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A Step-by-Step Guide on Python Variables

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A Step-by-Step Guide on Python Variables

Variable is the name given to the memory location where data is stored. Once a variable is stored, space is allocated in memory. Variables are named locations that are used to store references to the object stored in memory.

With the rapid rise of the advanced programming techniques, matching with the pacing advancements of Machine Learning and Artificial Intelligence, the need for Python for Data Analysis an Machine Learning Using Python is growing. However, when it comes to trustworthy courses, it is better to go for the best Python Certification Training in Delhi.

Now, coming to this article, here are some of the topics that will be covered in this article:

  • Rules to Define a Variable
  • Assigning Values to a Variable
  • Re-declaring a Variable in Python
  • Variable Scope
  • Deleting a Variable

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Rules to Define a Variable

These are the few rules to define a python variable:

  1. Python variable name can contain small case letters (a-z), upper case letters (A-Z), numbers (0-9), and underscore (_).
  2. A variable name can’t start with a number.
  3. We can’t use reserved keywords as a variable name.
  4. The variable name can be of any length.
  5. Python variable can’t contain only digits.
  6. The variable names are case sensitive.

Assigning Values to a Variable

There is no need for an explicit declaration to reserve memory. The assignment is done using the equal to (=) operator.

Multiple Assignment in Python

Multiple variables can be assigned to the same variable.

Multi-value Assignment in Python

Multiple variables can be assigned to multiple objects.

Re-declaring a Variable in Python

After declaring a variable, one can again declare it and assign a new value to it. Python interpreter discards the old value and only considers the new value. The type of the new value can be different than the type of the old value.

Variable Scope

A variable scope defines the area of accessibility of the variable in the program. A Python variable has two scopes:

  1. Local Scope
  2. Global Scope

Python Local Variable

When a variable is defined inside a function or a class, then it’s accessible only inside it. They are called local variables and their scope is only limited to that function or class boundary.

If we try to access a local variable outside its scope, we get an error that the variable is not defined.

Python Global Variable

When the variable is not inside a function or a class, it’s accessible from anywhere in the program. These variables are called global variables.

Deleting a Variable

One can delete variable using the command “del”.

In the example below, the variable “d” is deleted by using command Del and when it is further proceeded to print, we get an error “variable name is not defined” which means the variable is already deleted.

Conclusion

In this article we have learned the concepts of Python variables which are used in every program. We also learned the rules associated to the naming of a variable, assigning value to a variable, scope of a variable and deleting a variable.

So, if you are also hooked into Python and looking for the best courses, Python course in Gurgaon is certainly a gem of a course!



This technical blog is sourced from: www.askpython.com and intellipaat.com


 

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How to Structure Python Programs? An Extensive Guide

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How to Structure Python Programs? An Extensive Guide

Python is an extremely readable and versatile high-level programming language. It supports both Object-oriented programming as well as Functional programming. It is generally referred to as an interpreted language which means that each line of code is executed one by one and if the interpreter finds an error it stops proceeding further and gives an error message to the user. This makes Python a widely regarded language, fueling Machine Learning Using Python, Text Mining with Python course and more. Furthermore, with such a high-end programming language, Python for data analysis looks ahead for a bright future.

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In the Structure of Python

Computer languages have a structure just like human languages. Therefore, even in Python, we have comments, variables, literals, operators, delimiters, and keywords.

To understand the program structure of Python we will look at the following in this article: –

  1. Python Statement
    • Simple Statement
    • Compound Statement
  2. Multiple Statements Per Line
  3. Line Continuation
    • Implicit Line Continuation
    • Explicit Line Continuation
  4. Comments
  5. Whitespace
  6. Indentation
  7. Conclusion

Python Statement

A statement in Python is a logical instruction that the interpreter reads and executes. The interpreter executes statements sequentially, one by one. In Python, it could be an assignment statement or an expression. The statements are mostly written in such a style so that each statement occupies a single line.

Simple Statements

A simple statement is one that contains no other statements. Therefore, it lies entirely within a logical line. An assignment is a simple statement that assigns values to variables, unlike in some other languages; an assignment in Python is a statement and can never be part of an expression.

