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MICROSOFT 70-773 Exam Reference, Microsoft Documents
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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.
- Data reading with R Server.
- Data Processing with RxDataStep.
- Models with ScaleR.
- Compute concepts to Run R Server.
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Microsoft 070-773 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Perform data analysis with R | - Work with large datasets - Apply statistical methods in R - Descriptive statistics and exploratory data analysis |
| Prepare and clean data for analysis | - Handle missing and inconsistent data - Import and transform data using R - Clean and preprocess datasets |
| Build and evaluate predictive models | - Regression and classification models - Model training and validation - Feature engineering in R |
| Visualize and communicate results | - Data visualization with R packages (ggplot2, etc.) - Reporting and dashboards |
| Integrate R with Microsoft data platforms | - Use R with SQL Server - Use R in Azure Machine Learning environments |
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