Will Python Be the Leading Language in 2019?
If you are yet to start learning Python, this is the best time to do this. The fact is that everyone in the field is now embracing this programming language, and it is believed that 2019 will be a great year for the professionals who have taken time to learn it. According to report, Python is becoming the preferred programming language for IT specialists, especially data scientists. No doubt, if you are a newbie in programming, the first language you should try to learn is Python. The reason for this is quite simple: Python is easy to learn, it is versatile and can be applied to a wide range of disciplines. This programming language has a lot to offer the professionals in the year 2019. If there is any language you should start learning right now, it is Python. In this article, we will explore the reasons why it is becoming popular in the field of data science and why it is ideal for this realm. We will also look at how you can utilize it for machine learning projects and big data.
First, let us consider why you should learn Python if you are new in the field of programming. For those individuals who are just coming into the industry, it is normal to be a bit confused about where to start. There are numerous options of programming languages to choose from, ranging from C, C++, Ruby, and Java, among others. There have been various debates focusing on the language that is the best for beginners. Java usually comes to mind as the best option for newbies in programming. Interestingly, Python is becoming an excellent recommendations and for some other great reasons. There have been different indexes that evaluate programming languages popularity, and they have highlighted the rapid increase in the popularity of Python. For example, TIOBE indicator ranks its popularity based on the high volume on search engine and results. Python ranks 3rd with a 7.6% popularity share. Java ranks 1st with a 17.4% share while C ranks 2nd with 15.4% share. It is essential to mention that within a short period, Python has broken into the top three ranks in programming languages, and this can only point to one thing: it has come to stay and overtake the leaders in the field. Python ranks first in other indexes like IEEE and PyPL overtaking Java and C++. With this report, it is obvious that it is steadily taking the center stage as far as a programming language is concerned. What more reasons do you need to start learning the language right now? Well, if you still need to be convinced to learn Python, let us look at some other points.
Another exciting attribute of Python is readability. Code is quite difficult to read, especially if you are not an expert. However, with an average understanding of Python, you can easily read code and identify what it does. This language offers you the opportunity to get to the point where you can read and decode without being an expert in the field. Most of the programming languages don’t provide you with this opportunity. Python is developed around concepts and ideas that are simple in expression with fewer lines when compared to other languages.
In the aspect of white space, Python also ups its game in the use of white space indentation. Generally, indented lines are compulsory over braces and brackets, as it is common with C++. These are essential for defining the start and end points of various code chunks. It looks like sentences and can become quite confusing. On the other hand, the language does not have braces. You will see indentations rather than braces grouping the code in Python programming. The job of whitespace indentation is to make code very easy to read, understand, maintain, and change. It also enables readers to identify easily the structure of any program. No doubt, the ability to identify structure is very important to successful programming.
Currently, there are more than 70,000 libraries in the Python Package Index. This number is expected to continue growing. It is important to reiterate that Python provides various libraries that are targeted at data science. Now, if you do a simple Google search on top ten Python libraries for data science, you will be amazed at the huge number of lists that will be generated. According to studies, the most famous data analysis library is pandas, an open source library. It is known as a high performance range of applications that make analysis of data with Python a seamless and very simple task. Whatever you are trying to achieve with this language: whether it is prescriptive analytics or predictive causal analytics, you can be sure that it has the perfect tool sets that you need to carry out a wide range of powerful tasks. Therefore, it is little wonder that data scientists prefer Python as a programming language.
Ruby is also a great choice of programming language for performing various tasks like data mugging, data cleaning, and other data preprocessing responsibilities. However, it does not have lots of machine learning libraries features like Python. This attribute gives Python a big edge when it comes to machine learning and data science. Python also helps developers to design programs and get the prototypes running, which makes the development process faster. One major reason why the entry-level data scientists choose this language is because of the ease of use. According to report, about 48% of data scientists with fewer than five years work experience ranked Python as a preferred programming language. Interestingly, as the level of experience increases, the preferred choice changes, and this is because the analytics tasks require more extensive language at this stage. However, one thing that is certain is; Python has established itself as an excellent choice for entry-level data scientists.
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First, let us consider why you should learn Python if you are new in the field of programming. For those individuals who are just coming into the industry, it is normal to be a bit confused about where to start. There are numerous options of programming languages to choose from, ranging from C, C++, Ruby, and Java, among others. There have been various debates focusing on the language that is the best for beginners. Java usually comes to mind as the best option for newbies in programming. Interestingly, Python is becoming an excellent recommendations and for some other great reasons. There have been different indexes that evaluate programming languages popularity, and they have highlighted the rapid increase in the popularity of Python. For example, TIOBE indicator ranks its popularity based on the high volume on search engine and results. Python ranks 3rd with a 7.6% popularity share. Java ranks 1st with a 17.4% share while C ranks 2nd with 15.4% share. It is essential to mention that within a short period, Python has broken into the top three ranks in programming languages, and this can only point to one thing: it has come to stay and overtake the leaders in the field. Python ranks first in other indexes like IEEE and PyPL overtaking Java and C++. With this report, it is obvious that it is steadily taking the center stage as far as a programming language is concerned. What more reasons do you need to start learning the language right now? Well, if you still need to be convinced to learn Python, let us look at some other points.