Compound Statement

A compound statement contains one or more other statements and controls their execution. A compound statement has one or more clauses, aligned at the same indentation. Each clause has a header starting with a keyword and ending with a colon (:), followed by a body, which is a sequence of one or more statements. When the body contains multiple statements, also known as blocks, these statements should be placed on separate logical lines after the header line, indented four spaces rightward.

Multiple Statements per Line

Although it is not considered good practice multiple statements can be written in a single line in Python. It is advisable to avoid multiple statements in a single line. But, if it is necessary, then it can be written with the help of semicolon (;) as the terminator of every statement.

Line Continuation

In Python there might be some cases when a single statement is too long that does not fit the browser window and one needs to scroll the screen left or right. This can be a case of assignment statement with many terms or defining a lengthy nested list. These long statements of code are generally considered a poor practice.

To maintain readability, it is advisable to split the long statement into parts across several lines. In Python code, a statement can be continued from one line to the next in two different ways: implicit and explicit line continuation.

Implicit Line Continuation

This is the more straightforward technique for line continuation. In implicit line continuation, one can split a statement using either of parentheses ( ), brackets [ ] and braces { }. Here, one needs to enclose the target statement using the mentioned construct.

Explicit Line Continuation

In cases where implicit line continuation is not readily available or practicable, there is another option. This is referred to as an explicit line continuation or explicit line joining. Here, one can right away use the line continuation character (\) to split a statement into multiple lines.

Comments

A comment is text that doesn’t affect the outcome of a code; it is just a piece of text to let someone know what you have done in a program or what is being done in a block of code. This is especially helpful when a code is written and someone is analyzing it for bug fixing or making a change in logic, by reading a comment one can understand the purpose of code much faster than by just going through the actual code.

There are two types of comments in Python.
1. Single line comment
2. Multiple line comment

Single line comment

In python, one can use # special character to start the comment.

Multi-line comment

To have a multi-line comment in Python, one can use Triple Double Quotation at the beginning and the end of the comment.

Whitespace

One can improve the readability of the code with the use of whitespaces. Whitespaces are necessary for separating the keywords from the variables or other keywords. Whitespace is mostly ignored by the Python interpreter.

Indentation

Most of the programming languages provide indentation for better code formatting and don’t enforce to have it. However, in Python, it is mandatory to obey the indentation rules. Typically, we indent each line by four spaces (or by the same amount) in a block of code. Also for creating compound statements, the indentation will be of utmost necessity.

Conclusion

So, this article was all about how to structure the Python program. Here, one can learn what constitutes a valid Python statement and how to use implicit and explicit line continuation to write a statement that spans multiple lines. Furthermore, one can also learn about commenting Python code, and about the use of whitespace and indentation to enhance the overall readability.

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Alteryx is Inclined to Make Things Easy

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Alteryx is Inclined to Make Things Easy

Alteryx Analytics is primarily looking to ease the usability of the platform in all of the updates that are yet to come. The esteemed data analytics platform is concentrating on reducing the complexities to attract more users and thus, widen their age-old user base beyond that of the data scientists and data analytics professionals.

Alteryx is headquartered in Irvine, California. It was founded as SRC LLC in 1997 and comes with a suite of four tools to help the world of data scientists and data analysts to manage and interpret data easily. Alteryx Connect, Alteryx Designer, Alteryx Promote and Alteryx Server are the main components of the analytics platform of Alteryx. Thus, it is worth mentioning that the Alteryx Certification Course is a must if you are looking to make a career out of data science/data analytics.

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A Quick Glance at the Recent Updates 

The reputed firm launched a recent version of Alteryx 2019.3, in October, and is likely to release the Alteryx 2019.4 as a successor to it. The latter is scheduled for a December release.

What’s in the Update?

Talking about the all-new version Alteryx 2019.3, Ashley Kramer, senior vice president of product management at Alteryx, said that the latest version promises 25 new and upgraded features, all of them focussing on the user-friendliness of the platform at large.

One of the prominent features of the new version is a significant decrease in the total number of clicks that a user will take to arrive at the option of visualizing data to make analytic decisions.

Data profiling helps the users to visualize the data while they are working with it. Here, Alteryx discovered a painless way to work with data by modeling the bottom of the screen in a format similar to that of MS Excel.

All of these changes and additions are done keeping in mind the features that the “customers had been asking for,” according to Kramer.

