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AdaBoost
After watching this video; you will be able to use the boosting function from the adabag package in R.
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Bagging
After watching this video; you will be able to use the bagging function from the adabag package in R.
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Calculating Confidence Intervals
In this video; Steve Scott demonstrates how to use the confint function on a dataset to calculate the confidence interval in R programs.
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Categorize Continuous Variables
In this video; Steve Scott demonstrates how to categorize continuous data in R.
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Classification Trees with rpart
After watching this video; you will be able to create a basic classification tree using rpart in R.
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Classification Trees with the tree Package
After watching this video; you will be able to create a basic classification tree with the trees package in R.
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Cleaning Data
In this video; Steve Scott demonstrates how to segregate data in R.
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Clustering Large Applications (Clara)
After watching this video; you will be able to use the clusplot function to perform a cluster plot on a clara object in R.
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Combining Random Forests
After watching this video; you will be able to combine random forest ensembles into a single object in R.
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Data Frames
In this video; Steve Scott demonstrates how to create and access properties of a data frame in R.
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Examples of Data Science
Data Science employs techniques from different fields to extract knowledge from data. In this video; Steve Scott outlines common problems addressed by data scientists.
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Fitting Generalized Linear Models
In this video; Steve Scott demonstrates how to create a generalized linear model (glm) in R programs.
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Foreach Looping
After watching this video; you will be able to use the foreach loop in R.
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Fuzzy C-Means Clustering
After watching this video; you will be able to perform a fuzzy C-Means clustering from the e1071 package in R.
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Hierarchical Clustering with corclust
After watching this video; you will be able to use the corclust function in the klaR package in R.
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Import JSON Data
In this video; Steve Scott demonstrates how to import JSON data in R.
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Imputation using the Hmisc Package
In this video; Steve Scott demonstrates how to perform imputations by using the Hmisc package in R.
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K-Means Clustering
After watching this video; you will be able to perform k-means clustering on data in R.
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K-Nearest Neighbor Classification
After watching this video; you will be able to perform a K-Nearest Neighbor Classification in R.
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Linear Discriminant Analysis (LDA)
After watching this video; you will be able to use the lda function in R.
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Linear Modeling
In this video; Steve Scott demonstrates how to compute a linear model in R.
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Loess Regression
After watching this video; you will be able to perform a curve fit using the Loess method in R.
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Merging Data
In this video; Steve Scott describes how to merge two data frames in R.
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Mixture Discriminant Analysis (MDA)
After watching this video; you will be able to perform a MDS using the mda package in R.
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Modeling for Data Science
R is a powerful statistical program and programming language. In this video; Steve Scott identifies the most frequently used functions used for data modeling and analysis in R.
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Multidimensional Scaling
After watching this video; you will be able to perform classical multidimensional scaling using cmdscale in R.
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Overlay Density Plots
After watching this video; you will be able to create an overlay density plot using the caret package in R.
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Partial Least Squares Regression (PLS)
After watching this video; you will be able to perform a PLS regression using the pls package in R.
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Partitioning Around Medoids (PAM)
After watching this video; you will be able to use the clusplot function to perform a cluster plot on a pam cluster in R.
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Plotting Linear Models
Plotting linear models within statistical data analysis is used for the purpose of identifying trends; by discovering relationships among variables and summarizing considerations of how the data relates to the underlying population. In this video; Steve Scott demonstrates how to plot linear models in R using ggplot2's qplot function.
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Quadratic Discriminant Analysis (QDA)
After watching this video; you will be able to use the qda function from the MASS package in R.
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Random Forests for Unsupervised Proximity Classification
After watching this video; you will be able to use random forests for unsupervised classification in R.
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Regression Trees with rpart
After watching this video; you will be able to create a basic regression tree using rpart in R.
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Reshaping Data
Reshaping data is the process of rearranging data into a format appropriate for a specific type of analysis. In this video; Steve Scott demonstrates how to reshape data in R.
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Scatterplot 3D Visualization
After watching this video; you will be able to create a 3D Scatterplot in R.
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Scatterplot Matrix
After watching this video; you will be able to create a scatterplot matrix using the caret package in R.
