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Reference: https://www.microsoft.com/en-us/learning/exam-70-773.aspx
Analyzing Big Data with Microsoft R 70-773 Exam
Analyzing Big Data with Microsoft R 70-773 Exam been designed in order to test expertise and technical knowledge in designing and implementing Microsoft services in order to obtain value from the available data set. It examines the candidate's ability to execute Microsoft Big Data Services according to architecture practices. The Analyzing Big Data with Microsoft R 70-773 Exam is high level exam and it requires in-depth knowledge of data analytics concepts, before you go ahead and write this certification you must require some prerequisites Candidates for this exam are data scientists or analysts who process and analyze data sets larger than memory using R.
Candidates should have experience with R, familiarity with data structures, familiarity with basic programming concepts (such as control flow and scope), and familiarity with writing and debugging R functions. Candidates should be familiar with common statistical methods and data analysis best practices. Candidates should also have a high-level understanding of data platforms, such as the Hadoop ecosystem, SQL Server, and core T-SQL capabilities. We think our Analyzing Big Data with Microsoft R 70-773 Exam Practice Test Paper and Dumps will provide you 100% confidence to make you appear for Microsoft 70-773 Exam
This is the list of the contents in our Analyzing Big Data with Microsoft R 70-773 Practice Test:
- Hadoop Ecosystem and Spark.
- Compute concepts to Run R Server.
- Models with ScaleR.
- Data Processing with RxDataStep.
- Data reading with R Server.
Analyzing Big Data with Microsoft R 70-773 Dumps will include below mentioned topics with Exam focused percentage
- Read and explore big data: 20-25%
- Use R Server in different environments: 20-25%
- Process big data: 20-25%
- Build predictive models with Scale R: 20-25%
- Implement Domain Name System (DNS): 15-20%
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MICROSOFT 70-773 Exam Reference, Microsoft Documents
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Microsoft 070-773 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Perform data analysis with R | - Descriptive statistics and exploratory data analysis - Work with large datasets - Apply statistical methods in R |
| Topic 2: Integrate R with Microsoft data platforms | - Use R in Azure Machine Learning environments - Use R with SQL Server |
| Topic 3: Visualize and communicate results | - Reporting and dashboards - Data visualization with R packages (ggplot2, etc.) |
| Topic 4: Build and evaluate predictive models | - Feature engineering in R - Model training and validation - Regression and classification models |
| Topic 5: Prepare and clean data for analysis | - Handle missing and inconsistent data - Clean and preprocess datasets - Import and transform data using R |
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