The open-source project behind Julia, a programming language for data scientists, has revealed which languages users would shift to if they decided no longer to use Julia.

Julia, a zippy programming language that has roots at MIT, has published the results of its 2020 annual user survey. The study aims to uncover the preferences of those who are building programs in the language. This year, the survey attracted 2,565 Julia users and developers, up from 1,844 participants in 2019.

Python, a language that’s developed a strong affinity with data scientists for machine-learning applications, is overwhelmingly the language that Julia developers would turn to if they needed another language.

Regardless of which popularity index you look at, Python is in the top three, and its popularity is being driven by data scientists and a growing demand for machine-learning applications, plus a wealth of Python modules that helps extend its use in various fields.

But Julia, which developer analyst firm RedMonk has rated as a language to watch, does have decent support behind it too. Besides Julia Computing, the commercial side of the language, there is the Julia Lab at MIT’s Computer Science and AI Laboratory (CSAIL) and an open-source community gunning for its long-term success.

Last year, 73% of Julia users said they would use Python if they weren’t using Julia, but this year 76% nominated Python as the other language.

MATLAB, another Julia rival in statistical analysis, saw its share of Julia users as a top alternative language drop from 35% to 31% over the past year, but C++ saw its share on this metric rise from 28% to 31%.

Meanwhile, R, a popular statistical programming language with a dedicated crowd, also declined from 27% to 25%.

Some of these trends look positive for the long-term survival of Julia despite the threat posed by Python as the go-to language for data scientists.

The most frequently used languages after Julia are Python, and then Bash/Shell/PowerShell. And if Julia, which emerged in 2012, didn’t exist, most Julia users would be using C++, MATLAB, R, C, Fortran, Bash/Shell/PowerShell and Mathematica.

Julia users also revealed what they love and hate about the programming language, which Julia’s supporters claim is faster than Python and R for big-data analysis using CSV files for tasks like looking at stock-price states and analyzing mortgage risk.

Among the most-liked features include speed and performance, ease of use, its open-source status, and its ability to solve problems around using two languages. Non-technical reasons in its favor are that it’s free, has an active community of developers, and that it is available under an MIT license, while creating packages for Julia is supposedly easy to do.

The negatives that Julia users report are that it’s too slow to generate a first plot and has slow compile times. Also, there are complaints that packages aren’t mature enough – a key differentiator to the Python ecosystem – and that developers can’t generate self-contained binaries or libraries.

Julia is also suffering from an adoption obstacle due to colleagues and collaborators using other languages. Rust, another modern language that’s become popular for systems programming, is experiencing similar adoption obstacles with users because the companies they work at don’t use it.

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The latest edition of the TIOBE Index, which attempts to gauge the popularity of the world’s programming languages, reveals something fascinating: Go, Swift, Perl and R have gained substantial ground over the past year. But can any of them challenge the older, more-established languages (such as C, Java, and Python) for TIOBE’s top slots?

It’s worth nothing that Go, Swift, and R were among the languages that developers generally wanted to learn next, according to HackerRank’s 2020 Developer Skills Report (which surveyed 116,000 developers worldwide). Go also ranked highly on IEEE Spectrum’s recent list of the top programming languages for the web. 

The TIOBE Index just reinforces that these are languages to watch. “The programming language R continues to rise and is on schedule to become TIOBE’s programming language of the year 2020,” Paul Jansen, CEO of TIOBE Software, wrote in a note accompanying the data. “It is also interesting to follow the on-going fight for position #10 in the TIOBE index between Go, Swift and SQL. Swift lost 2 positions this month (from #10 to #12). SQL took over and is back in the top 10 this time. Also worth noting is Groovy‘s re-entrance in the TIOBE index top 20 at the expense of Scratch and the fact that Hack entered the top 50 at position #44.” 

To generate its rankings, TIOBE crunches data from various aggregators and search engines, including Google, Wikipedia, YouTube, and Amazon. In order for a language to rank, it must be Turing complete, have its own Wikipedia entry, and earn more than 5,000 hits for +”<language> programming” on Google. Critics complain that TIOBE more accurately measures “buzz” than actual language usage, but it’s nonetheless a useful ranking for determining what’s on developers’ (and other technologists’) minds when it comes to programming languages. 

