Python for Data Science
Python for Data Science is becoming a popular choice for most companies. Learn why data scientists prefer using Python and how it benefits a company’s data analytics.
In this modern age, the approach to running a business has become more and more data-driven. With the rise of tech trends like AI and Machine Learning, it’s become clear that tech solutions are the key to getting ahead in the industry.
When a company successfully integrates cutting-edge technology in their operations, they’re guaranteed an edge over their competitors. After all, technology is designed to maximize efficiency.
Data science, in particular, offers valuable insights that can rapidly scale a startup. We can’t deny the fact that data is crucial when it comes to making major business decisions. Hence, if you want to ensure a smooth-running operation, you’d better invest in data science.
But before we delve further, let’s first understand the whole concept of data analytics and why it’s so important.
What is Data Science?
Data science is the study of information extracted from complex or big data. It interprets the said data with a combination of methods in statistics and computation. Businesses often use these data sets as a baseline in making major operational changes.
Simply put, data science refers to the numbers you’re looking at when you’re evaluating your company’s performance and your target market’s behavior. Generally, we use these numbers as proof to back up our conclusion, especially in creating proposals.
In this modern age, you extract data from any channel, tool, or platform that an organization has access to. With the vast amount of information shared nowadays, companies can conveniently monitor customer behavior and buying patterns. And they can customize and design their products and services based on these gathered data.
These are some of the common ways startups use data science:
- Data mining
- Machine Learning
How Data Science Works
Now that we’ve learned what data science is and why it’s important to business, it’s time to find out how it works. Don’t worry, we’re not going into the technicalities. We’ll just discuss the stages of data analysis.
To be clear, data scientists don’t actually write code intensively. But they do need to have that basic programming knowledge to design and create their data models.
Their focus is on creating algorithms and predictive models to extract and analyze relevant data. So, to get a better understanding of how they work, we’ll see what technology data scientists use.
Python for Data Science
According to a 2018 Kaggle Survey, Python is the top programming language preferred by data scientists. In a more recent version of that survey, most data scientists are still using Python-based tools for their work.
Although they can technically use other programming languages, Python stands out. It has certain features that make data retrieval and analysis a lot more convenient.
Python is a general-purpose language. Because of its flexible nature and simple syntax, it’s easy for data scientists and developers to collaborate. Additionally, Python’s straightforward system makes it easy to communicate across platforms and people.
So, how is Python used for data science? To better illustrate this, let’s see how it’s incorporated in the different stages.
Stage 1: Parallel Processing
The first stage of data science is to obtain the raw form of data. To get insights, a data scientist can use certain functions and search for specific types of data. Using Python libraries can cut down hours of data retrieval as it can carry out the parallel processing approach.
Stage 2: Data Scraping
The next step is to filter and scrape away unwanted and unnecessary data from the web pages. This stage determines what information will be kept. Python Scrapy and BeautifulSoup are some of the industry’s best libraries to extract data quickly and efficiently from the web.
Stage 3: Data Visualization
At this stage, the data scientist will compile the data and produce their graphic representation (pie charts, graphs, infographics, etc.). Python’s libraries Seaborn and Matplotlib are great tools to create graphical layouts, web-ready plots, charts, and other data visuals.
Stage 4: Data Computation
The final stage involves complex computation methods since it’s about machine learning. Python’s Scikit-Learn can process complicated mathematical formulas and combine functions of multiple tools.
As you can see from the abovementioned stages, Python has a set of libraries that provide great solutions for each process. Now that we’ve learned the benefits of using Python for data science, the next question is, “should your business use it?” It depends.
Why Use Python for Data Science?
Although Python is the preferred language by data scientists, it’s still a case-to-case basis depending on a company’s needs. You’d have to consider what technologies and platforms you’re using. Nonetheless, it is a highly recommended language for data science.
Here are some of its key points:
One of the main reasons Python is popular is because it’s easy to learn. Unlike other programming languages, Python has simple syntax and functions. It only takes a few lines of code to create a whole function.
As an open-source language, Python has a large support community. Many members of this community strive to build world-class data science libraries. As a result, Python sees a lot of updated tools and first-class processing regularly.
With its large community also comes a rich amount of libraries and resources. Developers and data scientists alike can make use of large library databases. On top of that, the community generously shares countless tutorials on using Python tools.
Overall, Python is a great language to use whether you’re a starting company or a big corporation. Its flexible nature makes it easily scalable. Plus, it’s an open-source language so it won’t break the bank. If you’re just trying out data science for the first time, then it’s definitely a great starting point.
Hire Data Scientists
As technology develops further, so should your startup. In this modern age, it pays to keep up with the trend. But of course, you can’t just recklessly jump on the bandwagon.
Making data-driven decisions for your startup is essential. You don’t just make major changes on a whim. That’s why hiring data scientists will give you a big advantage. Don’t know where to find them? We can help!
Full Scale helps you build your team! We have seasoned tech experts in the industry. Whether you need data scientists, developers, project managers; we’ve got the best ones ready to help out. Our team of experts can work on your software development project. We offer a flexible arrangement that will cater to your company’s needs.
Want to learn more? Contact us!