Artificial Intelligence is a booming sector of Information Technology. AI has gained a lot of popularity over the years and now finds itself a part of various sectors even outside the IT industry. Smartphones have also helped it insert itself into the daily lives of most people.
Python is a popular language that is preferred by computer scientists and data scientists worldwide. The language offers many advantages that make it ideal for tasks involving large amounts of codes and is also very easy to master as well as reliable in execution.
Advantages of Python - At A Glance
Python is simpler than most languages and requires less code to be written for executing any program or building platforms. As compared to more popular languages like C and, using python saves a significant amount of code, and directly translates to less time spent in building and execution. Therefore, even though python is slightly slower than languages like C, the fact that it is easier and takes less time to build more than makes up for it.
Python is also one of the most flexible languages out there. It works across a variety of platforms and can adapt to be suitable for various situations and tasks. Python is platform-independent, which makes it convenient to work with across all platforms and add to mobility as well. Parts of the same program can be coded and executed across different platforms and operating systems, ensuring their suitability for all users. It also allows the programmer to choose between object oriented programming as well as scripting, which saves a lot of time and effort, and IDE is available to check for most programs, which helps coders a lot.
Diverse List Of Python Libraries
This is perhaps the most attractive feature that Python has to offer. It has a plethora of inbuilt libraries which are a boon for programmers struggling with lines and lines of code. These libraries perform many functions that are useful across all fields in IT, such as data sciences, app building and web development.
There are libraries fit for every task, and Artificial Intelligence is no exception. It even has a specific library for Machine Learning functions, called Pybrain. Other popular libraries include Numpy for scientific computation and Scipy for advanced computation.
The Vibrant Community
This is a general advantage that Python has due to its popularity. Python enjoys a lot of support online, and most of this support is from online communities of programmers and computer scientists. Python has a large fanbase of helpful individuals who are all eager to learn and are helpful enough to support to clear doubts, or even fix codes. This vibrant community is the heart and soul of python and helps bring more people into it.
Python for AI - A perfect Combo!
The role of python as the go-to language for AI/ML applications have been growing for years. Python is a strong language overall and offers unparalleled ease and flexibility as a programming language. Though it is not perfect, it’s advantages have attracted many engineers to use python for the following applications.
Python Libraries and AI
The large number of open source libraries that python boasts of is a key player in many AI programs using python. There are many examples of this, and the most notable ones are as follows.
NumPy is perhaps the single most important and useful library in python and finds its application in almost all Python programs. It can be a storage space for generic data and help call some very frequently used functions. It is the most versatile tool for scientific computation, it has N-Dimensional arrays, fourier transforms, random number capabilities and many other functions.
Other equally useful libraries are pandas, which provide easy to use data structures and analytic tools, and matplotlib which, as the name would imply, assist in creating graphs and mathematical plots of high quality via a Graphical User Interface.
Certain python libraries used exclusively for AI applications are AIMA, pyDatalog and SimpleAI among others. This shows that Python is well equipped to handle tasks and create AI solutions. Any AI expert can vouch for the benefit of having a large number of pre-existing libraries at their disposal.
Python in Practice - Diverse Practical Applications
Python is used in building many popular websites and applications such as Instagram, Facebook, YouTube and Gmail. The versatility of the language makes it easier to use it to cover multiple aspects in the same code. Since python is a glue language, it also makes it easier to add parts of code from other languages to it.
Most websites today which is built using Python uses some form of Machine Learning or the other. Whether it is suggesting new content or targeted advertising, they all involve steps of data analytics. The same tool that is used to perform this function can also be used for building and integrating AI into a platform.
TensorFlow, designed by the members on the Google Brain team, is a system that facilitates ML in Research, to help make a smooth transition from research prototype to production. Scikit-learn contains simple, open-source tools for data mining and analytics. Theano is essentially a calculator that can help the user define, solve and optimize complex mathematical expressions using multidimensional arrays. All these make use of Python as the foundation, the language used to build them. More examples can be found here.
