What is DP 100 Microsoft Data Science Certification?
This Azure Data Science Certification endorses your ability to build intelligent technology-enabled business solutions using Azure data science and machine learning capabilities. After passing DP 100 Exam, you become Microsoft Certified: Azure Data Scientist Associate. After Amazon’s AWS, Microsoft’s Azure is the next big cloud computing service provider with a 19% market share. This has led to the growth of the employment of candidates with Azure Data Scientist Certification.
Professionals with Microsoft Certified Azure Data Scientist Associate apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders. Candidates with Azure Certification Big Data use machine learning techniques to train, evaluate, and deploy models to build AI solutions that satisfy business objectives. Candidates earning data science certification from Microsoft use applications that involve natural language processing, speech, computer vision, and predictive analytics. Candidates serve as part of a multi-disciplinary team that incorporates ethical, privacy, and governance considerations into the solution. Microsoft Certified data scientists typically have a background in mathematics, statistics, and computer science.
Why Azure Data Scientist Associate Training
In this Microsoft Certified Azure Data Scientist Training, gain the necessary knowledge about how to use Azure services to develop, train, and deploy machine learning solutions. The course Microsoft Certified Azure Data Scientist Associate starts with an overview of Azure services that support data science. From there, it focuses on using Azure’s premier data science service, Azure Machine Learning service, to automate the data science pipeline. Our Data Science Certification Microsoft training is based on the new DP 100 Exam pattern updated in 2021.
Microsoft Data Science DP 100: Skills Measured
- Manage Azure resources for machine learning (25–30%)
- Run experiments and train models (20–25%)
- Deploy and operationalize machine learning solutions (35–40%)
- Implement responsible machine learning (5–10%)
Who Should Earn DP 100 Microsoft Data Science Certification?
Microsoft Data Science Certification helps data scientists to learn the best data collection, analysis, processing, and modeling practices. The machine learning techniques, and skills on Data Visualization and Reporting, Risk Analysis, etc., you earn through Azure Data Science Certification help you create actionable plans you need for an organization you work in.
Machine Learning Scientist
Microsoft Data Science Certification brings you a step closer to your dream of becoming a machine learning scientist. An Azure Data Scientist Associate designs an adaptive system using supervised, unsupervised, and deep learning techniques. The adaptive intelligence solution helps organizations to automate their processes, scale business and respond to opportunities and challenges.
Applications architects building enterprise applications make use of data science and machine learning techniques and tools that enable organizations to gain agility and scalability, avoid bottlenecks and benefit from descriptive and predictive analytics models.
If you provide consulting on computer systems design and related services, you can benefit from the Azure Data Science Certification. It helps you provide bespoke products or solutions to clients responding. Intelligent and responsive IT infrastructure helps in building a progressive and resilient organization. A Microsoft Certified: Azure Data Scientist Associate knows how to make organizations seize opportunities and respond to challenges.
Microsoft Certified: Azure Data Scientist Associate Prerequisite
Before attending this Microsoft Certified Azure Data Scientist Associate course, you must have:
• Azure Fundamentals
• Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
• How to program in the Python programming language and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.
Module 1: Define and Prepare the Development Environment
- Select Development Environment
- Set up Development Environment
- Quantify the Business Problem
Module 2: Prepare Data for Modeling
- Transform Data Into Usable Datasets
- Perform Exploratory Data Analysis (EDA)
- Cleanse and Transform Data
Module 3: Perform Feature Engineering
- Perform Feature Extraction
- Perform Feature Selection
Module 4: Develop Models
- Select an Algorithmic Approach
- Split Datasets
- Identify Data Imbalances
- Train the Model
- Evaluate Model Performance
Fees & Schedule
|Delivery Mode||Course Duration||Fees|
|Live Virtual Training||4 Days||Ask for Quote|
|Onsite Classroom Training||4 Days||Ask for Quote|
|Customized Training||4 Days||Ask for Quote|