R’s rise neatly counters the general narrative that the language, which is mainly used by researchers and data scientists for data-crunching, is slowly imploding. In July, R jumped to eighth place on TIOBE’s list, where it stayed through this month. “There are 2 trends that might boost the R language: 1) the days of commercial statistical languages and packages such as SAS, Stata and SPSS are over,” TIOBE wrote in a note accompanying the data at the time. “Universities and research institutes embrace Python and R for their [statistical] analyses, 2) lots of statistics and data mining need to be done to find a vaccine for the COVID-19 virus.”

TIOBE has also claimed in the past that Perl’s future is in serious doubt, yet this latest update suggests a core of developers aren’t giving the language up. Perhaps the Perl legacy codebase is behind this endurance. Go and Swift, meanwhile, are pushed by Google and Apple developers, respectively, which gives them a significant leg up over other languages. It might be some time, though, before the dominance of Java, Python, and C are seriously threatened. 

The post Programming Languages on the Rise: Swift, Go, and… Perl? appeared first on Artificial Intelligence.

Python is one of the most preferred high-level programming languages, which is being increasingly utilised in data science and in designing complex machine learning algorithms. In one of our articles, we discussed why one should learn the Python programming language for data science and machine learning.

In this article, we list down the top 9 free resources to learn Python for Machine Learning.

1| Google’s Python Class

About: This is a free class provided by the developers at Google. It includes written materials, lecture videos, and lots of code exercises to practice Python coding. The first exercises work on basic Python concepts like strings and lists, building up to the later exercises which are full programs dealing with text files, processes, and Http connections.

2| Introduction to Data Science using Python

About: In this course, you will understand the basics of data science and analytics as well as how to use Python and scikit-learn. The course will show you what data science is and how is it used. You will go through commonly used terms and write some code in Python as well.   

3| Data Science, Machine Learning, Data Analysis, Python & R

About: This course has been designed by data scientists to help you learn complex theory, algorithms, and Python libraries. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of data science. The course includes both Python and R and is also packed with practical exercises that are based on real-life examples.

4| MatPlotLib with Python

About: This course has been designed for those who want to learn a variety of ways to visually display data. With over 58 lectures and 6 hours of content, this course covers almost every major chart that Matplotlib is capable of providing. Intended for students who already have a basic understanding of Python. You will take a step-by-step approach to create line graphs, scatter plots, stack plots, bar charts, 3D lines, 3D wireframes, 3D bar charts, 3D scatter plots, geographic maps, live-updating graphs, and much more. On completion of this course, you will not only have gained a deep understanding of the options available for visualising data but also know how to create well presented, visually appealing graphs.  

5| Data Science with Analogies, Algorithms and Solved Problems

About: This course will help you learn complex theory, algorithms and coding libraries in a simple way. With every tutorial, you will gain new skills and enhance your understanding of this field. It includes a brief introduction to Python and its libraries and how to implement this language in machine learning. 

6| Machine Learning In Python

About: In this e-book, you will learn essential techniques of machine learning in predictive analysis using Python programming language. The topic includes predictive model building, writing ensemble methods using Python, understanding and working with data and much more. 

7| Machine Learning With Python

About: This tutorial provides a quick introduction to Python and its libraries like NumPy, SciPy, pandas, Matplotlib, and explains how it can be applied to develop machine learning algorithms that solve real-world problems. The tutorial starts with an introduction to machine learning and the Python language and shows you how to set up Python and its packages. It further covers all important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualisation, clustering, classification, regression and model performance evaluation.

8| Modern Machine Learning in Python

About: Here, you will learn the basics of Python and machine learning and why Python is needed for performing machine learning tasks. It includes an introduction to machine learning, why Python is becoming the central tool for data scientists, alternatives to Python in data science and other such.

9| Python for Data Science

About: This free Python course provides a beginner-friendly introduction to Python for Data Science. This will kickstart your learning of Python for data science, as well as programming in general. Upon its completion, you’ll be able to write your own Python scripts and perform basic hands-on data analysis using Jupyter-based lab environment. 

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How many of you agree with the following Assertions? 

Assertion 1 – Being a Data Scientist is one of the best jobs you can have today. It has been referred to as the “sexiest job of the century” and is in great demand.

Assertion 2 – Once you become a data scientist, life is going to be awesome. You get paid well and will be in a job which will disrupt multiple industries. So, while every other industry worries about disruption, you can enjoy the fruits of your hard work to become a data scientist.

I am sure you have come across both people with these beliefs in the last few years. While the first assertion is mostly fact-based and close to truth, the second couldn’t be farther from truth. As a data scientist, you will need to regularly make yourself uncomfortable. In fact, that is the reason why this role is so exciting and would stay that way for the times ahead.

