Trends for Data Science in 2019
AI Trends Report explores the key strategic shifts enterprises will make to stay intelligent and agile going into 2019. The year was marked by a series of technological advances, including advances in AI, deep learning, machine learning, hybrid cloud architecture, edge computing (with data moving away to edge data centres), robotic process automation, a spurt of virtual assistants, advancements in autonomous tech and IoT.
A majority of our respondents believe the growing adoption of AI and analytics has become a game changer across business functions and there will be an uptick in niche sectors such as sports. A key trend this year was core business processes being redesigned around AI to deliver advantages beyond cost savings. RPA has been widely deployed to automate well-defined, rule-based tasks and the spurt of virtual assistants in banking has spawned a new age of Conversational AI in finance. On the other end of the spectrum, we will see an increased AI-cloud interdependency which in turn will fuel the growth of public cloud companies. Organisations are also realising the benefits of cognitive technologies to provide better insights. This year we collaborated with AnalytixLabs, a leading Data science online training institute to bring out key trends.
Trends to Watch Out For and Prepare Yourself
So there’s been a lot of coverage by various websites, data science gurus, and AI experts about what 2019 holds in store for us. Everywhere you look, we have new fads and concepts for the new year. This article is going to be rather different. We are going to highlight the dark horses – the trends that no one has thought about but will completely disrupt the working IT environment (for both good and bad – depends upon which side of the disruption you are on), in a significant manner. So, in order to give you a taste of what’s coming up, let’s go through the top four (plus 1 (bonus) = five) top trends of 2019 for data science:
Artificial Intelligence and Intelligent Apps
The buzz created by AI is unlikely to die down in the coming year. We are in the nascent and initial stage of AI, and the following year will see the more advanced application of AI in all the fields. Harnessing AI will still remain a challenge. More intelligent apps will be developed using AI, Machine Learning and other technologies. Automated machine learning (ML) will become common and it will transform data science with better data management. There will also be the development of specific hardware for training and execution of deep learning. Incorporation of AI will enhance decision-making and improve the overall business experience. Applications and other services will increasingly rely on AI to improve the overall experience.
Cooling down: Hadoop
Data scientists
Hadoop once seemed like the answer to the question “How should I store and process really big data?” Now it seems more like the answer to the question “How many moving parts can you cram into a system before it becomes impossible to maintain?”
The Apache Hadoop project includes four modules: Hadoop Common (utilities), Hadoop Distributed File System (HDFS), Hadoop YARN (scheduler) and Hadoop MapReduce (parallel processing). On top of or instead of these, people often use one or more of the related projects: Ambari (cluster management), Avro (data serialization), Cassandra (multi-master database), Chukwa (data collection), HBase (distributed database), Hive (data warehouse), Mahout (ML and data mining), Pig (execution framework), Spark (compute engine), Tez (data-flow programming framework intended to replace MapReduce), and ZooKeeper (coordination service).
Blockchain
Blockchain is a major technology that underlies cryptocurrencies like Bitcoin. It is a highly secured ledger and has a variety of applications. It can be used to record a large number of detailed transactions. Blockchain technology can have far-reaching implications in terms of data security. New security measures and processes emulating the blockchain technology can appear in the coming year.
Edge computing and analytics.
Edge computing takes advantage of proximity by processing information as physically close to sensors and endpoints as possible, thus reducing latency and traffic in the network. Gartner predicts the evolution of edge computing and cloud computing as becoming complimentary models in 2019, with cloud services expanding to not just live in centralized servers, but also in distributed on-premise servers and even on the edge devices themselves. This should not only decrease latency, but also costs for organizations processing real-time data.
Heating up: R language
Data scientists with strong statistics
Data scientists have a number of option to analyze data using statistical methods. One of the most convenient and powerful methods is to use the free R programming language. R is one of the best ways to create reproducible, high-quality analysis, since unlike a spreadsheet, R scripts can be audited and re-run easily. The R language and its package repositories provide a wide range of statistical techniques, data manipulation and plotting, to the point that if a technique exists, it is probably implemented in an R package. R is almost as strong in its support for machine learning, although it may not be the first choice for deep neural networks, which require higher-performance computing than R currently delivers.
R is available as free open source, and is embedded into dozens of commercial products, including Microsoft Azure Machine Learning Studio and SQL Server 2016
IoT
IoT and the growth of digital twins. Even though the Internet of Things was on everyone’s lips in 2018, the buzz around the digitization of the world around us and its implications for data isn’t going away. The frenzied growth of IoT data – along with many organizations’ continued inability to handle or make sense of all that data with their traditional data warehouses – will be a major theme of 2019.
Conclusion
With such trends deemed to prevail in the coming year, the future for innovation and business looks bright. Like Big Data, Data Science will witness massive use and development in the upcoming year. The digital and physical world will increasingly get intertwined. Digital experience will get more intricately incorporated in human experiences. Fr more Read here data science Course
The five most interesting and important trends that could become common in the year 2019 (although the jury will differ on the topic of the quantum computer – it could work this year or ten years from now – but it’s immensely exciting).
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