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. Why Get into Big Data? A report by Deloitte Access Economics suggests that almost 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. 76 percent of businesses 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 . 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 . 29 percent 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. Professions in Big Data Big Data offers two major career paths: big data engineer and big data analytics. Big Data Engineer These are mainly data engineers who work with a large volume of data. They: Transform data into insights that companies can use for better business decisions. Retrieve data from relevant sources, interpret and analyze it, and then form reports. Maintain software and hardware infrastructure of the company, i.e., the machines used in the processing of data. The salary of a high-paying Big Data engineer ranges from $130,000 to $220,000 per annum. Big Data Analytics (Scientist) Data scientists or analysts are concerned with the design of data. They: Construct and develop new and innovative processes of modeling, production, and the mining of data. Develop prototypes and algorithms, Conducting experiments based on data studies. A trained and skilled scientist earns anywhere between $105,000 to $185,000 per annum approximately. Top 4 Programming Languages in Big Data 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 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 lands an individual some of the higher-paying jobs. Most companies have high regard for someone who is trained in Java, and they are usually in high demand. It pays well to be a Java developer. Aside from being a very useful and multi-purpose language for businesses, Java has one of the biggest communities that won’t shy away from helping beginners. Java is one of the most popular languages. Some major examples include Google Apps Script, IBM Domino, JSSP, and MongoDB. This further reinstates the popularity of the programming language. A lot of the server-side applications were written in Java, especially enterprise-level apps. 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: Apache Hadoop helps in the processing of large data sets. It is a framework that is used to culminate external data into a single unit and then further categorize it. Hadoop is well equipped in failover support and scalability. It also has a comparatively low intensity on hardware. Hadoop is widely regarded for its skill with local data processing. Apache Hadoop. If we are talking fast-paced, then Apache Spark is the one that comes into mind. It is super effective and lightning-fast analytics machinery that works in a unified manner and is invaluable for Big Data. Apache Spark. Apache Hive is a framework for reading, writing, and managing big data files. These files, however, have to be stored under Apache Hadoop Distributed File System. Apache Hive. Apache Storm is another framework that processes Big Data files at high speed. This is even faster and more effective than Apache Hadoop. Apache Storm. Where to learn Java? — it has a substantial lesson plan for beginner students but can also be used if you’re switching from another language. Lessons are easy to follow and are enriched with practice applying the gaming format. Keeping  80% of the practice, the platform offers 1200 coding tasks and code validation. In this course, you’ll learn Core Java that covers such topics as Syntax, Object Oriented programming and its realization in Java, Java Collections Framework, and Multithreading. CodeGym — it offers a detailed and fun process of learning and is complete with an official certification. It is a 17-hour course to gain a general understanding of what the Java language is. There is the extra perk of obtaining expertise in Java 8 and Java 11. The Complete Java Certification Course by Udemy Python 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? offers a valuable course in Python 3. Codecademy by Udemy is very popular among students of machine learning. Python for beginners Scala 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 It is a framework that specializes in stateful computations over data streams both bounded and unbounded in nature. It is functional in all common cluster environments and also performs computations at a highly effective rate. Apache Fink. The nature of this event is that it is distributed over a community. It is a streaming platform equipped with handling an uncountable number of events per day. Apache Kafka. Apache Samza uses Apache Kafka for messaging, and its framework is that of a distributed stream processing. To provide fault tolerance and processor isolation, it uses Apache Hadoop YARN. Apache Samza. It is a type of open-sourced library. It uses Java or Scala to develop concurrent applications. Akka works by leveraging the Actor model. Akka. Where to learn: Books and library docs on Scala Exercises Practical tasks on Scala Tutorials R 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: The statistical methods include linear and non-linear modeling, classical statistical tests, time-series analysis, classification, clustering, etc. R provides an impressive variety of both statistical and graphical techniques. The R language comes as a complement to the S language, which provides itself as a vehicle of choice for research in statistical methodology. The R is a stepping stone as it provides an open-source route for that. It is highly extensible. R could be used effectively for the Data Analytics part of Big Data; however, learn it if you are really interested in statistics and statistical analysis. It’s not very suitable for beginners. Where to Learn R? course by Codecademy Learn R by Coursera R Programming Conclusion 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! Also Published Here

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Top 4 Programming Languages for Data Science and Machine Learning

Top 4 Programming Languages for Data Science and Machine Learning

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