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Selecting K for kmeans Clustering
After watching this video; you will be able to use the kselection package to select k for a kmeans clustering in R.
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Simple Random Imputation
In this video; Steve Scott demonstrates how to perform basic imputations in R.
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Smoothing Splines
After watching this video; you will be able to plot a smoothing spline from the splines packages in R.
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Summary Statistics
In this video; Steve Scott describes how to use the structure; summary; and head functions in RStudio to analyze imported sample datasets.
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Supervised and Unsupervised Learning
After watching this video; you will be able to distinguish between supervised and unsupervised learning.
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Support Vector Machines (SVMs)
After watching this video; you will be able to use the SVM function from the e1071 library in R.
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The Predict Function
In this video; Steve Scott demonstrates how to use the predict function to predict values based on a linear model object in R.
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The R fitted Function
In this video; Steve Scott demonstrates how to use the fitted function on a dataset in R.
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The R forecast Package
In this video; Steve Scott demonstrates how to use the forecast package on a built-in time series dataset in R.
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The R residuals Function
In this video; Steve Scott demonstrates how to use the residuals function on a dataset to extract the model residuals in R programs.
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The randomForest Package
After watching this video; you will be able to use the randomforest package for classification in R.
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The TukeyHSD test
In this video; Steve Scott demonstrates how to use the TukeyHSD function in R.
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The Variance-Covariance Matrix
In this video; Steve Scott demonstrates how to use the vcov function on a dataset to retrieve the variance-covariance matrix in R.
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Transposing Data
In this video; Steve Scott demonstrates how to transpose data in R.
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t-test
In this video; Steve Scott demonstrates how to perform a t-test on a randomly generated dataset in R.
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What is Data Science?
In this video; Steve Scott describes and defines data science as well as the types of skills a data scientist use.
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HDInsight: Ambari Views
After watching this video, you will be able to use Ambari Views.
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HDInsight: HiveOL
After watching this video, you will be able to use HiveOL.
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HDInsight: Hive Tables
After watching this video, you will be able to use Hive tables.
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HDInsight: Visualizing Data with Zeppelin
After watching this video, you will be able to use Zeppelin to visualize data.
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HDInsight: Parsing Files Using Hive
After watching this video, you will be able to describe how to parse files such as CSV files with Hive.
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HDInsight: ORC for Caching
After watching this video, you will be able to use ORC for caching.
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HDInsight: Apache Phoenix in HDInsight
After watching this video, you will be able to use Apache Phoenix in HDInsight.
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HDInsight: Apache Phoenix Grammar
After watching this video, you will be able to use Apache Phoenix Grammar for queries.
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HDInsight: Use Spark SQL Data Analysis
After watching this video, you will be able to use data analysis for Spark SQL.
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HDInsight: Introduction to Apache Phoenix
After watching this video, you will be able to describe Apache Phoenix.
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HDInsight: Azure Resource Manager (ARM) PowerShell
After watching this video, you will be able to describe Azure resource manager (ARM) PowerShell.
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HDInsight: AdlCopy
After watching this video, you will be able to describe AdlCopy.
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HDInsight: Run MapReduce
After watching this video, you will be able to run MapReduce jobs.
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HDInsight: Azure Command Line Interface (CLI)
After watching this video, you will be able to use Azure CLI specifically to provision a cluster and perform routine small writes on a continuous basis.
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HDInsight: AzCopy
After watching this video, you will be able to describe AzCopy.
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HDInsight: Private Virtual Network
After watching this video, you will be able to create a HDInsight cluster in a private virtual network.
-
HDInsight: Custom Metastore
After watching this video, you will be able to create a HDInsight cluster with a custom metastore.
-
HDInsight: HDInsight Clusters
After watching this video, you will be able to identify HDInsight cluster types.
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HDInsight: HDInsight Cluster Script Actions
After watching this video, you will be able to use scripts to customize a cluster.
-
HDInsight: Manage Metastore
After watching this video, you will be able to manage metastore upgrades on HDInsight.
-
HDInsight: Hive Table Joins
After watching this video, you will be able to use join tables with Hive.
-
HDInsight: Python UDFs with Hive and Apache Pig
After watching this video, you will be able to describe Python UDFs with Hive and Apache Pig.