The Verdict - Python Amplifies The Power of AI
Python has disrupted almost all fields of Information Technology, and has proved to be capable of keeping up with the times, no matter what new applications arise for it. This refreshingly simple, yet powerful language shows promise and infinite capabilities, which are still being explored. AI is yet another crown jewel for python. Proficiency in the language is a must have for all aspirants in the field.
Python has been growing as a popular choice among Data Scientists. The language itself has merits that make it a great choice for all kinds of programming as well as analytic applications. As far as Data science goes, Python shines in that arena as well.
Python in Data Sciences - A Wide Array Of Application
Python in itself is a very flexible and user-friendly language. It can be adapted for any function and is also a glue language, which means lines of codes written in other languages can directly be added into the Python code. There are certain traits of Python that can be exploited in the Data Science field, which is the reason behind it being such a necessity for every data scientist.
For starters, Python is much simpler than conventional languages which makes it easy to study, write as well as troubleshooting. Coding and compiling in Python is an easy process, and the simplicity makes it easier for programmers to find and fix errors. This has helped its growth and now enjoys support from a large community of Data Scientists, which makes working with it easier.
Python also has a large number of libraries for Data Science applications, making it easier to produce results with it. These libraries are bundles of pre-existing functions that can be directly imported into your project to save time. Python has an ever-evolving collection of packages makes work less tedious and more productive. This is what makes the language a blessing.
Learning Python: For Data Scientists
Python is a flexible language with infinite capabilities, which makes it almost impossible to learn fully. However, this also means that as a Data Scientist, there are parts of Python that you need not concern yourself with, and you can selectively learn the part of the language which suits your work and the current project. Learning Python as a whole is a good thing, but to save time, its easier to start with the general concepts and learn as you go.
Start with the Basics - An Absolute Must
First things first; Learning to code is the basic part of learning any language. And coding in Python is quite easy due to how simplistic the language is. The short and crisp syntax makes it looks less of a test of typing and is quite refreshing as compared to other languages. You can start with basic commands and functions, move on to fundamental concepts like loops and reach reasonably high levels quite fast. Some applications and websites teach Python to users by having them practice python exercises of increasing complexity, helping them learn as they go.
Mini Python Projects For The Much Needed Experience
As it is with everything else, Practice makes you perfect. For Python, reading the code and learning the commands aren’t the key to learning it, but the practice is. Python mini-projects can help to understand how the language works by using it to solve problems and perform operations. The more you practice, the more you improve.
You can also improve by looking at codes written by other programmers, solving Python Exercises online and in the same ways as you learn any other subject.
Well-equipped Python Library
This is the most important part of Python that attracts coders to it. The language itself is growing in popularity due to its large and well-equipped Library. It has additions that you can directly attach to your code, which contains functions that would otherwise require you to manually add to the program. This makes it easier to execute them and obtain results faster.
Python Library is a package that has tools for every application, so not every addition is meant for every program. It is vital to recognise the ones that will serve your purpose, and hence a waste of time to learn about the ones that do not v=concern you. Therefore, learning about Python Libraries should be more of a continuous process, where you come across new and useful ones as you progress and start working. However, there are important libraries that you can learn about right off the bat, like NumPy and Pandas for Data Manipulation and Matplotlib for Data Visualisation and Plotting.
It is easier to learn Python nowadays due to the vast availability of experts and resources online. Forums like Quora and StackOverflow can help you interact with others, clear your doubts and learn easier.
Advanced Data Science in Python
As you progress, you will be able to do Advance Data Science applications like Regression models and k-means clustering on Python, which will be symbolic of how far you’ve progressed. Your ability as a Data Scientist will increase with your proficiency in Python, and you will even be able to start with higher tier aspects like Machine Learning.
Python as a Subject - Go For It
Learning a computer programming language is like any other subject, as it takes time and effort. It all depends on your skill, proficiency and dedication. However, once mastered, Python can be a strong point on your CV and an indispensable tool in your arsenal. It’s growing popularity is a sign of it becoming a no brainer as a programming language, and its simplicity, flexibility and large library contributes to it.
