Programming Languages for Data Science and Analytics

The Best 

Programming languages

For Data Science and Analytics

We live in the information age, and every day, we generate tons of data. Making sense of that data has emerged as a lucrative pursuit for many businesses. To achieve this, industries across the board are turning to big data analytics and data science. Data science provides a means through which businesses can translate the vast amounts of data available to them into usable information through a scientific approach.

Data scientists have the requisite knowledge to apply statistical algorithms to make sense of large sets of data. These statistical algorithms are implemented in several well-known programming languages with a proven fortitude for working with sets of data that, in most instances, go well beyond a few gigabytes.

If you learn and master one of these 6 best programming languages for data science, you join a select number of professionals who command some of the highest salaries in the labor market. Moreover, the Harvard Business Review declared data science as the sexiest job of the 21st Century.

Best Programming Languages For Data Science

Let’s take a look at 6 of the best programming languages for data science you can learn today and kick-start a lucrative career in data science.

1. Python

In the battler of the best data science tools, Python leads the pack. The language is the mainstay for general programming tasks such as desktop and web application development. What makes Python an attractive choice for data scientists is its readability and productivity.

With Python, you have access to a range of data analytics libraries through the Python Package index such as the popular NumPy and SciPy modules. These two modules allow you to implement numerical routines on multi-dimensional arrays and matrices and perform computations of signals and images which are common tasks in data analysis. There are other numerous Python libraries that make data analysis simpler such as the Natural Language Toolkit (NLTK) that allows for statistical analysis of natural languages.

The sheer number of Python libraries dedicated to data science makes the language an obvious choice for beginners and professional data scientists.

2. R Programming

When Ross Ihaka and Robert Gentleman designed the R language, they did so with the goal of designing a better and user-friendly way of doing data analysis, statistical and visualization computation on large sets of data.

The language’s foundation in statistics and data visualization has seen it gain rapid popularity in commercial data analysis, and therefore an obvious choice for data scientists. For beginners, the learning curve for R is simplified by its active and helpful user community, extensive documentation, and a plethora of R functions that simplify complex data analysis routines.

3. MATLAB

Developed by Jack Little, Moler, and Steve Bangert, the founder of MathWorks, MATLAB has etched a name for itself in the world of technical computing. It is more than a programming language as it brings together computation, visualization, and programming into a single environment.

That makes MATLAB an excellent tool for data analysis, exploration, and visualization without the need for external libraries or modules. In fact, MATLAB has been the main data analysis tool for the academic community for the past few decades. Its proven track record makes it an excellent choice for the fledgling data scientist.

4. Java

As one of the oldest and most used languages in the world, Java is a must for aspiring data scientists. Chances are that the organization that hires you to work on a data science project already uses data in its infrastructure. That would mean your statistical models must be in Java for interoperability.

Moreover, there are popular Java frameworks dedicated to data analysis, machine learning, and artificial intelligence. These frameworks such as Apache Spark, Hadoop, and Hive are increasingly popular in the commercial space making Java one of the most in-demand languages for data scientists.

5. Julia

Julia is another programming language that was developed from the ground up for data science. The language is geared towards scientific computing, data mining, machine learning, and parallel computing.

That makes Julia one of the fastest languages for all tasks a data scientist would want to perform on large sets of data. In a nutshell, Julia addresses any shortcomings common with other programming languages not specifically designed for data science.

6. Scala

Scala's rise to prominence in the data science circles came after the release of Spark, a data processing engine written completely in Scala. While Spark allows for the intuitive collection, cleaning, processing, and visualization of data, code written in Scala executes faster.

That means you can analyze large sets of data faster compared to other languages. Additionally, writing Scala code is relatively easy due to its simple syntax and making it easy to maintain large repositories of Scala code.

Conclusion

Learning these 6 languages will jump-start your career in data science. While there is no specific order to this list of programming languages for data science, you may want to learn more than one language. This will give you the versatility and competence as a data scientist.

Technologies Field Create a Huge Job vacant for AI and Big Data Analytics or Data Science

In Technologies Field Create a Huge Job Approx 1.4 lakh jobs vacant for AI(Artificial Intelligence), Big Data Analytics or Data Science roles in India: 

Create a Huge job in market Approx lakh jobs are vacant in the Artificial Intelligence (AI) and Big Data Analytics segment across various sectors in India out of the total demand of 5.1 lakh employees, According to  Nasscom Foundation.

Approximately 1.4 lakh jobs are vacant in the Artificial Intelligence (AI) and Big Data Analytics segment across various sectors in India out of the total demand of 5.1 lakh employees, according to the report of the Nasscom.

Of the total demand, 3.7 lakh jobs are filled, it said. By 2021 the employee deficit would increase to 2.3 lakh, as the total demand goes up to around 8 lakh employees, as per the report. 

To meet the deficit of qualified and skilled employees, Nasscom o said it would involve universities and colleges along with other companies including Wipro and Tech Mahindra to provide relevant skills to around one million employees and another one million prospective employees and students.

Currently, Nasscom has a user base of more than 200,000 from member firms who have committed to re-skilling their employees, it said in a statement.

"The Future Skills program which was launched earlier this February ultimately aims to reskill one million professionals along with skilling one million potential employees and students in the industry over a period of five years," the statement said.

Around 10 Nasscom member companies would be the "pioneers", the industry body said, adding that the second phase would also involve universities and colleges.

"Some of the 'pioneers' include, Wipro and Tech Mahindra (IT Services), Cyient (Engineering Services), Genpact and WNS (Business Process Management), CGI (Global Capability Center), Purpletalk (Products), Dev-IT and Kellton (small and medium enterprises)," as per the statement.

Most Useful Technologies Demands in future(2020)

1. MACHINE LEARNING

Machine learning and artificial intelligence are one of the most innovative and exciting fields moving into the future, making it one of the most profitable skills you can learn. From Sirs and Alex to chatbots to predictive analysis of self-driving cars, there are a ton of uses for this futuristic tech.

Machine learning can be applied to every industry, including healthcare, education, finance, etc. Translation? The possibilities are endless, and you can apply your machine learning skills to a role that suits your personality and interests.

2. DATA ENGINEERING

Data engineering is separate from data science, but the former is what enables the latter to exist. Data engineers build the infrastructure and tools that data scientists rely on to conduct their own work.

3. NETWORK AND INFORMATION SECURITY

For any company that collects customer information or deals with sensitive data of their own, keeping networks secure is paramount.

When data breaches do happen, they can be big, newsworthy, and costly for the company to recover from. 2017 had its fair share of cybersecurity disasters, and companies famously hacked in the past include Sony, LinkedIn, Chipotle, and others.

4. Cloud engineer

As the vast majority of companies move important systems to the cloud, more and more are choosing a hybrid approach, with multiple vendors. In the coming years, cloud engineers will develop solutions at a scale that are a mix of both in-house technology and outside systems -- going beyond Amazon engineers working on AWS or Microsoft engineers working on Azure, Mukherjee said.

5. App Developer

Across both end-users and vendors, app developers will be in large demand in 2020, Meneer said. "It's really the intersection of where technical capability comes to face the business need," he added.

6. Database administrator

Database administrators will become more in-demand by 2020, particularly as companies move toward more software offerings that include AI, and the ability to create AI-powered models, Meneer said. "Having well-maintained databases is really the secret to allowing those products to work effectively," he added.

7. Artificial intelligence (AI)

the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasks—as, for example, discovering proofs for mathematical theorems or playing chess—with great proficiency. Still, despite continuing advances in computer

8. Internet of Things(IoT)

"The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.”