If this truth has punctured your dreams to become a data scientist, I am actually glad that I stopped a few people from entering the industry for wrong reasons. 

The fact is that while data scientists are in great demand, anyone who has to excel in this field needs to continuously learn and relearn in several dimensions. You get exposed to new business functions and verticals, you come across people who act weird and their behaviour is far from logical (i.e. you need to learn the soft skills).

However, the biggest challenge comes up in keeping pace with the technology itself.

Don’t believe me? I have been in the data science industry even before it was christened as ‘data science’. So, you would think that I know the industry inside out. The amount of learning (on technology front) I have done in the last five years is actually more than what I have learnt in 10 years before that.

I had to learn a few new programming languages, dozens of libraries, go through a whole lot of research papers, navigate through datasets and open source contributions from the likes of Google, Microsoft and Facebook. Also, did I mention new tools, techniques and startups which spring up across the globe? 

Even after spending so much time keeping pace with the technology, I have this feeling that there is so much more which I am not able to keep pace with.

Now that you grasp the landscape, let me delve deeper in different types of technological developments and how they impact life of a data scientist:

1. New Hardware released by companies opening up possibilities which did not exist before. Intel and Nvidia have been fighting this race historically, but the next decade would belong to mobile chips and possibilities on handheld devices. There is an entire sub-field in data science called “Edge Computing” studying what algorithms can run on what devices, which makes your devices “Smart”.

2. New Software released by companies to make computations faster and more efficient. This includes new libraries and open source contributions, which end up taking a lot of time for a data scientist. These developments are also very relevant as they address a lot of pain points faced by the data scientists.

3. Research & Development coming out of Research labs. These developments are typically the most exciting and they get the most media coverage as well. Google beating DOTA champions or using AI to save electricity costs of its data centre are all part of this. Not everything coming out here is relevant to every data scientist, but when it is, it can be game-changing.

Irrespective of which domain you are in, as a data scientist, you need to keep a track on these developments to ensure you are on top of this game. The only way to ensure this is to set aside time at personal level and at professional level to ensure you and your team members are continuously learning and experimenting with these new developments.

As an employer or a team leader, you need to ensure that your team gets enough time for this learning. Continuous learning, hackathons, participation in meetups, webinars and conferences are some of the effective tools being used in the industry.

If there is one thing you take away from this:

• As a data scientist, make sure you are prepared for learning a whole lot of new tools and techniques.
• As an employer, make sure that data scientists in your company get time to explore, experiment and work with the latest developments in the field.

The post How new technology in Data Science is impacting the life of Data Scientists appeared first on Artificial Intelligence.

JavaScript remains the most popular programming language in the world, according to Amazon’s State of the Developer Nation Survey for 2019.

The language has more than 12 million users worldwide and is one of the fastest-growing programming languages globally – growing by 3 million developers between Q4 2017 and Q4 2019.

Amazon estimates that there are more than 20.4 million active software developers in the world, more than half of which are using JavaScript.

“Not only do new developers see it as an attractive entry-level language, but also existing developers are adding it to their skillset,” Amazon said.

“As a result, JavaScript is now used by more than half of developers working on web applications, cloud services, or extensions for third-party ecosystems.”

Two other languages that have seen steady growth are Python and Java.

Python added 2.2 million new developers in 2018 and surpassed Java in terms of popularity. Despite this growth slowing in 2019, Python remains the second-most widely-used programming language overall.

Popular applications

The survey found that JavaScript was most popular in web, cloud, and third-party ecosystem applications, and least popular in IoT app and gaming development.

Python was the most popular programming language for IoT applications and the least popular in gaming and mobile development.

Java, the third-most-popular language in the world, was most popular among mobile and cloud developers and was one of the least popular languages for web applications.

Programming languages which were popular for AR and VR applications included C#, Visual tools, Swift, and Rust.

C# was also most popular among video game developers for platforms other than mobile.

“The fastest-growing language community in percentage terms is Kotlin,” Amazon noted. “It nearly doubled in size in the past two years, from 1.1 million developers in Q4 2017 to 2 million in Q4 2019.”

“Given that Google has made Kotlin its preferred language for Android development, we can only expect this growth to continue, and Kotlin becoming a core language in mobile development.”

Open-source contributions

The survey also found that 3 out of 5 developers contribute to open-source software.

“Developers are most motivated to contribute to open-source projects to improve coding skills (29%) and a belief in the benefits of open source (26%),” Amazon said.

