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Top 10 Best Programming Languages must Learn in 2023


Programming Languages List – Table of Content

However, you need to invest more, and mastering any programming language will take some time to master any programming language. So, you have to make the right choice for a better future. Also, while choosing the best programming language, you must check the difficulty level, system requirements, and other aspects. Selecting the best one from the list of programming languages might be challenging.

Therefore, we have compiled a programming languages list to learn in 2023 and beyond to make it easy. Before diving into this blog, you should briefly know about programming language.

What is a Programming Language?

A programming language is a computer language that a programmer or developer uses to communicate with the computer. Moreover, a programming language is a set of instructions and rules written in any specific language to execute a particular task. Developers primarily use programming or coding language to develop desktop, mobile, and web apps. Further, many others, like web designers, video game developers, computer system engineers, etc., use programming languages.

We cannot develop and use any application or website without a programming language. So, programming language plays a vital role in developing various apps. Further, there are two types of programming languages- Low Level and High-Level languages.

Low-Level Programming Languages

A low-level programming language is machine-dependent in which a processor can run these programs much faster. It doesn’t need a compiler or interpreter. This low-level language is further divided into two types as-

  • Machine Language
  • Assembly Language

High-Level Programming Languages

A high-level programming language helps developers to build user-friendly websites, software programs, etc. These languages require a compiler to translate the program into machine code. Further, high-level coding languages like Python, Java, C, C++, Cobol, Perl, Lisp, Swift, etc., are easy to understand and write. Also, we can easily maintain them.

The high-level programming languages are further classified into different parts, such as;-

  • Oops Language
  • Procedural-oriented programming language (POP)
  • Natural Language

Best programming languages to learn

Most aspirants are confused about what coding language they should learn for a better career. In this blog, we have compiled the best programming languages for beginners and professionals. So, anyone interested in advancing their career can start learning these programming languages.

  • Java
  • Python
  • JavaScript
  • C and C++
  • R
  • Swift
  • GoLang
  • HTML
  • Ruby
  • Kotlin

Java

Java is a high-level object-oriented programming language that tops the list of programming languages. It is one of the most widely used coding languages today. Sun Microsystems developed it. Oracle later acquired it. Java language is handy for creating various desktop, mobile, and enterprise-level apps and is compatible with any device. Further, it is compatible with any platform, such as Windows, Linux, iOS, Android, etc.

Java programming language is open-source and has a vast community of developers. They contribute well towards its growth. They also support Java users worldwide. Moreover, Java is much popular language because of its features like:-

  • Platform independence
  • Versatility
  • User-friendly interface
  • Portability across multiple platforms
  • Oops language

Today, Java developers with extensive skills are in high demand worldwide. An expert Java Developer in India can earn an annual salary between Rs. 2 to 9.5 lakhs p.a. The average annual salary is Rs. 4 LPA.

Moreover, Java language offers an easy learning curve to aspirants with basic coding knowledge. Thus, Java is considered the best programming language for beginners. Many large-scale companies use Java, including popular websites and apps like Google, Amazon, YouTube, Netflix, Uber, etc. All these features make Java the best programming language.

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Python

Python is the most popular, widely used, and the best programming language for beginners. Moreover, Python is an open-source coding language with excellent community support. It is famous for its readability because it uses simple syntax. Further, Python language is easy to integrate with multiple web services and helps to build GUI-based desktop apps quickly. Also, many Machine Learning and Deep Learning apps use Python language.

Python is compatible with web and desktop platforms and offers an easy learning curve for beginners. Besides the above, Python is extensively used to develop video games, 3D animations, and popular websites like Quora, YouTube, Instagram, etc.

Companies like Intel, IBM, Netflix, Facebook, etc., also use Python. However, it is popular because it is flexible, intuitive, highly readable, and scripting language.

The following features make it a more popular coding language:-

  • Highly flexible
  • Easy to read
  • Open-source availability
  • OOPs language
  • Extensible and easy to code language
  • Highly Portable

After learning Python, you can go for multiple job profiles like Python Developer, Web Developer, Data Scientist, Web Testing, etc. The annual salary that a Python Developer can earn ranges between Rs. 3 to 9 lakhs per year. The average annual salary stands at Rs. 4 lakhs p.a. Moreover, the popular companies that hire Python developers include Microsoft, IBM, Intel, Cisco, Facebook, Spotify, etc.

JavaScript

JavaScript is also one of the best programming languages, mainly used in the world wide web. It is a dynamically typed scripting language used in developing dynamic web pages. However, it is frequently used with HTML and CSS to create many front-end apps, which makes it easier to click, drag, and move any webpage element. Moreover, it is widely used on client-side apps, which gives websites more power.

