Microsoft released a new certification for Azure Administrators, the Associate Level Certification. This certification is designed to verify knowledge and skills of implementing, managing, and monitoring an organization’s Microsoft Azure environment. The responsibilities for this role include implementing, managing, and monitoring identity governance storage compute virtual networks in a cloud environment plus provision size monitor adjust resources when needed. Candidates for the Azure Administrator Associate certification should have subject matter expertise with one or more of these tasks. Top software training institutes in Bangalore like IQ Stream Technologies offering Azure Administrator Associate online training with expert-led online curriculum that could help the students and professionals to have a detailed understanding of the Azure concepts.


How to Become a Microsoft Azure Administrator Associate?
Becoming a Microsoft Azure Administrator Associate can lead to many career opportunities. This certification is for candidates who have subject matter expertise implementing, managing, and monitoring an organization’s Microsoft Azure environment. A few responsibilities include implementing, managing, and monitoring identity, governance, storage, compute and virtual networks in a cloud environment. Other responsibilities include provisioning new resources when needed or adjusting existing ones to meet the needs of the company’s workloads. The Microsoft Azure Administrator Associate certification is for candidates who have subject matter expertise implementing, managing, and monitoring an organization’s Microsoft Azure environment. Candidates should be knowledgeable in all aspects of the cloud including identity, governance, storage, compute and virtual networks. They must also be able to provision resources such as server size and monitor/adjust them when needed. The Microsoft Azure Administrator Associate certification validates a candidate’s ability to configure, monitor, and maintain an organization’s cloud environment using the Microsoft Azure platform. The Associate level of this certification is for individuals who have demonstrated experience in managing and monitoring an organization's Azure infrastructure and provide specific technical expertise in implementing and maintaining storage, compute, virtual networks (e.g., load balancing), identity management, access control services such as Active Directory Federation Services (ADFS).

What's the Future of Azure Administrators?
Microsoft Azure Administrators play a critical role in implementing, managing, and monitoring an organization’s Microsoft Azure environment. As the business world becomes more digitalized and dependent on cloud-based services, organizations are looking for professionals with experience running Microsoft Azure. In order to become certified as an Associate Administrator of Microsoft Azure (MAA), candidates must demonstrate technical expertise implementing, managing or monitoring identity governance storage compute virtual networks in a cloud environment; provisioning adjusting resources when needed; and maintaining security. Becoming an Azure Administrator Associate is a crucial career move for IT professionals who want to get ahead in the cloud computing industry. With Microsoft Azure’s fast-growing popularity, it’s important that you have expertise implementing, managing, and monitoring this environment. As a candidate for the certification, you should be skilled at configuring your environment with identity governance and storage solutions from products like Active Directory Domain Services (AD DS) or Windows Server Active Directory (WSAD). The Microsoft Azure Administrator Associate certification is a vendor-neutral credential that confirms an individual's ability to configure, manage, and monitor an organization’s Microsoft Azure environment. In the recent years, the IT industry has been moving away from traditional on-premises datacenters to cloud-based services for cost effectiveness and agility. The need for specialists who can design and maintain cloud environments is rapidly increasing as companies move their workloads into the public or private clouds.

The Latest Job of the Future? An Azure Administrator Associate.
In a world where jobs are being disrupted and the economy is going through rapid changes, it's important to keep up with technology. A new certification has been introduced by Microsoft called Azure Administrator Associate. The requirements for this certification include a person who knows how to install, configure, manage and monitor cloud environments as well as having expertise in identity governance storage compute virtual networks. Achieving the Azure Administrator Associate certification will help you meet the demand for cloud-focused roles in organizations, as it's quickly becoming one of the most sought after certifications on today's job market. The Azure Administrator Associate certification is aimed at candidates with subject matter expertise implementing, managing, and monitoring an organization’s Microsoft Azure environment.

Let's try to think about each one in terms of what it would cost us to replicate in our own data centers instead of using Azure so on a diagram on the right part of my screen we'll start with high availability which already should be selected and high availability is the ability to maintain an acceptable continuous performance despite temporary load fluctuations or failures in services hardware or data centers. And the key point here in Microsoft Azure is to maintain acceptable continuous performance which is in bold and that means that even if traffic varies to our site if it increases greatly or decreases greatly or in fact if certain Azure services fail if some hardware fails or in fact if an entire data center fails our applications and systems can still maintain acceptable continuous performance. Microsoft azure concepts course can be accessed here online: https://www.udemy.com/course/linux-academy-microsoft-azure-concepts/

And so let's take a look at how Azure does that and it's based on redundancies at every level. So we'll start at the finest level which is a single individual data center within Azure and we'll look at the redundancies employed there to make sure that everything stays up as much as possible.
First off with power the power systems in these Azure Data centers are fully redundant. It's not uncommon for a data center to be able to generate its own power and as well there might be different providers of power coming into the data center. IQ Stream Technologies offering top quality Microsoft azure training in Bangalore with 100% placements. All the azure training sessions will be delivered by highly experienced professionals and industry experts.

So if one has an interruption the other stays up. And there's also redundancies in the power generation the power battery backups and systems like that. There's also redundancies with the cooling systems they might use a combination of different cooling approaches like water cooling and air cooling and they might have redundancies as well with the providers that they use to power and to fuel those cooling systems and additionally with the networking equipment like switches and routers and other networking hardware all of those are fully redundant.

