{"id":268,"date":"2021-01-19T18:07:38","date_gmt":"2021-01-19T12:37:38","guid":{"rendered":"https:\/\/data-flair.training\/news\/?p=268"},"modified":"2021-01-19T18:08:54","modified_gmt":"2021-01-19T12:38:54","slug":"python-and-data-science-a-match-made-in-heaven","status":"publish","type":"post","link":"https:\/\/data-flair.training\/news\/python-and-data-science-a-match-made-in-heaven\/","title":{"rendered":"Python and Data Science – A Match Made in Heaven"},"content":{"rendered":"

As more opportunities open up in data science, one can notice a common skill that all data science roles require – the knowledge of python. <\/span><\/p>\n

The language has almost become synonymous with data analysis. <\/span><\/p>\n

So, why is it so important to know Python if one wants a career in data science?\u00a0<\/span><\/p>\n

Python – A Brief Introduction<\/h2>\n

Python is a high-level general-purpose programming language. It has a simple syntax and code readability. Thus, one can get accustomed to coding in Python in no time.\u00a0<\/span><\/p>\n

In Python, there is no compilation step. It follows an edit-test-debug process which makes coding quick and easy to maintain. Coding in Python is highly productive as well.\u00a0<\/span><\/p>\n

Python for Data Science<\/h2>\n

Python is a general-purpose programming language. <\/span><\/p>\n

It is useful for web development, report generation, simulation and a host of other applications. <\/span><\/p>\n

Today, it has become a favorite tool of data scientists worldwide. How does it find application in data science?<\/span><\/i><\/p>\n

1. Gathering<\/span> data<\/span><\/h3>\n

Sometimes, it so happens that the data we need has to be collected from various sources on the web. <\/span><\/p>\n

Python has the libraries Python Scrapy and BeautifulSoup specially to extract data from the internet.\u00a0<\/span><\/i><\/p>\n

2.<\/span> Data<\/span> cleaning and preprocessing<\/span><\/h3>\n

Once the data collection process is over, the data is ready. But large datasets in real life applications usually have noise – missing values, incorrect values in categorical columns, invalid values and so on. <\/span><\/p>\n

Python has functions to identify such values and eliminate noise. Thus, Python is useful in cleaning large datasets.<\/span><\/i><\/p>\n

3.<\/span> Data<\/span> visualization<\/span><\/h3>\n

While dealing with large amounts of data, it is essential to have some visualization tools. Visualization is the mapping of data points to space. <\/span><\/p>\n

A visual representation helps in easy identification of trends, outliers and other patterns which can influence some strategic decisions.\u00a0<\/span><\/p>\n

Python libraries like Matplotlib and Seaborn offer a huge variety of tools to visually represent data. It is easy to carry out exploratory data analysis using Python libraries and methods.\u00a0<\/span><\/p>\n

4. Training a model\u00a0<\/span><\/h3>\n

The next stage of data analysis is building an appropriate machine learning model for a dataset. Python provides implementation for plenty of machine learning models. <\/span><\/p>\n

There are algorithms to train all kinds of problems – classification, regression, clustering, image recognition and many more.\u00a0<\/span><\/p>\n

Thus, it is evident that Python libraries are of use in almost every step of data analysis. Hence, Python is the most preferred language in data science.\u00a0<\/span><\/p>\n

Commonly Used Python Libraries for Data Science<\/h2>\n

Below are some of the commonly used Python libraries.<\/span><\/p>\n

1. Pandas<\/span>\u00a0<\/span><\/h3>\n

Pandas is an open-source library that is especially useful to clean, preprocess and\u00a0 analyze tabular data. <\/span><\/p>\n

It has two kinds of data structures – data frame for two-dimensional data and series for one-dimensional data.\u00a0<\/span><\/p>\n

Pandas contains methods to read tabular data, calculate summary statistics for data, combine data from tables, manipulate textual data and perform many more operations.\u00a0<\/span><\/i><\/p>\n

2. Numpy<\/span><\/h3>\n

Numpy stands for Numerical Python. The library is primarily used while working with arrays. <\/span><\/p>\n

Numpy provides methods required for linear algebra, matrix manipulations and Fourier transformation. <\/span><\/p>\n

For large single and multi-dimensional arrays, Numpy is the best library.<\/span><\/i><\/p>\n

3. Scikit-learn<\/span><\/h3>\n

Scikit-learn is the go-to library to implement machine learning algorithms on datasets. <\/span><\/p>\n

It provides all the functions necessary to apply models such as linear regression, logistic regression, decision tree, random forest, SVM and many such algorithms.\u00a0<\/span><\/i><\/p>\n

4. Seaborn<\/span><\/h3>\n

Seaborn is a popular data visualization library. From simple graphs and pie charts to heatmaps and kde plots, it is possible to implement all kinds of visualizations using methods in seaborn. <\/span><\/p>\n

Graphs created using this library and insightful and visually attractive.<\/span><\/p>\n

Benefits of using Python for Data Science<\/h2>\n

Python offers several benefits over other data analysis tools. <\/span><\/p>\n

Here are the top advantages that make the language a popular choice among data scientists.\u00a0<\/span><\/i><\/p>\n

1. The global community<\/span><\/h3>\n

Getting stuck in the middle of a project is something every programmer faces. However, with Python, there is no need to worry. <\/span><\/p>\n

Thanks to the growing number of Python developers and data scientists worldwide, there are plenty of platforms to seek help from the community. <\/span><\/p>\n

Coding and debugging in Python becomes much simpler with guidance from the worldwide community.\u00a0<\/span><\/i><\/p>\n

2. Simplicity<\/span><\/h3>\n

Most programming languages have a steep learning curve.<\/span><\/p>\n

It can be weeks or months before a novice programmer can start working on their first project. That is not the case with Python. <\/span><\/p>\n

The simple syntax coupled with relatively lesser lines of code make it an ideal language.<\/span><\/i><\/p>\n

3. Open Source Language<\/span><\/h3>\n

It is an open-source language. Thus, data scientists can install the language and its popular libraries on their devices for free.\u00a0<\/span><\/p>\n

Summary<\/h2>\n

Python serves as a versatile language with a million possibilities. With its open-sources libraries, numerous useful packages, a low learning curve, and an enthusiastic developer community, data scientists find it easy to use Python for data analysis.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

As more opportunities open up in data science, one can notice a common skill that all data science roles require – the knowledge of python. The language has almost become synonymous with data analysis....<\/p>\n","protected":false},"author":6722,"featured_media":274,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[104],"tags":[202,201,200],"class_list":["post-268","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology","tag-data-science-and-python","tag-how-is-python-useful-for-data-science","tag-python-for-data-science"],"yoast_head":"\nPython and Data Science - A Match Made in Heaven - DataFlair<\/title>\n<meta name=\"description\" content=\"Learn the importance of Python for Data Science. 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