Industry analyst Doug Laney postulated the current definition of Big Data comprising the three V’s: volume, velocities, and variety. It’s used to describe a large amount of structured and unstructured data, which overwhelms us on a day-to-day basis.
Businesses are interested in Big Data because it can be analyzed to gain valuable insights for better strategies and decision-making.
If you learn Java or any other language that suits data science and some special tools and libraries, you’ll become well equipped with the necessary skills for the job market.
A report by Deloitte Access Economics suggests that almost 76 percent of businesses will be making a huge contribution to their data science spending in the coming years. Data science is helping companies to increase and enlarge the customer base in record time.
For example, in 2003, it took iTunes 100 months to gain 100 million subscribers. The mobile game Pokémon GO achieved the same feat in a matter of mere days back in 2016. This is because Data Science and Big Data study trends in the customer base and offer a solution with almost guaranteed profits.
Data science was marked as the highest paying job in 2016 by a survey conducted by Glassdoor. It is said that every year the need for data scientists increases by 29 percent. The demand for these positions continues to grow in importance. According to the domestic job market, there will be 5,200 new computer and information research scientist positions between 2018 and 2028. That’s a market growth of 16%.
This unprecedented level of growth can be owed to the rise in popularity of Artificial Intelligence and Machine Learning. It was around the year 2005; this surge started taking place. Once it hit the market, data science changed everything about the business. By bringing in key elements of specifications, data scientists observed trends of the customers and thus expanded businesses.
The increasingly technical nature of the corporate world has completely changed the job market of the 21st century. Data science and Big Data have emerged to be the key players in the market. They offer brilliant job prospects and opportunities for climbing up the corporate ladder.
Big Data offers two major career paths: big data engineer and big data analytics.
These are mainly data engineers who work with a large volume of data. They:
The salary of a high-paying Big Data engineer ranges from $130,000 to $220,000 per annum.
Data scientists or analysts are concerned with the design of data. They:
A trained and skilled scientist earns anywhere between $105,000 to $185,000 per annum approximately.
The top four programming languages in data science and machine learning are Java, Python, R, and Scala. So here is an overview of each.
Java is one of the most popular programming languages. The motto of this language is that it enables data scientists to “write once, run anywhere,” meaning that a well-designed Java code can actually run on any platform without any changes in it.
Some facts about Java:
Java and Big Data
Java is used by many enterprises and comes as one of the most efficient languages to learn Big Data. Large companies use huge datasets, which almost makes Java the basic language for Big Data. Also, the fact is that the part of the Hadoop ecosystem, components that support the processing of Big Data, is also written in Java:
Where to learn Java?
Python is a diverse coding language and also one of the most important tools for data science and holds great value to developers. It is one of the most popular tools used in dealing with Big Data files. It is a high-level language well equipped to deal with tasks like Machine Learning, Deep Learning, Artificial Intelligence, and many more. It’s very simple to learn and easy to operate. Python is very effective for small programs, but not so much for large ones.
Python is also famous for the high number of libraries in the likes of TensorFlow, PyTorch, SKlearn, Matplotlib, Scipy, Pandas, etc.
Python is the API for most Big Data frameworks.
Where to learn Python?
Scala is modern and cutting-edge. It is a functional and object-oriented multi-paradigm language that has a scalable approach with a robust and steady type system.
Scala runs on Java Virtual Machine and thus shows seamless interoperability with Java. However, due to the smaller community (as opposed to Java or Python) and complexity of the language, it’s not very suitable for beginners. Taking both facts into account, if you want to learn Scala, it’s good to start with Java first.
APIs that Scala Big Data projects use
Where to learn:
The R language was created for scientists and researchers. The nature of the R language is very scientific, and it was mainly perceived as an instrument for statistical and graphical computational methods. It has many useful statistical and computational methods.
Some facts about R:
Where to Learn R?
In order to gain expertise in the field of data analysis, master a programming language. Data scientists may often find themselves entangled within a wide variety of programming languages to choose from, but the main ones to choose from are Java, Python, R, and Scala.
If you are a beginner programmer and are interested in Big Data, Java, or Python — your choice. Java is great for projects of varying complexity; it has a very rich pool of tools and is widely used not only in scientific programming. We wish you good luck with your studies!
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