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By: Microtek Learning
Jun. 22, 2022
Data science is the emerging career preference for many IT industry professionals. This explains the growing demand for the DP 100 examination in the job market. So it is understandable if you too want to be a data scientist.
But like every aspirant, you must have various questions, confusions, or doubts that concern the examination. For example, who is an Azure data scientist Associate? How is the data scientist certification beneficial for IT professionals? - You are at the right platform if these questions bother you.
This blog addresses such doubts about giving you an understanding of the Microsoft Azure Data Scientist Certification DP-100 exam.
The DP 100 Microsoft Azure Data Scientist Certification is meant for data scientists and machine learning experts who want to use Azure Machine Learning Service to integrate and operate machine learning workloads. It also includes preparing and implementing a better workplace for Azure workloads related to data science. Moreover, a data scientist also conducts data experiments, and train machine learning models, predictive in nature.
DP-100 exam certification makes an enormous scope for individuals aiming to become data science experts. According to various statistics, the average annual salary for a data scientist is $117,345. The number is more than $44,564 the national average. As a result, a Data Scientist's income is 163% elevated in comparison to the national average!
Glassdoor reports that the third-best job in the US is becoming a data scientist. The report is based on three factors: growth, demand, and supply.
The numbers on LinkedIn suggest that data science jobs have grown 650% in the last decade. In addition, a forecast by the US Bureau of Labor indicates that opportunities in data science will surge by an estimated 28% in the next 5 years. It can amount to approximately 11.5 million vacancies.
Increasing Demand & Limited Supply: Most IT Industries globally churn out immense raw data every day. The data requires evaluation and hence a dedicated team to process it. Data scientists use this data to reach a reason and implement the Machine Learning algorithm to make forecasts. However, only a handful of professionals are available for the job while the raw data piles up. It creates a great demand and supply gap for Data Scientists.
The wide gap between demand and supply of data scientists makes it a lucrative and high-paying career for IT professionals. Any professional's resume can shine bright with the mention of data scientist certification.
The certification leads to a significant increase in both employment opportunities and salary significantly.
The majority of respondents think that certification has enhanced their earning potential, with 84% reporting that certification has improved their work prospects.
Updating your CV with this certificate can improve your career lookout, and makes you more likely to get selected.
There is much information available about this on various pages. It may seem confusing at first because there is an overload of information - especially when an individual is trying to figure out if DP-100 is for them. These pointers are here to help you.
If Machine Learning and AI fascinate and interest you, you should sit for the exam.
IT professionals with a detailed knowledge of Microsoft Azure and data handling should apply for the certificate.
Individuals who are interested in or excel at statistics can also pursue it.
The certificate can benefit Data Scientists who are unfamiliar with Azure but have prepared data, trained models, and evaluated competing models.
The professionals who aspire to build a solid career in data science may go ahead with the DP-100: designing and executing a Data Science solution on the Microsoft Azure certification examination. However, it is advised that applicants have a foundational understanding of technology, arithmetic, IT, or connected fields.
Candidates should also have a basic understanding of the Azure cloud platform and also machine learning. According to Microsoft Azure, individuals should also have remarkable cognitive aptitude and skilled professional experience in the IT business. You can take notes from the pointers.
Candidates must have a fundamental understanding of Microsoft Azure.
Professionals attempting the exam must have experience in writing Python code. They should also be acquainted with libraries like Numpy, Pandas, and Matplotlib.
Candidates must have a fundamental Knowledge of data science. It involves preparing data and training machine learning models with common machine learning libraries like PyTorch, Scikit-Learn, or Tensorflow.
DP-100 is a crucial exam for your Azure journey. Hence, there are various recommendations for the topics candidates must cover. The topics mentioned below are those.
Creation of Azure Machine Learning space and data object management
Implementation of training scripts in Azure Machine Learning Area
Comprehensive knowledge of model training method Automation
Auto Machine Learning Utilization - building the best models
Creation of model as a service
The DP-100 certified professional assumes the tasks and roles of a Microsoft Certified Azure Data Scientist Associate. Machine learning approaches should be used by the Azure data scientist Associate for model training, analysis, and preparation in order to produce AI solutions that address corporate goals.
Professionals with DP-100 certification make use of applications involving computer vision, predictive analytics, speech abilities, and language process.
It is crucial for candidates to know the topics covered and their weightage in the examination to have a clear picture. The exam comprises the following.
25-30% - Azure Resources Management For Machine Learning
Establishing an Azure Machine Learning Workspace: It entails using Azure ML Studio to create an Azure Machine Learning Workspace.
Using Azure ML Workspace for data management: It involves choosing Azure storage services to create and manage datasets.
Manage to Compute for Experiments: It encompasses identifying the best compute parameters for a training workload, along with designing compute goals for research and training.
Employ access control and confidentiality: Identify access criteria and map them to built-in roles, use Azure Key Vault to handle credentials, and so on.
Create an Azure Machine Learning production workstation: It comprises setting up compute instances along with gaining access to Machine Learning spaces from the other runtime environments.
Create an Azure Databricks work environment: It involves creating an Azure Databricks workspace, an Azure Databricks cluster, and making & operating notebooks using Azure Databricks.
20-25% - Run Experiments And Train Models
Making ML models operating Azure ML designer: This step involves constructing a training pipeline using a designer.
Use an Azure ML workspace to run training scripts: It covers everything from developing and conducting an experiment with the Azure ML SDK to receiving data from a sample to specifying run options for a script.
Produce metrics, get research findings, and troubleshoot research problems: It explains how to see experiment outputs and how to track metrics created by a test run.
To develop the best models, use automated machine learning: It walks you through utilizing Azure Machine Learning Studio's Automated ML interface, defining a core metric, and retrieving the best model.
Azure Machine Learning can be used to fine-tune hyper parameters: Picking a sampling method, tracing the search space, and more.
35-40% - Deployment And Operation Of Machine Learning Systems
Choosing Compute for Model Deployment: It comprises thinking about security for deployed services and weighing the various compute solutions available.
For batch inferencing, establish an Azure Machine Learning framework: It involves configuring a ParallelRunStep, setting up compute for a batch inferencing pipeline, publishing it, and more.
Implement Machine Learning Ops Practices: It features, among other things, activating an Azure Machine Learning pipeline from Azure DevOps and automating model retraining in response to new data additions or modifications.
5-10% - Implement Responsible Machine Learning
Using model explainers to understand models: This step includes picking a model interpreter and creating feature importance data.
Explain how models should be fair: It entails assessing and minimizing model unfairness depending on forecast disparity.
Explain data privacy considerations: It thoroughly explains propositions of distinctive privacy and assessing noise in data (at acceptable levels) & the effects on privacy.
Name of the Certification: [DP-100] Microsoft Certified: Azure Data Scientist Associate.
Total Number of Questions: 40 – 60
Duration of Exam: 180 minutes
Cost of Examination: USD 165.00
Score Needed to Clear: 700
Validity of DP-100 certification: The Microsoft Certified Azure Data Scientist Associate certification DP 100 exam is valid for a year. The qualified professionals can reappear for the DP-100 certificate post one year to extend the certification validity. Regarding new services and technologies, Microsoft Azure advises professionals to renew their certifications to update their skills. Some candidates may question the duration of validity, but it is decided based on new methodologies and additions to learning.
We hope this article helps you in your journey to become an azure data scientist. A career in data science can be fruitful but highly demanding. However, candidates can truly aim toward success if they move in the right direction. DP-100 exam can be a step further for those who have not begun their Azure journey yet. They can look up and explore their options with Microtek Learning Solutions.
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