Advantages of Python
Python was first developed in the 80s as a programming language designed to be more perceptive and human readable than other low level languages. On the rank of user friendliness, C++ ranks quite low when real CPU machine code is involved. Python was designed to be easy, pleasant to work with, and stylish. There was not much emphasis placed on regular syntax, which makes the process of learning and debugging of code less rage-stimulating. Features such as readability, wealth of excellent documentation, use of white space, and rich communities make Python a much easier programming language to learn for beginners.Another exciting attribute of Python is readability. Code is quite difficult to read, especially if you are not an expert. However, with an average understanding of Python, you can easily read code and identify what it does. This language offers you the opportunity to get to the point where you can read and decode without being an expert in the field. Most of the programming languages don’t provide you with this opportunity. Python is developed around concepts and ideas that are simple in expression with fewer lines when compared to other languages.
In the aspect of white space, Python also ups its game in the use of white space indentation. Generally, indented lines are compulsory over braces and brackets, as it is common with C++. These are essential for defining the start and end points of various code chunks. It looks like sentences and can become quite confusing. On the other hand, the language does not have braces. You will see indentations rather than braces grouping the code in Python programming. The job of whitespace indentation is to make code very easy to read, understand, maintain, and change. It also enables readers to identify easily the structure of any program. No doubt, the ability to identify structure is very important to successful programming.
Why are Python and Data Science Very Compatible?
Data science entails extracting valuable information from enormous stores of registers, data, and statistics. These raw data are often un-arranged and hard to associate with meaningful precision. Technically, machine learning has the capability to make correlations between contrasting data sets but needs extensive power and computational literalism. This is where Python becomes very useful. It fills the need by becoming a multi-purpose programming language. Python allows you to develop CSV output designed for seamless data reading in spreadsheets.Currently, there are more than 70,000 libraries in the Python Package Index. This number is expected to continue growing. It is important to reiterate that Python provides various libraries that are targeted at data science. Now, if you do a simple Google search on top ten Python libraries for data science, you will be amazed at the huge number of lists that will be generated. According to studies, the most famous data analysis library is pandas, an open source library. It is known as a high performance range of applications that make analysis of data with Python a seamless and very simple task. Whatever you are trying to achieve with this language: whether it is prescriptive analytics or predictive causal analytics, you can be sure that it has the perfect tool sets that you need to carry out a wide range of powerful tasks. Therefore, it is little wonder that data scientists prefer Python as a programming language.
Python in 2019
The skills in Python will continue to be in high demand and this is expected to grow in 2019. Looking at the TIOBE index, it is easy to come to the conclusion that more enterprises will adopt Python, which will eventually drive the demand for Python expertise and skills. Therefore, if you are considering exploring programming, it is highly recommended that you start with this programming language. You don’t have to be a programmer to learn it. An understanding of Python can help you in other fields. You can also employ it in other trendy technology areas, such as data science, information security, and machine learning. Academic institutions have also adopted the use of Python and they use it extensively. Many organizations have their personal sets of libraries like code add-ons that offer functionality without necessarily having to start coding from the scratch.Why is Python the Best Programming Language?
Over the years, Python has become a better choice of simple programming language, especially when you look at it from syntax point of view. In addition to this, it has a very active community with a large section of resources and libraries. What this means is that the professionals have a programming platform that is ideal for use with budding technologies such as data science and machine learning. You shouldn’t be swamped with complex programming requirements when working with data science applications. All you need is a programming language such as Python to carry out your task without stress.Ruby is also a great choice of programming language for performing various tasks like data mugging, data cleaning, and other data preprocessing responsibilities. However, it does not have lots of machine learning libraries features like Python. This attribute gives Python a big edge when it comes to machine learning and data science. Python also helps developers to design programs and get the prototypes running, which makes the development process faster. One major reason why the entry-level data scientists choose this language is because of the ease of use. According to report, about 48% of data scientists with fewer than five years work experience ranked Python as a preferred programming language. Interestingly, as the level of experience increases, the preferred choice changes, and this is because the analytics tasks require more extensive language at this stage. However, one thing that is certain is; Python has established itself as an excellent choice for entry-level data scientists.
Final Thoughts
The year 2019 is going to be a great year for the professionals with skill sets in the Python programming language. This is basically because many organizations in the field of IT and other industries are shifting focus to it. The implication of this is that there will be a high demand for the individuals with Python skill sets. Taking the steps to learn the programming language at this point is an excellent decision because it will position you for better opportunities in the industry. Python is still in its developmental stage, which means it gets regular updates. Thus, learning it for data science is a rewarding experience because machine learning and big data are becoming more popular in government and business, which means the demand for the Python skilled professionals is about to grow exponentially.More information: https://www.spotoclub.com/
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