Now, with the December update, which will come with an enhanced mapping tool, the Alteryx analytics will strive to further lower the difficulties surrounding the platform.

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If you are interested in knowing all the latest features, it is better to join one of the finest AlterYX Training institutes in Delhi NCR, with exhaustive Analytics Courses in Delhi NCR, along with other demanding courses like Python for Data Analysis, R programming courses in Gurgaon, matchless course of Big Data, Data Analytics and more.

 
The blog has been sourced from ― searchbusinessanalytics.techtarget.com/news/252474294/Alteryx-analytics-platform-focuses-on-ease-of-use
 

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An All-Inclusive Guide on Python and its Changing Trends

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An All-Inclusive Guide on Python and its Changing Trends

Python is an extremely readable and versatile high-level programming language. Many companies such as Google, YouTube, Dropbox use the language for developing applications. It also finds its use extensively in diverse fields as in Python for data analysis, Machine Learning Using Python, Natural Language Processing, Web Development, Scientific Computing, Image processing, Robotics, Computer Vision and many more.

It supports both Object-oriented programming and Functional programming. Python is generally referred to as an interpreted language which implies that each line of code is executed one by one and if the interpreter finds an error, it stops immediately with an error message on the screen.

Another important feature of Python is its interactive prompt. A Python statement can be typed and immediately executed, which is in sharp contradiction to any other compiled language.

What are Python 2.x and Python 3.x?

There are two main versions of Python: Python 2.x and Python 3.x. If someone is new to Python, then he/she might be in confusion about which version to use. However, in the current scenario, we can easily migrate from Python 2 to Python 3, as the Python Software Foundation has finally taken the step to formally announce that Python 2 will reach the end of life (EOL) on January 1st, 2020.

Key differences between Python 2.x and Python 3.x

This article discusses the differences between these two versions of Python, making Python 3 less confusing for a new programmer.

  1. Print Function

In Python 2, print is a statement. There is no need of parenthesis.

In Python 3, print is a function. It needs parenthesis.

  1. Integer Division

In Python 2, if the division operator is performed on two integers, then the output will be an integer for example: – 7/3 = 2.

In Python 3, if the division operator is performed on two integers, then the output will be accurate. It can also be in float for example: – 7/3 = 2.33.

To get the result in an integer only a different division operator is used that is (//) it returns an integer result for example, – 7//3 = 2.

 3. Unicode Support

Both the versions of Python can handle strings (sequences of characters) differently.

Python 2 uses the ASCII encoding standard by default. ASCII is limited to representing 256 characters. This limits the flexibility of Python to encode the characters, particularly non-standard ones. Using Unicode in Python 2 requires extra syntax—for example when using print, the input text is to be wrapped in the Unicode() function to handle special characters.

In Python 3, Unicode is the default. The Unicode standard is much more versatile—it supports over 128,000 characters. There is no need for an extra syntax to define the Unicode values—they get printed automatically as utf-8 strings.

  1. Range Function

In Python 2, the range function returns a list of numbers.

In Python 2, the xrange class represents an iterable that provides the same object.

 In Python 3, original range function is removed and xrange is renamed to range:

In Python 3, it is needed to convert the range object to a list if someone desires the same result as the range function provides in Python 2.

  1. ­­­­Input() Method

Mainly what is expected from the input() method is that it reads input as string, then it can be converted into any datatype as per the requirement.

In Python 2, it has both the input() and raw_input() methods for taking input. The difference between the raw_input() and input()is that the raw_input() reads input as a string while the input() reads input as string only if it is inside quotes else reads as an integer.

In Python 3, there is no raw_input() method. The raw_input() method is replaced by input() in python 3. 

If someone still wants to use the input() method like in python 2, then it can be availed by using eval() method.

There are many other differences between Python 2 and Python 3 like: –

  1. Next() Method

In Python 2, .next() method is used and in Python 3 next() function is used to iterate the next element of an iterator.

  1. Raising Exception

To raise an exception in Python 3, the argument should be in parenthesis, while in Python 2, it is not necessary.

  1. Handling Exception

Handling exception is also changed in Python 3, “as” keyword is used in Python 3, while it is not necessary in Python 2.

So, if someone is a beginner, then it is strongly recommended to use Python 3 because it is the future of Python and also January 1, 2020, will be the last day of Python 2. It means that no improvement will be done anymore after that day, even if someone finds a security problem in it.