For the aspirants of web development, JavaScript is an ideal choice to learn. It is a highly versatile helpful language for front-end and server-side programming. We can use it for mobile app development and games and to automate many mundane tasks. Also, JavaScript has a vast community that openly supports beginners worldwide and resolves issues.

Its benefits include:-

  • User-friendly interface
  • Easy for beginners
  • Supports cross-platform
  • Can be used across the web
  • Best for mobile app development, etc.

A skilled JavaScript Developer makes an annual average salary of Rs. 6.3 lakhs per year. The yearly salary of JavaScript Developers ranges between Rs. 2 to 12 lakhs per year. Globally popular companies like Facebook, Google, LinkedIn, PayPal, Instagram, Walmart, etc., use JavaScript.

C and C++

C is the oldest programming language and is a base for many other programming languages like Java, C#, JavaScript, and more. On the other side, C++ is an advanced version of C language. However, these are widely used programming and high-performance languages that support app development.

Moreover, both are the best programming languages to learn, which require basic problem-solving skills, basic computer operations, etc. The following are the best features of C & C++ languages:-

C Language

  • Simple and fast
  • Highly efficient
  • Portable
  • Rich in libraries
  • Good memory management
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C++ Language

  • OOP language
  • Platform Independent
  • Simple to use
  • Supports dynamic memory allocation
  • Exception handling

A skilled C Developer earns an annual salary between Rs 1.7 to 12 lakhs p.a. with an average salary of Rs 4.5 lakhs p.a. Similarly, a C++ developer earns around Rs. 10 lakhs per year as an average salary. The top companies using C/C++ languages include Apple, Mozilla, Adobe, Bloomberg, and Microsoft.

R Programming

Among the best programming languages to learn, R is one of the most popular open-source coding languages. It is a modern language with the functions like statistical computing, testing, analysis, data analysis, data visualizations, data science, etc.

Moreover, R is a simplified coding language that helps us do modular programming using various functions. Also, it can easily integrate with the languages like C, C++, Python, .Net, etc. Further, R programming language is generally used by data analysts, marketers, researchers, etc. It helps to clean, analyze, visualize, and present data effectively.

Features of R programming include-

  • Simple and very effective
  • Useful for data analysis
  • Open-source
  • Highly extensible
  • Effective in data handling

An R language expert earns an average salary of Rs 5.19 lakhs annually.

Swift

Swift is a popular programming language developed by Apple company. It is in Linux and MacOS applications. It is an open-source coding language, one of the top programming languages to learn. To learn Swift language, basic coding skills are necessary. Further, it is popularly used in iOS apps such as WordPress, Firefox, etc.

Features and benefits of Swift language include-

  • Easier to read, write, and maintain
  • Very fast and have concise code
  • Highly secured language
  • Supports many dynamic libraries
  • Open-source availability

An iOS Swift Developer’s salary in India ranges between Rs. 2 to 11 lakhs p.a. with an average annual salary of Rs. 4 LPA. Many top companies like Uber, CRED, Lyft, Glovo, Asana, etc., use Swift language.

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GoLang

Go is one of the popular and best programming languages developed by Google, especially for web apps. It is a compiled language and is efficiently helpful for many large apps. Moreover, it is one of the fastest-growing coding languages that support cross-platforms, including desktops. Many giant companies in the IT sector use the Go language in their large-scale projects.

Further, it is the best programming language for beginners to learn basic skills. GoLang helps many popular global companies like Google, Netflix, PayPal, Trivago, YouTube, Walmart, and Twitter. There are multiple job offers available for Golang experts in the market.

Below are the various features of Golang:-

  • Simple Code
  • Supports Testing
  • Excellent performance
  • Easy to learn and maintain
  • Suitable for large-scale projects
  • Cross-platform support
  • Highly compatible and fast

A skilled Golang developer in India earns a salary between Rs. 2 to 24 lakhs per annum. The average annual salary is Rs. 7 LPA, as per AmbitionBox.

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HTML

HTML refers to Hyper Text Markup Language, primarily helpful in developing web pages and apps. Adding links, images, videos, and other content to the web page is also helpful. HTML is a simple programming language requiring only a few skills. It helps you to build a basic web page quickly. Those who are new to web development can start with HTML. Learning HTML can open doors for many job opportunities.

Further, HTML is valid for developing structured documents. HTML’s major benefit is building standard and well-designed web pages. You can easily maintain and update these web pages.

