Mon-Fri 9am to 6pm CST
Microsoft Azure Big Data Analytics Solutions (MS-55224-1)
You have already taken demo for this course.
If you want to get access to demo again, feel free to contact our support at (855) 800-8240
About this course:
This Azure training course is designed to equip the students with the knowledge need to process, store and analyze data for making informed business decisions. Through this Azure course, the student will understand what big data is along with the importance of big data analytics, which will improve the students mathematical and programming skills. Students will learn the most effective method of using essential analytical tools such as R, and Apache Spark.
These Azure classes are held over two days, and led by the instructor for data professionals with a desire to expand their understanding and knowledge regarding development of big data analytic solutions on Microsoft Azure. By enrolling in this two day azure training, students will gain the ability to create solutions for real time and batch data processing. Various methods of utilizing Microsoft Azure are taught and practiced in lab exercises including Azure PowerShell, Azure CLI, and Azure Portal. 55224A-1 labs and exercises are focused on addressing the first two objectives of exam Microsoft 70-475 Designing Big Data batch, interactive & real-time solutions. The following two objectives, namely; cloud analytics solutions and designing machine learning, are covered in MS-55224A-2.
On average, a Big Data Analytic having Microsoft Azure skills is $135,000/- annually.
Once the course is complete, students will gain the capabilities to:
Develop interactive solutions and big data batch processing (55224A-1)
Develop real-time processing solutions for big data (55224A-1)
Develop Machine Learning solutions (55224A-2)
Operationalize end-to-end cloud analytics solutions (55224A-2)
All those individuals having experience in designing big data analytics solution for Azure can enroll in this course.
Students should have the following experience before enrolling in this course.
Querying and processing bulk data
Historical and real-time data analysis
Using SQL, visualization, and data analysis tools (e.g. Power BI)
PowerShell (Note: A basic PowerShell tutorial is part of the course.)
Suggested prerequisites courses:
Analyzing Data with Microsoft Power BI (MS-20778)
Introduction to Big Data
Career & Salary Insight
|Learning Style||Virtual Classroom|
|Course Duration||2 Days|
Monthly Payments With Affirm
QuickStart and Affirm have teamed up to offer you financing, allowing you to pay off your purchase over time, on your own terms. Simply select Affirm in checkout, and you will need to take a few moments to fill out some information for a real-time decision. Checking your eligibility and terms won't affect your credit score. Once you are qualified, you will have the option to choose from 3, 6, 12, 18 or 26-month financing terms*, with each term clearly showing your monthly payment. No hidden fees or costs! it's that simple!
Rates are between 0-30% APR. As an example, a $700 purchase might cost $63.24/mo for 12 months at 15% APR. A down payment may be required. Subject to eligibility check and approval. Payment options depend on your purchase amount. Estimated payment excluded taxes and shipping fees. Paid interest is nonrefundable. Actual terms may vary. Payment options through Affirm are provided by these lending partners: affirm.com/lenders. Visit affirm.com/help for more info.
(Data Science Enthusiast)
Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.