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It is highly recommended to upgrade the version of the programming language to Python 3. Some ways can help the Python 2 users in porting their code from Python 2 to Python 3 and get the feel of Python 3 and figure out how it is different from Python 2. The code can be imported by using tools like “Futurize” and “Modernize”. Also, if someone wants to check the availability of Python 3 as part of his tests, then “caniusepython3.check()” can be used.

As a final note, everyone must look for upgrading their Python version to Python 3 to understand the subtleties of the new version and usher in the future. However, if you are interested in Deep learning for computer vision with Python and similar courses, then opt for the premium Python training institute in Delhi now!


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R Vs Python: A Debate Forever

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R Vs Python: A Debate Forever

In this blog, we will bring forth the age old question and check which one is better, R programming and Python programming, when it comes to data science?

To be very honest, this question does not have a strict answer to it. However, in this blog we will lay down the key components of both the languages to give you a clearer picture. In the end, please decide for yourself and leave your comments in the section below.

The aim of this blog is to objectively put forward the pros and cons of both languages strictly from the perspective of data science.

We will discuss only about three main components, which are as follows:

  • Syntax
  • Performance
  • Applicability

There are other metrics, such as, trends in Industries and adaptation in the recent years which are beyond the scope of this blog. However, you can safely declare Python as the clear winner if those perspectives were concerned.

So let’s get started:

Syntax

Both R and Python are object-oriented languages. This is to say that everything is created as an object in which the information is mapped with the idea of using that object later in the analysis. However, when it comes to the syntax, i.e., the grammar of programming, R and Python are indeed very different.

R Programming

R programing is more suited to more seasoned coders who have prior experience of coding. The syntax is actually very similar to that of the previous languages, such as C, or C++ or Java and so on. The fundamental rules are that of C programming language. Also, use of semicolons is deemed optional in R. However, semicolons are necessary for multiple lines in a code inside a code block.

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Python

Python on the other hand, is the language more adaptable to the new generation of programmers. You can come from a non-programming background and still learn Python with relative ease.

Python is one of the most user friendly languages for the beginners. The syntax is designed to prioritize readability over preciseness of the code. In layman’s terms – coding in Python is very close to reading and writing with hand. In this regard, it is really popular amongst beginners in Data Science.

Performance

The performance is essentially measured by speed essentially when it comes to programming.

R Programming

As far as the general consensus goes R programming is much slower in terms of speed. The reason behind this is that R programming was initially designed to be used by statisticians for data analysis. Thus, R programming stresses more on precision than the speed.

Python

Python on the other hand, is relatively faster than R. Python offers the same level of precision whilst acting on a faster speed.

Note – The speed is taken into account independent of packages and libraries.

Applicability

Lastly, we will discuss the popular domains in which these languages are used.

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R Programming

As mentioned above, R was developed specifically for statisticians. For this reason, R is mainly used in various research organizations and academia in general. However, R is now quickly being absorbed in the enterprises as well, mainly because of its popularity and the availability of a large number of packages for statistical computation.

Python

Python is a gene

As Python is a general-purpose programming language we can use to build different kinds of applications. We can use Python to build web applications using popular frameworks like Django or Flask.

Lately, Python is becoming popular amongst data scientists as the language of choice given the simplicity of syntax, high speed and performance it has to offer. There has been a trend which has seen a sharp rise in the adaptability of Python over R in the last few years in Data Science.

So, there you have it folks. Decide for yourself now! We will meet you soon in the next blog.

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Statistical Application in R & Python: Poisson Distribution

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Statistical Application in R & Python: Poisson Distribution

Continuing with the series of blogs, the first of which was Statistical Application In R & Python: Normal Probability Distribution, here we bring you a post on how you can calculate Poisson distribution effortless using R & Python. So, stay tuned!

Poisson distribution is a counting process which is a discrete probabilistic model. It has only one parameter, (lambda or “m”) which is essentially the average rate of change. Poisson distribution is used to model “number of anything”. The probability distribution function of a Poisson distribution is given by the below expression.

If m is the mean occurrence per interval, then the probability of having x occurrence with in a given interval is:

Application:

A business firm receives on an average 6.5 telephone calls per day during the time period 11:00 – 11:15 A.M., Find the probability that on a certain day, the firm receives exactly9 calls during the same period.

