According to a forecast from International Data Corporation, the worldwide revenues of Big Data and Business Analytics solutions would reach $260 billion by the end of 2020. It’s no surprise, data analytics allows businesses to predict customer needs and personalize their approach to customers.
Data analytics is gaining popularity. If back in 2015 only 17% of companies have been utilizing big data analytics, in 2017 the percentage has grown to 53% and is getting higher each year.
To join the most successful companies that use data, and reap the benefits of it, you must be proficient in at least one programming language for data science.
This article will examine Python, one of the most popular data science using Python programming languages. Learn whether Python is suitable for data analysis and how to use it for data analysis. Also, learn about its pros and cons.
What is the best language for data analysis?
Although Python was first introduced in 1990, it only gained popularity a few years later. In 2020, Python became the fourth most used language after JavaScript, HTML/CSS, and SQL, with 44,1% of developers using it.
Python is an interpretable, general-purpose language that can be used for any purpose and has an object-oriented approach. It is used to develop APIs, Artificial Intelligence, web design, Internet of Things, and other purposes.
The part of why Python has become so popular is because it is widely used among data scientists. It is one the easiest languages to learn, has amazing libraries, and works flawlessly for all stages of data science.
The short answer to the question “Is Python good for data analysis?” is “Yes.” We’ll discuss the pros and cons of Python later in this article.
What is Python used for data analysis?
Python is a good tool for data analysis at all stages, as we’ve already mentioned. The Python libraries designed for data science are the most useful.Data mining, data processing and modelingAlong withdata visualizationThese are the three most common ways Python is used for data analysis.
Data Mining
Data engineers use libraries like BeautifulSoup and Scrapy to data mine Python-based data. Scrapy allows you to create programs that collect structured data from the internet. It can also be used to collect data from APIs.
BeautifulSoup can be used to retrieve data from APIs. It scrapes data and arranges it in the preferred format.
Data Processing And Modelling
At this stage, NumPy (Numerical Python) and Pandas (Pandas) are the main libraries. NumPy (Numerical Python) is used to organize big data sets. It makes it easier to perform math operations and vectorize them on arrays. Pandas provides two types of data structures: series (a list with items) and data frames. A data frame is a table with multiple columns. This library converts data into the data frame, allowing you to add or delete columns and perform other operations.
Data Visualization
For Python data visualization, Matplotlib or Seaborn are popular. This means they can convert large numbers into easily-understood graphics, histograms and pie charts, heatmaps, and so on.
Of course, there are way more libraries than we have mentioned. Python provides many tools that can be used to analyze data and assist in any part of the process.
Alternatives to Python for Data Analysis
Although Python is the most popular language for data analysis there are many other languages. Each language is focused on a specific task, such as mining, visualization or working with large data sets. Some languages are only designed for data analysis and statistical computing, which means they have all the necessary features.
R
R is the second most popular language for data analysis and is often compared with Python. It is ideal for data analysis and was designed for statistical computing. R is a powerful tool for data visualization. It is compatible with all statistical applications. R can also be used offline. Developers have access to rich software packages for data manipulation and charting.
SQL
SQL is widely used to query and edit data. It’s also an excellent and tried tool for data storage, retrieving and retrieving. The language is able to work with large databases and retrieve information faster than other languages.
Julia
Julia was created for scientific computing and data science. Although it is still a new language, data scientists are rapidly adopting it. The main purpose of the language is to overcome the disadvantages that Python has shown in data analysis and become the first choice of data engineers. Julia can be compiled, which gives it faster performance and a similar syntax to Python. It also has more math-friendly syntax and can use Python, C and Forton libraries. Parallel computing is a feature that is more advanced than Python and is well-known in the language.
Scala
Scala and its framework Spark are often used for projects with big-volume databases and are beloved by BigData engineers. It is possible to work with the data in smaller chunks and not download the entire set. Scala runs on JVM. It can easily be embedded in enterprise code. Scala has many data transformation tools and is faster than R and Python with explicit loops.
These languages are most used by data scientists and analysts. It’s important to note that data scientists and analysts may also use MATLAB, TensorFlow, JavaScript, and Parallel Computing for big data analysis.
Conclusion
Any business looking to gain a competitive edge in the market and make informed business decisions has to have data.
Data analysis can be done in many languages, including R, SQL and Scala. Each language can do some tasks better than another in data development. There is no perfect language, but there are some that will work better for your project.
Python is still the most used language for data analysis. It is easy to learn and has many libraries that support data analysts at every stage of their work.
