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:
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 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 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:
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:
SciPy: It’s a scientific library with some unique features, as the name suggests.
Pandas: Pandas is used to perform structured data operations and manipulations.
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:
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:
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.
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