The following are the features of HTML:-

  • Platform independent
  • User-friendly interface
  • Flexible to design web pages
  • Simple to use
  • Easy to structure and present the web content
  • The latest version is HTML5, which offers multiple features like audio, video, multimedia, local storage, 3D Graphics, etc.
  • These features make it a powerful tool that provides an engaging web experience to its users.

An HTML Developer earns an annual salary of Rs. 1.1 to 6.5 lakhs per year. The annual average salary stands at Rs. 3 lakhs. This salary range will go higher with increasing experience.

Ruby

Ruby is another best programming languages, relatively simple to learn and developed in the mid-1990s. It is a dynamically typed, Oops language helpful for web development. Also, it helps to implement a popular web application and server-side framework, Ruby on Rails. It is written in the Ruby language. Beginners in the web development field can easily learn Ruby. There are many opportunities for Ruby developers in the market.

The following are the various features of Ruby:-

  • Highly flexible
  • Exception handling
  • Open-source language
  • Used for CGI scripts
  • Easy syntax
  • Supports GUI tools

A Ruby on Rails developer’s salary in India is between Rs. 2 to 12.5 Lakhs per year, with an average salary of 5 lakhs p.a.

Kotlin

Kotlin is also the best programming language to learn that runs on JavaVM. It is a statically typed language. Also, it is a general-purpose coding language that is beneficial for web, desktop, and server-side app development, including Android apps. It supports many Google apps today, and many global companies use Kotlin as their primary coding language.

If you have prior experience with programming languages like Java, it will be easier to learn Kotlin. Further, many Android app developers use this coding language popularly.

The following features of Kotlin present it as the best programming language:-

  • Easy language to adopt.
  • Highly compatible with Java.
  • Its code is very expressive and concise.
  • It offers less code with better readability.
  • Helps in developing many backend and web apps along with Android apps.

Moreover, Kotlin Developers are in good demand, where an expert with good skills earns around Rs. 4 lakhs per year on average.

Bottom Line
Thus, above are the top 10 programming languages listed among many other languages available today. The other best programming languages include PHP, CSS, Perl, SQL, Scala, Matlab, etc. These are also famous programming languages and developing web and mobile apps. So, learning any of these programming skills will benefit the aspirants looking forward to building a solid IT career.



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EDA in Machine learning| overview of EDA in Machine learning


EDA in Machine Learning – Table of Content

What is Exploratory Data Analysis (EDA)?

A method for summarizing data, identifying patterns and relationships, and detecting outliers is exploratory data analysis. This type of data analysis is most often used when the data set is large or complex, and it can help with data comprehension. There are numerous techniques for exploratory data analysis, but the most common include visual methods like plotting data on a graph and statistical methods like calculating summary statistics. Exploratory data analysis is an important step in data analysis that can be used on both qualitative and quantitative data.

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Steps Involved in Exploratory Data Analysis

Let us look into the various steps involved in Exploratory Data Analysis

Identifying the Data Source(s) and Data Collection

To understand the data, identify the data source(s) and the data collection process first. It is possible to use primary or secondary data sources. If the data comes from a primary source, it was gathered by the study’s researcher(s). If the data is from a secondary source, it was collected by someone other than the researcher(s) and made available for use.

Following the identification of the data source(s), the next step is to understand the data collection procedure. Understanding how the data was gathered and what biases, if any, may exist in the data is part of this. Researchers can interpret data more accurately if they understand the data collection process.

Machine Learning

Machine learning is a rapidly expanding data science field with enormous potential in exploratory data analysis (EDA). EDA has traditionally been performed manually by inspecting data sets for patterns and trends. Machine learning, on the other hand, enables us to automate this process and have computers do the work for us. There are several machine learning algorithms available for EDA, each with its own set of benefits and drawbacks. There are several popular machine learning algorithms and how they can be used to improve your EDA.

Exploratory Data Analysis(EDA)       

 Exploratory Data Analysis is a critical component involved while working with data. Exploratory data analysis is used to comprehensively understand the data and discover all of its characteristics, typically by employing visual techniques. This makes it possible for you to understand your data more thoroughly and find interesting patterns in it.

1. Load .csv files

 A CSV (comma-separated values) file is a type of text file that saves data in a table-structured format using a specific format.

 2. Dataset Information

You must first understand your dataset in order to perform an Exploratory Data Analysis (EDA). This includes understanding the dataset’s data type, what each column represents, and any other relevant information. This understanding is critical for properly performing an EDA because it will help you know what to look for and how to analyze the data.