The random variable x is the ‘number of telephone calls received during the period 11:00 – 11:15 A.M, since x is assumed to Poisson distribution. The parameter m is equal to the mean of the distribution; i.e.  m = 6.5 and x = 9, then the equation is:

Calculate Poisson Distribution in R:

So, while calculating Poisson distribution in R, we notice that the probability of occurring exactly 9 calls instead of average 6.5 calls in a given particular time (11:00 A.M – 11:15 A.M ) = 85.81%

Calculate Poisson Distribution in Python:

So, while we calculate Poisson distribution in Python, we notice that the probability of occurring exactly 9 calls instead of average 6.5 calls in a given particular time (11:00 A.M – 11:15 A.M) = 85.81%

Conclusion:

Companies can use the Poisson distribution to contrive effective steps to improve their operational efficiency. For instance, an analysis done with the Poisson distribution might reveal how a company can arrange staffing in order to be able to handle the peak periods efficiently, when the customer service calls keep on pouring.

In this problem we see that the business firm receives on an average 6.5 telephone calls per day during the time period 11:00A.M – 11:15A.M, then the probability of the firm receives exactly 9 calls in a same is 85.81%.

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A Nifty Guide to Initiate AIOps in 2019

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A Nifty Guide to Initiate AIOps in 2019

AIOps (artificial intelligence for IT operations) is the buzz word of the 21st century.

In this digitally-charged world, AIOps platforms are the key. They fuse ML and big data functionalities to boost and partly replace primary IT operations’ programs, including event correlation and analysis, performance monitoring and IT service automation and management.

In simple terms, AIOps is the combined application of data science and machine learning to help mitigate IT operations-related challenges and find faster insights. It fixes high-severity outages in a jiffy. 

The main objective of revolutionary AIOps platforms is to ingest and analyze the aggravating volume, variety and velocity of data and deliver it in a useful manner.

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IT bigwigs are excited about the prospects of applying AI and ML to IT operations.

Gartner expects that big enterprises’ usage of AIOps and other monitoring tools and applications will rise from 5% in 2018 to 30% in 2023. The long-term impact of AIOps on IT operations is predicted to be transformative.

Fortunately, AI capabilities are making headway, and more real-time solutions are being formulated and made available each day.

Read on to know how to get started with AIOPs:

Be prepared

First and foremost, you have to familiarize yourself with all the ML and AI capabilities and vocabulary. It doesn’t matter if you are gearing up for an AIOps project or not. Capabilities and priorities change; so be ready to implement the platform anytime soon.

Select the first few test cases carefully

Small and steady wins the race. The same phrase applies to transformation initiatives. They start small, seize knowledge and iterate from there. Imbibe the same approach for AIOps success.

Enhance your proficiency

Decode the intricacies of AIOps amongst your colleagues by displaying simple techniques. Ascertain your skills and identify the loopholes, then devise a relevant plan to fill up those gaps in-between.

Feel free to experiment

Although a majority of AIOps platforms are complex and costly, there is a substantial number of open-source and relatively low-cost ML software available in the market that lets you evaluate the efficacy of AIOps and ML applications and their uses.

Look beyond IT

Don’t forget to leverage all kinds of data analytics resources available in your organization. Data management is the cornerstone of AIOps. Most of the teams are already skilled in it. Statistical analytics and business analysis are key components of contemporary business frameworks, and many techniques traverse public domains. 

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Standardize and modernize, as and when required

Prepare your work infrastructure to implement a robust AIOps adoption by embracing secure automation architecture, immutable infrastructure patterns and infrastructure as code (IaC).

Interested in learning more about Machine Learning Using Python? Feel free to reach us at DexLab Analytics. We’re a premier learning platform specialized in offering in-demand skill training courses to the interested candidates.

 

The blog has been sourced from ― www.gartner.com/smarterwithgartner/how-to-get-started-with-aiops

 

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Retail 4.0: How Trending Technologies Are Influencing the Retail Industry?

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Retail 4.0: How Trending Technologies Are Influencing the Retail Industry?

The retail industry is undergoing unprecedented changes: courtesy Retail 4.0! It is the term used to denote the transformation that’s taking place at a rapid pace. Technological advancements and customer expectation are key driving factors behind the evolution.

