Study material for exam 70-773 Analyzing Big Data with Microsoft R
By: Daniel Calbimonte | Updated: 2017-07-21 | Comments (2) | Related: More > Professional Development Certifications
I have been reading your tips on the SQL Server certification series. Great job with the series. I am now looking for information about the 70-773 exam focusing on Big Data and Microsoft R. Do you have information about this exam?
Yes, we have. This time we will talk about Big Data and Microsoft R.
Who should take this exam?
This exam is oriented to DBAs, Data Scientist, Data architects, Data Analysists, Data Developers or professional who want to learn or who want to be certified in Big Data.
What Microsoft Certifications are related to this exam?
This exam is mandatory to earn the MCSA in machine learning (Microsoft Certified Solutions Associate). You can also become a MCP (Microsoft Certified Professional) with this exam.
Is the exam easy?
If you do not have previous experience with R, big data and machine learning, you do not have any chance of passing this exam.
What is R?
It is a GNU project that consist of a language for statistics, graphics, etc. Visual Studio and SQL Server support R.
What is the relationship between R and SQL Server?
SQL Server now includes the R Services (Machine learning services in SQL Server 2017). SQL Server now enables you to perform Data Science tasks.
Which books would you recommend for this exam?
The following courses may be useful:
- The Book of R: A First Course in Programming and Statistics
- R in Action: Data Analysis and Graphics with R
- The Art of R Programming: A Tour of Statistical Software Design
- R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics
- Advanced R (Chapman & Hall/CRC The R Series)
- R Packages: Organize, Test, Document, and Share Your Code 1st Edition
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data 1st
- Microsoft Azure Essentials Azure Machine Learning
- Microsoft Azure Machine Learning with Stock Data
- SPSS Statistics for Dummies 3rd Edition
- Learning SAS(R) by Example:: A Programmer's Guide
- Big Data Analytics with R and Hadoop
- Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale (Addison-Wesley Data & Analytics)
Are there some courses for this exam?
Yes, the following courses will be useful:
- R Courses
- Machine learning courses
- Udemy Big Data Courses
- Pluralsight Courses
- R Programming
- Data Camp Courses
- EDX courses
- Try R
- R Studio
Can you provide some links to study, for this exam?
Yes, here you have some useful links:
Read and explore big data
- Read data with R Server
- Summarize data
- Frequencies and Crosstabs
- R - Exploring Data (part 3) - Univariate Summaries
- rxCrossTabs: Cross Tabulation
- rxCube: Cross Tabulation
- Introducing the dplyrXdf package
- rxQuantile: Approximate Quantiles for .xdf Files and Data Frames
- Comparison of Base R and ScaleR Functions
- Visualize data
Process big data
- Process data with rxDataStep
- Perform complex transforms that use transform functions
- Manage data sets
- Process text using RML packages
Build predictive models with ScaleR
- Estimate linear models
- Build and use partitioning models
- Generate predictions and residuals
- Evaluate models and tuning parameters
- Create additional models using RML packages
Use R Server in different environments
- Use different compute contexts to run R Server effectively
- RxHadoopMR: Generate Hadoop Map Reduce Compute Context
- RxSpark: Create Spark compute context, connect and disconnect a Spark application
- RxLocalSeq: Generate Local Compute Context
- RxLocalParallel: Generate Local Parallel Compute Context
- RxTextData: Generate Text Data Source Object
- Debugging with RStudio
- Get started with PemaR function in Microsoft R
- Optimize tasks by using local compute contexts
- Perform in-database analytics by using SQL Server
- Deploying to SQL Server (demo)
- SQL Server 2016 R Services: Executing R code in SQL Server
- Step 6: Operationalize the Model (In-Database Advanced Analytics Tutorial)
- Lesson 3: Create Data Features (Data Science End-to-End Walkthrough)
- SQL Server Configuration (R Services)
- SQL Server 2016 R Services: Executing R code in Revolution R Enterprise
- R and Data Optimization (R Services)
- SQL Server Profiler
- Implement analysis workflows in the Hadoop ecosystem and Spark
- Practice data import and exploration on Apache Spark
- Get started using R Server on HDInsight
- Hadoop tutorial: Get started using Hadoop in HDInsight
- Analyzing Big Data with Microsoft R Server
- Scalable Machine Learning and Data Science with Microsoft R Server and Spark
- Distributed and parallel computing with ScaleR in Microsoft R
- Deploy predictive models to SQL Server and Azure Machine Learning
- R machine learning and Azure are new and very exciting technologies. We hope you enjoy studying this fascinating topic. If you have any questions, please write them in the comments section below.
- For more information about this exam, refer to these links:
Last Updated: 2017-07-21
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