The field of cybersecurity — both offensive and defensive security has come along a long way since the first computer virus called Creeper, created by Bob Thomas in 1971, that successfully disguised itself to move from one host to another within the intranet and leaving its trail on the hosts. The program was a research project, and it was not intended to harm anything.
In today’s era, where we have a massive spider web of internet, the attack surface is huge, and we are not talking about simple programs but advanced persistent threats. They are advanced because the attackers/hackers have a full spectrum of intelligence-gathering techniques at their disposal. They are persistent because they like to follow a ‘low-and-slow’ approach to intrude and maintain long-term access on the target devices. They are threats because they have both capability and intent to harm.
On the other hand, Data Science, Big Data & Artificial Intelligence (AI) with its sub-domains like Machine Learning and Deep Learning are making tremendous progress in many business areas like Sales and Marketing, Product Recommendation, Image Classification, Risk Analysis, Fraud Detections, Autonomous driving, and the list goes on.
I have been working in the Cyber Security industry for a while, and now I have also gained some knowledge and professional skills in Data Science. I used to always pause and ponder- where is that perfect intersection or connection of Data Science and Security. If you browse the internet to search for articles, research papers, and more importantly datasets to work on network/cyber security area, the chances of finding what you want or need are lower as compared to other areas.
Both data science and Cyber security jobs are in high demand. The mix of these two skillsets will definitely give you more strengths to become marketable. Besides the big names like Symantec and McAfee, there are many companies that are trying to pioneer and invent predictive models in cyber security space. Some of those companies are Cylance (now acquired by BlackBerry), Darktrace, Crowdstrike, Carbon Black (acquired by VMware), Obsidian Security, and more. You can already realize that- if big companies are acquiring the start-ups in billions of dollars, undoubtedly that subject area is hot in the market.
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With the rise of Big Data management concepts, we are seeing rapid adoption of data science techniques to retrieve vital information from unstructured data sources.
These projects are mostly led by trained data analysts and IT managers who have recognized data science certification from top institutes in India.When we talk about data, we are often struck with the various nuances of data operations (popularly referred to as DataOps) which could be related to any of the following sub-fields mentioned below:Data ManagementData StorageData IntelligenceData AnalyticsData Repurposing, and so onIn general terms, we are evaluating mostly the role of analytics in data storage techniques, and how data science practices influence key benchmarks or KPIs in the data management cycle.
We still need highly trained and reliable ML models to identify the different types of data storage techniques and the activities that could simply data engineering as a whole.
You would mostly work with three types of data storage practices;Auto-SearchDocument-basedObject-basedIn recent times, 90% of the data storage techniques are developed to handle rich metadata and parallel architecture that infinitesimally influence the way AI ML workflows are managed, allowing databases to grow continuously in scale, reliability, and agility.Common techniques in data storage Data storage management incorporates the following techniques that can be learned and experimented with, in a data science online course:Cloud computingInferential statistics / computational statisticsStatistical modelingSignal processingNatural Language Processing (NLP)Predictive AnalyticsData extraction, loading, and transformationLet’s understand the role of data science in Data Storage, as witnessed from recent developments and innovations in the industry.Why you should learn about data storage management in a data science certification courseFor top-grade data engineers, understanding how data storage works are critical to the success of many underlying practices that go into the development of modern IT frameworks, mostly supported by Cloud environments, AI Ops, and AutoML techniques.
Newer concepts such as Dockers/ Kubernetes, containerization, virtualization, GPUs, and ETL techniques are related to data engineering, data storage, and retrieval – all put together in one basket.
In data science certification, you would come across many topics related to search engines, content discovery, and document storage, and these can be understood by working in environments familiarized with SQL databases, Elasticsearch, Redshift, MongoDB, Apache Kafka, AWS, PostgreSQL, and so on.
ExcelR Solutions Data science certification Coaching in Bangalore, IndiaThe learning classes clarify the entire project administration cycle, together with project initiation, plan, execution, control and closure.
ExcelR data science is among the greatest training suppliers for project management coaching in Bangalore.
They have educated around 35,000 professionals in this space and its Google rating is 4.8 out of 5.
There are numerous modes of coaching obtainable for PMP in Bangalore corresponding to online or digital training or even classroom coaching.
They deliver coaching by leading trade-recognized consultants, who're licensed and who've good work experience.I even have attended Data science certification, the experience of the training was sensible.
The coaching was very interactive, and the coach efficiently have interaction everyone in the team by numerous actions.