The business sector is a field which embraces technology. The sector is constantly in flux because one needs to make the most of novel opportunities to finish first. In such a competitive sector, certain skills are high in demand due to their wide range of applications, and SQL is one of them. And out of the various SQL Developers available, Oracle offers a powerful yet versatile platform suitable for SQL programming.
Structured Query Language - A Widely Accepted Programming Language!
SQL is called so because it uses queries as part of its code. Queries are commands used to manipulate data. Another salient feature of SQL is that it is a structured language, and all data is assumed in a tabular format. These features make it the perfect tool for business purposes, and it has found rising popularity in the same.
SQL Developer is the platform on which you can code, compile and execute various programs in SQL.
Oracle SQL Developer - A Robust Platform
Oracle SQL Developer is an IDE for programming in SQL on Oracle Databases. It is made available by the Oracle Corporation for free and is one of the most popular Relational Database Management Systems today.
Even though all such platforms use SQL, many differences make some better than the other. SQL itself comes in different types, so these programs are diverse in their capabilities. This is because different platforms use different forms of SQL and follow different protocols and mechanisms.
Oracle and PL/SQL - A Powerful Integration
Oracle SQL Developer uses a version of SQL called Procedural Language or PL/SQL, whereas most others, especially Microsoft SQL Server uses T-SQL. This itself gives Oracle an edge over its counterparts, as PL/SQL has many advantages over other formats.
For starters, PL/SQL is different from T-SQL in its syntax as well as capabilities, since they both handle variables, stored procedures and built-in functions differently. PL/SQL can also create packages of grouped procedures, unlike T-SQL.
This also makes it easier to convert applications to a different database without posing many challenges in editing or reworking the code. PL/SQL also has much more DBMS system packages than T-SQL and is better at error exception handling.
Organizing Database objects in Oracle - Highly Structured
Another major feature of Oracle which makes it more desirable is the way Database objects are organized in Oracle. Oracle has a subgroup of collection of database objects under a schema. There are many such Schemas, and they are all shared with the users. The sharing is universal but can be regulated through permissions.
Oracle works across all platforms and operating systems. This makes it a viable option for enterprises running on custom operating systems or freeware.
Transactions are a group of tasks that must be treated as a single unit. These are executed differently in different platforms, and Oracle is more in control of its transactions. Oracle treats each new database connection as a new transaction. As each query is executed, the changes made are only in memory until an explicit statement is given. Upon issuing the COMMIT statement explicitly, the changes are permanently made. This offers great flexibility as you can easily roll back changes and correct errors.
Oracle also has a wider variety of options to choose from in DBMS packages. Other options like Microsoft SQL Server does not contain provisions to declare some object types like public and private synonyms, independent sequence objects and so on. Therefore, Oracle is a more comprehensive option as it covers all the bases.
A cluster of servers refers to a connected group of physically separate servers that act in harmony and are perceived as a single system by networks. This helps in up-scaling by increasing computing power. Oracle can take advantage of Clustered systems, unlike their peers. With the new parallel servers in Oracle, you can place any application on a cluster without affecting the application, and it can be up-scaled by adding another server. This puts it miles ahead of its competing platforms.
Working with computers on anything can be quite a hassle. Even in simple cases such as writing this article, one mistake can cause you to lose all your progress. Therefore, applications have to be reliable in all aspects.
Oracle has many features that ensure a smooth work-flow and contains safeguards against any unexpected issues. It allows you to mirror transaction log files, which show exactly what programs were executed and when. It also prevents crashes occurring as a result of less space on the hard disk, and saves the server from downtimes and rebooting. This makes Oracle a safe option when compared to other DBMS platforms.
Oracle: A Powerhouse
The above-mentioned features show exactly why Oracle is a more desirable option over its competitors. As a freeware, it is readily available. It also has a wider assortment of DBMS packages and options to choose from and is much more flexible. SQL Programming in Oracle works with every platform and OS. It is also more reliable and versatile as a platform, and the Clustering feature alone puts it at the top of the table.
