Is Python the Right Choice for Enterprise Data Analysis?
One of the most widely used programming languages, Python’s use in data science extends to many diverse applications. It includes finance, agriculture, healthcare consulting and technology among various others.
For instance, healthcare players use Python’s ML algorithms to optimize hospital operations and diagnose and prevent diseases, better. On the other hand, in agriculture, Python helps project yield and uses IoT to mitigate crop diseases.
The wide use of Python for data analysis is evident from these examples. But is it genuinely the right choice for enterprise data analysis? If yes, what makes it so? Let’s explore some aspects of the use of Python in enterprise data analysis.
The Use of Python for Enterprise Data Analytics
Data analytics has a multi-dimensional role to play in the enterprise environment. It isn’t only about processing data to make informed decisions. Other concepts like data mining, modeling, and visualization also exist. Here’s how Python proves itself a comprehensive data analytics partner.
- Data Mining
While mining data with Python, data engineers use libraries like BeautifulSoup and Scrapy. BeautifulSoup helps scrape data and arrange it in a preferable format. Scrapy helps build programs that can collect structured data from the web.
- Data Visualization
Python data visualization involves the use of Seaborn and Matplotlib. It helps convert long lists of numbers into simplified graphics, heatmaps, pie charts, etc. But the list doesn’t end here. Data engineers use many more libraries that support the needs of data analysis projects.
- Data Processing and Modeling
Again, the two libraries that play a significant role are Pandas and NumPy.
Pandas offer two data structures – series (an item list) and data frames (a multiple-column table). It enables you to convert data to the data frame, thus allowing you to add new columns or delete the existing ones.
On the other hand, NumPy (Numerical Python) helps arrange big datasets, simplifies math operations, and makes vectoring them on arrays, easier.
- Descriptive Analytics
Descriptive analytics helps fetch descriptions of events that have happened. It helps us understand trends and evaluate metrics over a particular duration.
Python enables data analysts to describe and classify the currently existing data. They perform exploratory analytics involving data profiling, creating observations, and visualizing results, which pave the way to the next steps.
Libraries like Pandas can help manipulate data and streamline workflows. Matplotlib can enable data analysts in creating visualizations.
- Predictive Analytics
Looking into the future and knowing what it has in store excites everyone. ML, one of the predictive analytics branches, uses streamlined statistical algorithms to make predictions based on existing insights and identify relationships.
Python has emerged as a winner across this space. It is quickly becoming the bankable language of ML, as it helps prepare models for decision trees, Bayesian networks, etc. TensorFlow, which is Google’s Python-based library, enables data scientists to access many supervised and unsupervised ML algorithms.
Is Python the Correct Choice for Enterprise Data Analytics?
Python’s numerous libraries and applications in the enterprise context make it apt for enterprise data analytics’ complex and diverse needs.
While you now know the uses, here are more factors that make Python an apt choice:
- Extensive and Free Library Range
Of course, Python offers various libraries to use. But what excites companies more is that they are free to use. It helps them control their data analysis budget. Additionally, they continuously evolve and get upgraded, thus staying relevant within the competitive environmental context.
- Higher Degree of Flexibility
It is precisely what enterprises appreciate. They can use Python for various fields and projects with any rapid application development tool. It is the enormous amount of flexibility that qualifies Python for enterprise data analytics.
Enterprise data analytical needs are as complex as they are extensive. Thus, data scientists, analysts, and engineers may frequently need technical support to seek answers to their questions. Python offers nearly 90,000 repositories on GitHub. Users can connect with the community and find solutions to their concerns seamlessly.
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