Microsoft Business Intelligence Advancement

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Microsoft Business Intelligence Advancement – A comprehensive list of Microsoft certifications covers business intelligence and data science domains. As data science and business intelligence consultants, we can easily explain why these certifications are so popular: these directions are actively developing and the market is in high demand for the relevant workforce.

Here, we take a closer look at the current state with Microsoft certifications that certify mastery of SQL Server, Power BI, Excel, and Azure services related to AI and machine learning (we’ll call them business intelligence and data science certifications). And explain what skills they certify, how to acquire them and what benefits they can bring.

Microsoft Business Intelligence Advancement

For companies, having a Microsoft Certified Expert on board means having skills that the company can use in real-life projects. According to research, Microsoft Certified Developers are 90% more productive and nearly 60% more efficient than their non-certified peers.

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For a large number of BI and data science-related jobs, such as BI developers, data scientists and data analysts, Microsoft certifications serve as a quality mark. That’s first-hand experience that ScienceSoft’s analytics team can share. Being Microsoft certified translates into a competitive advantage and increases the chances of holders being hired and paid higher than their non-certified peers. Also, it shows their willingness and ability to learn and develop.

At Inspire 2019, Microsoft announced significant changes to its certification program and the adoption of role-based certification. For example, they are moving away from Microsoft Certified Solutions Associates and Microsoft Certified Solutions Experts (or MCSAs and MCSEs, which are still available) and introducing a ‘Microsoft Certified’ status that goes with the job role and the level achieved within their certification. Framework (Basic, Collaborative, or Expert). Also, Microsoft will expand their list of certifications in January – June 2020 with 10+ roles some of which may be from BI or data science domains.

Please note that according to the classification suggested by Microsoft, this group of certifications is called Data Analytics and Management.

Please note that according to the classification suggested by Microsoft, this group of certificates is called Azure certification

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To get the certification, the candidate has to follow a certain path suggested by Microsoft. Mainly, this route consists of two related tests. However, there are cases when a candidate must first fulfill certain prerequisites. For example, to get

For each exam, Microsoft shares clear guidelines. They describe what skills will be tested, indicate sample tasks and the proportion of these types of tasks in the overall test. Also, Microsofts provides rich learning materials and the candidate can choose the preferred preparation option, be it instructor-led or self-paced training, studying relevant books or taking official practice tests.

The cost of certification depends on the path followed by the candidate. Since one exam costs $165, the final cost will depend on the number of exams a candidate takes along the way. Let’s consider the longest (and therefore most expensive) path – earning

Certification by candidate following the MCSA SQL Server 2012/2014 path. Since the candidate has to pass four exams, the total cost of obtaining the MCSE is $660 (if the candidate passes all the exams on their first attempt). By the way, a candidate is allowed to take a certain exam more than 5 times within 12 months.

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However, Microsoft offers discounts to some candidates, for example, Microsoft Partner Network members, as well as when making promotional offers, we strongly recommend determining the exact price before registering for the exam.

Technically, Microsoft BI certificates do not expire. However, each certification is always tied to a specific date when it is earned. As technologies evolve and their newer versions replace their predecessors, it is natural to expect that a certification obtained in 2012 will fail to create a competitive advantage in 2019. Fully aware of this, Microsoft regularly updates its tests. Plus, they often offer transitional exams to help renew certification at a discount. When legendary computer scientist Jim Gray accepted the Turing Award in 1999, he laid out a dozen long-range information technology research goals. One of those goals is to create trouble-free server systems, or, in Gray’s words, “to build a system that is used by millions of people every day and yet administered and managed by a single part-time person.”

Gray envisioned a self-managing “server in the sky” that would store large amounts of data, and refresh or download the data as needed. Today, with the rise and rapid development of artificial intelligence (AI), machine learning (ML) and cloud computing and the development of cloud intelligence/AIOPS, we are closer than ever to realizing that vision—and moving forward. this.

Over the past fifteen years, the most significant paradigm shift in the computing industry has been the migration to cloud computing, which has created unprecedented digital transformation opportunities and benefits for business, society, and human life.

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The implication is profound: cloud computing platforms have become part of the world’s basic infrastructure. As a result, the non-functional properties of cloud computing platforms, including availability, reliability, performance, efficiency, security, and stability, have become extremely important. Yet the distributed nature, large scale, and high complexity of cloud computing platforms—from storage to networking, computing, and beyond—present major challenges in building and operating such systems.

Cloud Intelligence/AIOps (“AIOps” for short) aims to innovate AI/ML technologies to help design, build and operate complex cloud platforms and services efficiently and effectively.

Gartner, a leading industry analyst firm, first coined the term AIOps (opens in new tab) (artificial intelligence for IT operations) in 2017. According to Gartner, AIOps is the application of machine learning and data science to IT operations problems. (Opens in a new tab) While Gartner’s AIOps concept focuses only on DevOps, cloud intelligence/AIOps research has a much broader scope, including AI for systems and AI for customers.

‘s Cloud Intelligence/AIOps broad scope stems from the software analytics research we offered in 2009, which seeks to enable software professionals to explore and analyze data to gain insight and actionable information for data-driven operations related to software and services. We began focusing our software analytics research on cloud computing in 2014 and named this new topic cloud intelligence (Figure 1). In retrospect, software analytics is about the digital transformation of the software industry, such as empowering practitioners to use data-driven approaches and techniques to develop software, operate software systems and engage with customers.

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There are several scenarios around each of the three pillars of AIOps. Some example scenarios include predictive capability forecasting for effective and sustainable services, service health status monitoring, and timely health problem detection in AI for systems; Ensuring code quality and preventing defective builds deployed in production on AI for DevOps; And providing effective customer support on AI for customers. In all of these scenarios, there are four major problem categories that, taken together, constitute the AIOps problem space: detection, diagnosis, prediction, and optimization (Figure 2). Specifically, the purpose of detection is to identify unexpected system behavior (or anomalies) in a timely manner. Given the symptoms and associated artifacts, the goal of diagnosis is to localize the cause of service problems and find the root cause. Attempts to predict predictive system behavior, customer workload patterns, or DevOps activities, and so on. Finally, optimization tries to identify the optimal strategies or decisions needed to achieve certain performance goals related to system quality, customer experience and DevOps productivity.

Every problem has its own challenges. Find out for example. To ensure service health at runtime, it is important for engineers to continuously monitor various metrics and detect anomalies in a timely manner. In the development process, to ensure the quality of continuous integration/continuous delivery (CI/CD) practices, engineers need to create mechanisms to catch defective builds and prevent them from being deployed to other production sites.

Both scenarios require timely detection, and both have common challenges to effective detection. For example, time series data and log data are the most common data forms. Yet they are often multidimensional, the data may contain noise, and they often have different detection requirements—all of which can pose significant challenges to reliable detection.

Each AIOps is continuously researching problem categories. Our goal for this research is to empower cloud systems to be more autonomous, more proactive, more manageable, and more pervasive across the entire cloud stack.

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AIOps strives to make cloud systems more autonomous, minimizing human operations and rule-based decisions, thereby reducing user impact due to system problems, making better operational decisions, and reducing maintenance costs. This is achieved by automating as much DevOps as possible, including build, deployment, monitoring, and diagnostics. For example, the purpose of safe deployment is to quickly catch a defective build, preventing it from rolling out to production and resulting in significant customer impact. This can be extremely labor-intensive and time-consuming for engineers, because abnormal behaviors have a variety of patterns that can change over time, and not all abnormal behaviors are due to new construction, which can introduce false positives.

In the research, we used transfer learning and experiments

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