And as your data centers as well. And in fact there might be multiple Internet service providers coming into the data centers as well. So again if one is actually down or has an interruption the other one can stay up and service the needs of the data center so within an Azure Data Center there's redundancies at nearly every level but in case something happens to an entire data center we can step up a level from the data center and go to
Availability Zones and reap even more redundancies there.

So an availability zone is essentially a grouping of one or more data centers put together.

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Course overview:
Maths – Maths in Data Science include Linear Algebra which refers to familiarity with integrals, differentiations, differential equations, etc.; Statistics including Inferential Statics, Descriptive Statistics, Chi-Squared Tests, Random Variable, Gaussian and Normal Distributions, etc.; Probability like Bayes Theorem, Optimization like Convex Optimization, etc.
Programming Language- Mastering a programming language is highly necessary to pursue Data Science. One can choose from an array of languages including R, Python, Julia, C++, Go, Octave GNU, Matlab, etc. For Python, libraries and packages like Matplotlib, Numpy, Pandas, Seaborn, Scikit-Learn, Scipy, BeautifulSoup, Bokeh, Urllib, PandaSQL, etc., are extremely important to know. Similarly, for R – ggplot2, tidy, etc., are extremely important.
Data Wrangling and Management: This involves thorough understanding of Data Mining, Data Cleaning, Data Management which includes having knowledge of SQL such as MySQL, NoSQL like MongoDB, Cassandra, etc.
Data Analysis and Visualization: Analytics and Visualization forms an integral part of the Data Science workflow. One needs to have the knowledge of using plotting libraries in programming languages like plotly, matplotlib, seaborn in Python and ggplot2 in R. Also having the understanding of using Tableau, Excel and D3.js is considered necessary for interactive visualization.
Machine Learning: Under Machine Learning, topics to be covered are Supervised Learning, Unsupervised Learning, Reinforcement Learning, Testing, Evaluation and Validation of Models to say in brief.

More concisely, the detailed topics are as follows, Linear Regression, Polynomial Regression, Logistic Regression, Decision Trees, Random Forests, Boosted Trees, Naïve Bayes, Bayes Theorem, Ensemble Learning, Ada Boost, Hierarchial Clustering, Divisive and Agglomerative Clustering, DBSCAN, Kmeans Clustering, K-Nearest Neighbours, Perceptrons, Gradient Descent, Multi-Layered Perceptrons(MLP), L1 and L2 Regularization, Cross Validation, Entropy, Train-test, F1 Score, Accuracy, Precision, Recall, Support Vector Machines, Collaborative Filtering, Confusion Matrix, Principal Component Analysis, Dimensionality Reduction and Neural Networks.
Deep Learning- Under Deep Learning, the topics to be covered are- Neural Networks, Feed Forward Neural Networks, Fuzzy Logic, Hyperparamaters Optimization, Sequence Models, Long Short Term Memory (LSTM), Recurrent Neural Nets(RNN), Convolutional Neural Nets(CNN), YOLO, Object Detection, Natural Language Processing, Computer Vision using OpenCV, etc.
BigData – Topics under BigData include MapReduce, Hadoop, Apache, Spark, Hive, Pig, Mahout, Yarn, BigData Analytics, etc.

Statistics with R

Descriptive:

-Data collection

-Data collection - Questionaire Designing

-Data collection - Observation

-Data collection - Case Study Method

-Qualitative Data Vs Quantitative Data

-Data Patterns

-Deciles Statistics

-Venn Diagram

-Central limit theorem

-Chebyshev's Theorem

-Kurtosis

-Normal Distribution

-Laplace Distribution

-Log Gamma Distribution

-Rayleigh Distribution

-Exponential distribution

-Multinomial Distribution

-Binomial Distribution

-Beta Distribution

-F distribution

-Negative Binomial Distribution

-Gamma Distribution

-Chi-squared Distribution

-Geometric Mean

-Harmonic Mean

-Outlier Function

-Stem and Leaf Plot

-Poisson Distribution

-Cumulative Poisson Distribution

-Inverse Gamma Distribution

-Continuous Uniform Distribution

-Hypergeometric Distribution

-Harmonic Number

-Gumbel Distribution

-Comparing plots

-Power Calculator

-Process Sigma

-Harmonic Resonance Frequency

-Standard normal table

-Pooled Variance (r)

-Mean Deviation

-Means Difference

Rcode(+theory) for Descriptive:

central tendency:

-Arithmetic Mean

-Arithmetic Median

-Arithmetic Mode

-Arithmetic Range

-Range Rule of Thumb

-Adjusted R-Squared

-Standard Deviation

-Relative Standard Deviation

-Analysis of Variance

-Grand Mean

-Boxplots

-Quartile Deviation

-Frequency Distribution

-Bar Graph

-Dot Plot

-Scatterplots

-Correlation Co-efficient

-Pie Chart

-Histograms

-Cumulative Frequency

-Cumulative plots

-Skewness

-Goodness of Fit

-Transformations

-Trimmed Mean

-Reliability Coefficient

-Linear regression

-Logistic Regression

-Quadratic Regression

-Regression Intercept Confidence Interval

-Residual sum of squares

-Equation Sum of Square

-Standard Error ( SE )

-Root Mean Square

-Cohen's kappa coefficient

-Ti 83 Exponential Regression

-Shannon Wiener Diversity Index