Python is now regarded as a must-have skill for most of the data analytics and data science job roles. And even though its seeming popularity is not the primary reason for it, it seems to be a major contributing factor. Due to its popularity, Python exercises, Python programming proficiency, and Python interview questions form an important aspect of getting a job in the data analytics sector. Almost every recruiter looks for Python as a necessary skill, instead of just one that gives the recruit brownie points. And this is because of how widely accepted it is as a programming language.
Python-based websites and apps are the frontrunners of their sector, and this is a list of some of those.
The tech giant Google which has evolved into a household name even has a saying about it, “Python where we can, C++ where we must.” Quite a bit of Google infrastructure is built using Python, especially YouTube. The largest video sharing platform in the world uses Python for almost everything, most notably their targeted advertisements and suggestions.
Instagram is another popular site that is almost entirely built on Python. The social media platform revolutionized the sharing of pictures on such levels that Google was practically throwing money at it for acquiring it. This all started as a simple website with a Django backend running on just one server. Django is an open-source developer used by Instagram to this day, and it runs on, you guessed it, Python.
The front page of the internet is a massive online society that, if you know, you know. For the uninitiated, Reddit is a place where you can find a community (called a subreddit) or everything. If you don’t know about this site, chances are you’re living under a virtual pile of rocks. This website is also reliant on Python, and cannot survive without its simplicity, and endless libraries.
For those of who are done with seeing examples of social media, here is a breath of fresh air. IBM, which has been and still is a big name in the IT industry uses python for many things, most notably using a Python SDK for IBM’s big data and AI service called Watson, and a free Python tutorial that they have released. And when a company that has been a huge contributor to the tech environment uses Python, that’s a tell-tale sign of Python being capable.
Spotify is the music streaming service that has revolutionized the Music Industry as we know it. Millions of users trust the platform to not only listen to their favorite songs but also put them on new ones with their incredibly personalized and accurate suggestion feature. Spotify uses Python for many backend functions and Analytics, which means that Python is responsible for the suggestion algorithms Spotify is popular for. Netflix, the global Media streaming service also follows in Spotify’s footsteps by using Python for a similar purpose. When you see these two giants using the same language for their exceedingly well-reputed work, that is a testament to the relevance and capability of the language.
Dropbox is a popular online data storage service that makes use of cloud computing to safely store your data. It is one of the most widely used platforms for this purpose across all operating systems, both personal and enterprise-related and has a total value of over $8 billion. Dropbox also uses Python for various purposes, most notably its well-sculpted Desktop version.
Uber has disrupted Taxi services and has brought this part of the transportation sector into the Cyberspace. They use Python as their go-to programming language, helping them with their Analytics and algorithms.
Python is widely used for a plethora of applications in various sectors, from social media to various services. This is even though it is not the fastest computer language out there. Even though computers are preferred for their speed, Python, despite being slower, is used more than faster languages such as C. And the reason behind this trend, simply put, can be summed up as follows.
Python is easy, reliable, and manageable. The script itself is easier to understand, and this makes coding and maintenance of the program easier. And even though it is slower, Python still gets the job done. Since most of these programs are run on a huge scale, the difference of milliseconds or seconds in speed does not matter.
Also, being a glue language makes Python more flexible and easier to write. This means that you can write part of the program in one language, and simply attach that part to the Python language. This is an important feature, as many other languages possess features better suited for certain applications, and they can all be executed in Python.
Taking Advantage Of The Python-philia
When a language is in such a coveted position, learning it can give you an edge over other candidates that do not possess the level of skill as you. Practicing Python exercises online and solving Python Practice sets gives you a grasp over the language, that can put you ahead. Python is overwhelmingly popular, which means knowing it makes you a favorite with the recruiters. In addition to the bigwigs in the Cyberspace, almost all websites and apps are initially written in Python. The ease of handling and its flexibility as a glue language has turned it into a Universal language for Coding.
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