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HDInsight: Apache Pig Scripts
After watching this video, you will be able to design scripts with Apache Pig.
-
HDInsight: Hive Query Bottlenecks
After watching this video, you will be able to identify Hive query bottlenecks.
-
HDInsight: Java UDFs with Hive
After watching this video, you will be able to describe Java UDFs with Hive.
-
HDInsight: Manage Spark Resources
After watching this video, you will be able to use YARN to share resources between Spark applications.
-
HDInsight: Spark Performance
After watching this video, you will be able to describe how to optimize Spark performance.
-
HDInsight: Hive Storage Formats
After watching this video, you will be able to identify Hive storage formats.
-
HDInsight: Use Hive Tables
After watching this video, you will be able to use Hive tables.
-
HDInsight: Spark External Data Sources
After watching this video, you will be able to connect to external Spark data sources.
-
HDInsight: Spark Datasets
After watching this video, you will be able to describe Spark dataset programs and how to add custom Python and Scala code.
-
HDInsight: Tuning Spark Performance
After watching this video, you will be able to describe how to tune Spark performance using executors, partitioning, and bucketing.
-
HDInsight: Create Clusters Using Azure Data Factory
After watching this video, you will be able to create a cluster using Azure Data Factory (ADF).
-
HDInsight: Storage with Azure Data Factory Activity
After watching this video, you will be able to connect a storage account to a cluster using the Azure Data Factory (ADF).
-
HDInsight: Spark SQL Query Graph
After watching this video, you will be able to identify bottlenecks using Spark SQL query graphs.
-
HDInsight: Introduction to Azure Data Factory (ADF)
After watching this video, you will be able to describe Azure Data Factory (ADF).
-
HDInsight: Integrating Hive Metastore with Spark
After watching this video, you will be able to describe how to share metastores and storage accounts between clusters such as Hive and Spark.
-
HDInsight: On-demand Clusters with Azure Data Factory
After watching this video, you will be able to create an on-demand Hadoop cluster in HDInsight.
-
HDInsight: Apache Oozie with HDInsight
After watching this video, you will be able to use Apache Oozie in HDInsight.
-
HDInsight: Introduction to Spark SQL
After watching this video, you will be able to describe Spark SQL in HDInsight.
-
HDInsight: Spark SQL Queries
After watching this video, you will be able to run queries using Spark SQL.
-
HDInsight: Comparing Storage Types
After watching this video, you will be able to compare different storage types for data pipeline.
-
HDInsight: Connect to External Data Sources
After watching this video, you will be able to connect to external data sources.
-
HDInsight: Spark Clusters and BI Tools
After watching this video, you will be able to describe how to use BI tools with Apache Spark on HDInsight.
-
HDInsight: Spark SQL Join Types
After watching this video, you will be able to optimize Spark SQL join types.
-
HDInsight: Iterative Queries with Spark DataFrames
After watching this video, you will be able to cache Spark DataFrames.
-
HDInsight: Parquet Files
After watching this video, you will be able to read and write Parquet files in Spark.
-
HDInsight: Spark Thrift Server
After watching this video, you will be able to manage Spark Thrift server.
-
HDInsight: Spark Storage Types
After watching this video, you will be able to describe the different storage types for interactive queries.
-
HDInsight: Interactive Hive Clusters and BI Tools
After watching this video, you will be able to connect Interactive Hive clusters to BI tools.
-
HDInsight: Run Spark SQL Queries
After watching this video, you will be able to run Spark SQL queries.
-
HDInsight: Introduction to Interactive Hive
After watching this video, you will be able to describe Interactive Hive.
-
HDInsight: Hive LLAP
After watching this video, you will be able to describe Hive LLAP and how to enable it through Hive settings.
-
HDInsight: Apache Parquet
After watching this video, you will be able to describe Apache Parquet.
-
HDInsight: Interactive Livy Sessions
After watching this video, you will be able to manage interactive Livy sessions.
-
HDInsight: Introduction to Jupyter and Apache Zeppelin
After watching this video, you will be able to describe Jupyter and Apache Zeppelin.
