlib are examples of Python libraries
that can be used to solve data science and machine learning problems.
https://youtu.be/pD8YM2kea4M

Why Python?
Python has grown in popularity as a programming language in recent years. Its use in
data science, IoT, AI, and other technologies has increased its popularity. Python is a
programming language that data scientists recommend because it is user-friendly, has
a large community, and has a good library availability. It is one of the primary reasons
that data scientists all over the world use Python. Other reasons why Python is one of
the most popular programming languages for data science include:
1.Speed: Python is a relatively fast programming language when compared to other
programming languages.
2.Availability: There are many packages available that have been developed by other
users and can be reused.
3.Design Goal: Python syntax roles are intuitive and straightforward to grasp, which
aids in developing applications with a readable codebase.
4.Choice of Libraries: Python has a massive library like NumPy, Pandas, and SciPy.
Many of these collections are also easily accessible in the form of tutorials.
5.Visualization and Graphics: Python provides several different visualization options.

Python Libraries for Data Analysis
Python is a simple programming language to learn, and you can do some basic things with it, such as
adding and printing statements. However, you’ll need to import specific libraries if you want to do data
analysis. Here are a few examples:

Let’s take a closer look at a few of the most important Python libraries:
NumPy: An essential Python package for scientific computing is NumPy. It includes
the following:
 Arry objective with powerful N-dimensional
 C/C++ and Fortran code integration tools
 It has linear algebra, Fourier transform, and random number capabilities that are
all useful
SciPy: It’s a scientific library with some unique features, as the name suggests.
 Special functions, integration, ODE solvers, gradient optimization, and other
features are available
 It includes fully functional linear algebra modules
 NumPy is used to create it

Pandas: Pandas is used to perform structured data operations and
manipulations.
 Python’s most valuable data analysis library
 Contributing to the increased use of Python in the data science
community
 Data mugging and preparation are common uses for this tool
Data Wrangling Using Pandas
The process of cleaning and unifying messy and complicated data sets is
referred to as data wrangling. Some of the advantages of data wrangling are
as follows:
 More data about your information is revealed
 Enhances the organization’s decision-making abilities
 Assists in the collection of valuable and precise data for the organization

In reality, most of the data generated by an organization will be
sloppy and contain missing values. There are several options for
filling in the blanks. The business scenario will determine which
parameters to use when filling them in. To see if your data has
any missing values, do the following:
 Dtypes can be used to check the data types for each
column.
 Use simple concatenation and merge methods to combine
and merge data frames.

Conclusion
Python is an essential tool in the Data Analyst’s toolbox because it is
designed to perform repetitive tasks and data manipulation. Anyone who has
worked with large amounts of data knows how often repetition occurs.
Because a tool handles the grunt work, Data Analysts can focus on their jobs
making it more exciting and rewarding.
Get Started
Check out InfosecTrain’s Data Science with Python Certification Course if you
want to get a head start in data science. Our Data Science with Python
Certification Course will show you how to use Python to master data science
and analytics techniques. With this course, you’ll learn the fundamentals of
Python programming and gain in-depth, helpful knowledge in data analytics,
data visualization, Exploratory Data Analysis(EDA), Statistics, machine
learning and deep learning.

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