“What’s more, 22% of developers contribute to open-source software because it’s fun or to solve an issue with an existing open-source software project such as fixing a bug or creating a new feature.”

The graph below details the most popular programming languages in the world according to Amazon’s State of the Developer Nation Survey for Q4 2019.

The post The most popular programming languages – And what they are used for appeared first on Artificial Intelligence.

R, Python, and Scala are the three major languages for data science and data mining. Here you’ll find out about their respective popularity, ease of use, and some pros and cons. Before all that, however, an important link between data warehousing and Big Data needs discussing.

Organizations and enterprises of all sizes can analyze the large stores of unstructured and structured data they are inundated with on a daily basis for trends, patterns, and correlations, with the expectation that such analysis leads to better business decisions and more knowledge on human behavior. According to Forbes, the adoption of so-called Big Data analytics increased to 53 percent of companies during 2017.

A big part of the transition to Big Data analytics is getting an acceptable computing infrastructure in place to store all this data, however, the challenge doesn’t end there. Companies must also decide which programming language their developers and data scientists will use when working with Big Data.

Data Warehousing & Big Data Analytics

Data warehousing ties in with Big Data analytics in the sense that it is also an important driver of business intelligence. In a data warehouse, multiple sources of enterprise data are integrated into a centralized repository for reporting, analysis, and decision-making purposes. To read more on data warehouses, check out this guide to data warehouse basics.

Big Data is just data—it’s the analysis that can turn it into valuable business intelligence. However, much of the information in Big Data systems ends up not being of much use; special systems, software, and processes are required to even get to grips with all this voluminous data that companies gather at high velocity. Big Data evolved as a distinct term because traditional database systems can’t cope with all that data. The end goal is similar, though, between Big Data systems and data warehouses: analyze data and get actionable insights from it; the scale and data structure are what differ.

Even though Big Data systems and data warehouse systems are typically distinct, some SQL data warehouses can be useful for Big Data analysis, including the open-source Cloudera Impala, Apache Hive, and Apache Spark. Let’s now focus on some Big Data programming languages.

R is a programming language used primarily for statistical analysis. A series of packages exist for R known as Programming with Big Data in R (pbdR), which facilitates the analysis of Big Data, distributed across multiple systems, using R code.

R’s flexibility is a strong point because you can run on almost all operating systems. In addition, R has excellent graphical capabilities, which can come in useful when trying to visualize patterns and associations within Big Data systems. Packages like ggplot2 can further enhance R’s data visualization capabilities and make it easy to produce high-quality graphs.

However, R is less of a general-purpose language, meaning developers and data scientists might have some trouble getting to grips with it compared to a more traditional programming language. It has a steep learning curve for anyone approaching it without a purely statistical background. Furthermore, users of R can encounter some speed and efficiency issues.

The average pay for a data scientist with extensive R skills is $115,531 per year.

Python is more of a general-purpose programming language that developers are much more likely to be familiar with. Python is also easier to learn, and there are several excellent, completely free tutorials online that go through the basics. Python is regarded as a ‘glue’ language, meaning it’s good for when data analysis tasks require integration with web applications.

Python is the most popular language used by data scientists to explore Big Data, thanks to its slew of useful tools and libraries, such as pandas and matplotlib. Python also has excellent performance and scalability for data science tasks., and it can be used with fast Big Data engines such as Apache Spark via the available Python API.

A disadvantage is that the community data for exploration and learning is not as extensive as that for a dedicated statistical language like R.

A data scientist with Python skills can command an average salary of $93,185 per year.

Scala is a general-purpose programming language designed partly with the intention to address some of the main criticisms of the Java language. The Apache Spark cluster computing solution is actually written in Scala, which explains the popularity of this language in data science, particularly Big Data analysis.

Scala used to be mandatory to work with Spark, but this has been addressed with the opening of API endpoints accessible with other languages. However, it’s still the de facto language for some current Big Data tools, such as Finagle. Scala has superb concurrency support, which is imperative for parallelizing a lot of the processing needed for large data sets. Scala runs on Java virtual machine (JVM), making it ideal for use with a framework like Apache Hadoop.

The average annual salary for a data scientist with Scala skills $102,980.

In summary, you can’t really go wrong with choosing any of these languages for Big Data programming. As the most general purpose language and the one likely to take the least time for developers and data scientists to become familiar with, Python is probably worth starting off with, particularly due to its well-established API endpoints with engines like Apache Spark, which are often used for Big Data analytics.

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