Study material for exam 70-773 Analyzing Big Data with Microsoft R

By: Daniel Calbimonte   |   Comments (2)   |   Related: More > Professional Development Certifications

Problem

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?

Solution

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
  • Microsoft R
  • Data Import
  • Importing text data in Microsoft R Server
  • Access to Database Management Systems (DBMS)
  • Package ‘RODBC’
  • Using R to perform FileSystem Operations on Azure Data Lake Store
  • R Data Import/Export
• Summarize data
  • Frequencies and Crosstabs
  • R - Exploring Data (part 3) - Univariate Summaries
  • RevolutionAnalytics/dplyrXdf
  • 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
  • ggplot2
  • rxSummary: Object Summaries
  • rxCube: Cross Tabulation
  • rxHistogram: Histogram
  • rxLinePlot: Line Plot

Process big data

• Process data with rxDataStep
  • rxDataStep: Data Step for RevoScaleR data sources
  • Date-Time Classes
• Perform complex transforms that use transform functions
  • Transform an Object, for Example a Data Frame
  • Package ‘lubridate’
  • Available CRAN Packages By Date of Publication
  • RevoScaleR Functions
  • Data transformations using RevoScaleR functions (Microsoft R)
• Manage data sets
  • How to sort data using rxSort (Microsoft R)
  • How to merge data using rxMerge (Microsoft R)
• Process text using RML packages
  • Package ‘rlm’
  • Using the MicrosoftML Package with SQL Server
  • R Server 9 Adds Machine Learning to Work with Your Data Where It Lives

Build predictive models with ScaleR

• Estimate linear models
  • rxLinMod: Linear Models
  • rxGlm: Generalized Linear Models
  • rxLogit: Logistic Regression
  • rxTweedie: Tweedie Generalized Linear Models
  • Package ‘FSelector’
• Build and use partitioning models
  • Estimating Decision Tree Models
  • Estimating Decision Forest Models
  • Estimating Models Using Stochastic Gradient Boosting
  • Package ‘rpart’
• Generate predictions and residuals
  • Fitting Logistic Regression Models
  • Distributed and parallel computing with ScaleR in Microsoft R
  • Estimating Decision Forest Models
  • Fitting Linear Models
  • rxDForest: Parallel External Memory Algorithm for Classification and Regression Decision Forests
• Evaluate models and tuning parameters
  • Distributed and parallel computing with ScaleR in Microsoft R
  • Estimating Decision Tree Models
  • varImpPlot
  • Judging predictive performance
  • MAPE
  • PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R
• Create additional models using RML packages
  • Introduction to One-class Support Vector Machines
  • One-Class Support Vector Machine
  • Multiclass Logistic Regression
  • Two-Class Boosted Decision Tree
  • Two-Class Neural Network
  • Simple 3-Layer Neural Network for MNIST Handwriting Recognition
  • Evaluation.NDCG
  • Fitting a Neural Network in R; neuralnet package

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
  • Distributed and parallel computing with ScaleR in Microsoft R
  • RxLocalSeq: Generate Local Compute Context
  • RxLocalParallel: Generate Local Parallel Compute Context
• 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
  • Lesson 5: Deploy and Use the Model (Data Science End-to-End Walkthrough)
  • Getting Started with the AzureML Package
  • Package ‘AzureML’
  • About DeployR

Next Steps

• 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:
  • Exam 70-773
  • 70-773 Analyzing Big Data with Microsoft R Certification Exam
  • Skills measured in Exam 70-773

About the author

Daniel Calbimonte is a Microsoft SQL Server MVP, Microsoft Certified Trainer and 6-time Microsoft Certified IT Professional. Daniel started his career in 2001 and has worked with SQL Server 6.0 to 2022. Daniel is a DBA as well as specializes in Business Intelligence (SSIS, SSAS, SSRS) technologies.

This author pledges the content of this article is based on professional experience and not AI generated.

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