Customers are the bedrock of the retail industry. They are fickle and demanding. With higher spending power and low brand loyalty, they are redefining the consumer trends and forcing retailers to harness the power of big data to ensure a seamless, positive customer experience coupled more secure payment methods and easier online store formats.

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Data is Power

For years, retailers have been working on consumer’s behavior and how to serve them well. Today, amidst increasing competition, data explosion and advanced technological implementations, they seem to lose their erstwhile charm. Data is the answer. In a digital-enabled landscape, retail industry players need to leverage several emerging technologies, such as augmented reality, virtual reality, mixed reality, AI and Internet of Things and draw clear actionable insights.

Gone are the days when retailers relied on their instincts and formulated marketing strategies. Today, predictive analytics is used to boost informed decision-making and conclude the future success of an enterprise. Put simply, retail analytics using Python is the tool to drive optimization, follow corrective measures and reduce revenue leakage. With data at the forefront, retail analytics and its diverse platforms are providing customers with relevant products, superior service and the facility to experience the products even before purchase.

How Does It Work?

Retail analytics targets customer acquisition and focuses on customer study. Through data analysis, the retailers ascertain buying patterns and curated customer engagement strategies. For that, deep insights are generated based on their search criteria, purchase records and frequency of shopping.

Also, retailers can now predict demand precisely. Based on a customer’s historical data, they anticipate when he/she is likely to make a purchase decision and within what duration of time. They can also predict the products the customers are going to re-purchase with the help of AI. Robust machine learning algorithms deliver insights that specify accurate customer recommendations, which help increase retailers’ profit margin.

Deep Learning and AI using Python

Understanding the nuances of consumer behavior is of utmost importance. This is why IoT and AI are combined and used in monitoring customer-store interactions – resulting in better service engagements and higher revenue. Social media has added to the effect. Extracting user information from social media platforms has become a piece of cake. Retail market players can now leverage the social media data, influence customer purchase decisions and enjoy a certain edge against the tailing rivals.

As endnotes, retailers need to embrace the digital transformation and create fresh, enhanced experiences to entice the consumers. After all, the future belongs to the data-inspired companies. So, just stay ahead of the curve using data as the power tool.

 

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Python vs. Scala: Which is Better for Data Analytics?

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Python vs. Scala: Which is Better for Data Analytics?

Data Science and Analytics seem to be synonymous to progress as far as the field of computer science is concerned. Now, with the rise of these technologies, everything goes down to the programming languages, which single-handedly help in the growth of them. 

This gave rise to Python, now known as the most significant language in the world of technology. Scala is another versatile language which is not unknown to the researchers and tech geeks. These two languages are the most talked about in the industry today. Nevertheless, both of them are extensively used in data analytics and data science. However, the debate regarding which one to opt for among the two has always been constant. But worry no longer because here we will discuss both of them, in brief, to help you with your choice!

Deep Learning and AI using Python

Python

Python is really one of the most popular languages in the industry. The open-source nature of the language makes it a popular choice for scripting and automation works. 

Besides, Python is powerful, effective, and easy to learn. Moreover, Neural Network Machine learning Python boasts of its efficient high-level data structures and for object-oriented programming.

Advantages

  • Easy to learn and effective too.
  • Exhaustive support from active communities.
  • Python enjoys built-in support for the datatypes.

Disadvantages

  • Your computer might slow down a little when you are running Python. This is in contrast to when you are running other languages like C or Java.

Scala

If you want an object-oriented, functional programming language, then Scala would certainly be your first choice. It was basically built for the Java Virtual Machine (JVM) and remains the most compatible programming language with Java code till date.

Advantages

  • Scala can utilise the majority of the JVM libraries, thus helping them to be embedded in the enterprise code.
  • It shares an array of readable syntax features of the popular languages, like Ruby.
  • Scala brags about numerous incredible features like string comparison advancements, pattern matching and its likes.

2

Disadvantages

  • Scala has a limited number of users in the communities, which encourages lesser interactions and stunted growth.
  • At times the type-information in Scala is really complex to comprehend. This difficulty can be attributed to the functional and object-oriented nature of the language.

We hope that this article helps you to have a brief insight into two of the most demanding programming languages: Python and Scala.

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