 3. Data Cleaning/Wrangling

 To perform effective Exploratory Data Analysis (EDA), your data must first be cleaned and wrangled. The process of transforming raw data into a format suitable for analysis is known as data wrangling. This usually involves removing invalid or irrelevant data, dealing with missing values, and standardizing data types. You can begin EDA once your data is in good shape.

 4.Group by names

 One of the first steps in Exploratory Data Analysis is to group data by one or more variables (EDA). This helps us understand the relationships between the variables and identify any trends or patterns. There are several approaches to data grouping, but one of the most common is to group by name. The groupby() function in Pandas can be used to accomplish this. To group by name, we must first create a dataframe with columns for each variable. For this example, we’ll use the dataframe:

 | name | age | gender |

|——|—–|——–|

| John | 20 | Male | 

| Jane | 21 | Female | 

| Dave | 22 | Male | 

| Emily | 23 | Female |

 5.Summary of Statistics

 Your sample data is summarized and informed by summary statistics. It gives details about the values in your data set. Determine where the mean is and whether or not your data is skewed.

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 6 Dealing with Missing Values

 Missing data are values or variables that are not stored (or are not present) in the given dataset. Certain values may be missing from the data for a variety of reasons. The causes of missing data in a dataset influence how missing data is handled. As a result, it is critical to understand why the data may be missing.

 7.Skewness and kurtosis 

Skewness is a measure of the asymmetry of a distribution. Kurtosis is a summary statistic that conveys information about a distribution’s tails (the smallest and largest values). When graphical methods cannot be used to communicate data distribution information, both quantities can be used.

 8.Categorical variable Move

 A categorical variable (also known as a qualitative variable) in statistics is a variable with a limited (and usually fixed) number of possible values that assigns each individual or other unit of observation to a specific group or nominal category based on some qualitative property

9.Create Dummy Variables

 Dummy variables are used in statistical modeling to represent categorical variables. A categorical variable has only one of a few possible values, such as gender, race, or political affiliation. Dummy variables are frequently used in regression analysis to represent variables that are not linearly related to the dependent variable. Creating dummy variables is a common data preparation step in exploratory data analysis. Simply create a new variable with a value of 1 if the original variable is equal to a certain value and a value of 0 otherwise to create a dummy variable.

10.Removing Columns 

During the early stages of Exploratory Data Analysis, it is frequently advantageous to remove columns from your dataset (EDA). This can be done for a number of reasons, including shrinking your dataset or removing columns that are no longer relevant to your analysis. There are several methods for removing columns from a dataset, and which one you use depends on your specific situation. This article will demonstrate three methods for removing columns from a dataset: drop(), column indexes(), and remove columns (). Once you’ve learned how to remove columns from a dataset, you’ll be able to easily manipulate your data.

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11.Univariate Analysis

You examine data from only one variable in Univariate Analysis. In your dataset, a variable refers to a single feature/column. This can be accomplished visually or non-visually by locating specific numerical values in the data. Visual techniques include:

Histograms are bar plots that display the frequency of data using rectangle bars.

Box plots: Information is represented by boxes in this plot.

12. Bivariate Analysis

Bivariate Analysis compares two variables. This enables you to see how one feature affects another. It is accomplished through the use of scatter plots, which depict individual data points, or correlation matrices, which depict the correlation in hues. Boxplots are another possibility.

13.Multivariate Analysis

The term “multi” refers to “many,” and “variate” refers to “variable.” Multivariate analysis is a statistical procedure for analyzing data that contains more than two variables. This method can also be used to investigate the relationship between dependent and independent variables to perform exploratory Data Analysis.

14.Distributions of the variables/features

Understanding the distributions of the variables/features in your dataset is critical for exploratory data analysis. This will help you understand the data better and identify any outliers or unusual behavior. The histogram is a popular method for visualizing distributions. A histogram shows how frequently each value appears in a dataset. It’s a handy tool for determining the distribution of a numerical variable.

15.Correlation

A correlation matrix is used to investigate the relationship between various variables. The correlation coefficient determines the degree to which two variables are linked. The following table depicts the relationship between salary, age, and balance. Correlation describes the relationship between two variables. This allows us to see how changes in one variable affect changes in the others.

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Conclusion

Machine learning is a rapidly growing field with a wide range of practical applications. Before developing effective machine learning models, it is critical to first understand the data. Exploratory data analysis (EDA) is an important step in the machine learning process. EDA helps us understand the data better and identify patterns and trends that may be hidden within it.EDA can also be used to identify potential data issues. Overall, EDA is an important part of the machine learning process. By better understanding the data, we can build better machine learning models that are more likely to produce accurate results.

 

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