-
HDInsight: Merge DataFrames Using Spark SQL
After watching this video, you will be able to merge DataFrames using Spark SQL.
-
HDInsight: Interactive Querying Using Hive
After watching this video, you will be able to describe what interactive querying is and how its used with Hive.
-
HDInsight: HDInsight Domain-joined Clusters
After watching this video, you will be able to create a HDInsight domain-joined cluster.
-
HDInsight: HDInsight Managed Disks
After watching this video, you will be able to configure managed disks on HDInsight.
-
HDInsight: HDInsight Clusters Using PowerShell
After watching this video, you will be able to use PowerShell to provision an HDInsight cluster.
-
HDInsight: HDInsight Clusters Using Azure CLI
After watching this video, you will be able to use Azure CLI tools to provision an HDInsight cluster.
-
HDInsight: HDInsight Clusters Using Azure Portal
After watching this video, you will be able to use Azure Portal to provision an HDInsight cluster.
-
HDInsight: HDInsight .NET SDK
After watching this video, you will be able to use .NET SDK to provision an HDInsight cluster.
-
HDInsight: Ambari Services
After watching this video, you will be able to use Ambari to start and stop services.
-
HDInsight: Ambari Alerts and Metrics
After watching this video, you will be able to manage alerts and metrics using Ambari.
-
HDInsight: HDInsight VNet Peering
After watching this video, you will be able to configure vNet peering on HDInsight.
-
HDInsight: Ambari Configuration
After watching this video, you will be able to describe Ambari configuration.
-
HDInsight: Hadoop Heap Dumps
After watching this video, you will be able to describe how to enable heap dumps for Hadoop services.
-
HDInsight: Cluster-level Debugging
After watching this video, you will be able to describe cluster-level debugging.
-
HDInsight: Azure Table Storage
After watching this video, you will be able to describe Azure Table storage.
-
HDInsight: Azure Data Lake
After watching this video, you will be able to describe Azure Data Lake and how to store data.
-
HDInsight: Azure Blob Storage
After watching this video, you will be able to describe Azure Blob storage and how to store data.
-
HDInsight: Provision HDInsight Cluster
After watching this video, you will be able to provision HDInsight cluster.
-
HDInsight: On-premise and Cloud Data
After watching this video, you will be able to copy data from cloud to on-premise and vice versa.
-
HDInsight: Ingest Data Using AzCopy
After watching this video, you will be able to describe how to ingest data in Apache Hive and Apache Spark using AzCopy.
-
HDInsight: Ingest Data Using Apache Sqoop
After watching this video, you will be able to describe how to ingest data in Apache Hive and Apache Spark using Apache Sqoop.
-
HDInsight: Ingest Data Using ADF
After watching this video, you will be able to ingest data in Apache Hive and Apache Spark using Application Development Framework (ADF).
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HDInsight: YARN ResourceManager UI
After watching this video, you will be able to describe how YARN ResourceManager UI works.
-
HDInsight: YARN Command Line Interface
After watching this video, you will be able to use the YARN CLI to kill an HDInsight job.
-
HDInsight: Ingest Data Using AdlCopy
After watching this video, you will be able to describe how to ingest data in Apache Hive and Apache Spark using AdlCopy.
-
HDInsight: Apache Hadoop YARN
After watching this video, you will be able to describe what Apache Hadoop YARN is and how it works.
-
HDInsight: Azure Operations Management Suite (OMS)
After watching this video, you will be able to describe the Azure Operations Management Suite (OMS).
-
HDInsight: HDInsight Users, Groups, and Permissions
After watching this video, you will be able to manage HDInsight users, groups, and permissions.
-
HDInsight: HDInsight Job Logs
After watching this video, you will be able to view logs for different types of HDInsight jobs.
-
HDInsight: Debug HDInsight Jobs
After watching this video, you will be able to debug jobs such as Hadoop and Spark jobs.
-
HDInsight: Securing HDInsight Clusters
After watching this video, you will be able to configure Kerberos on HDInsight.
-
HDInsight: HDInsight Service Accounts
After watching this video, you will be able to configure service accounts on HDInsight.
-
HDInsight: HDInsight SSH Tunneling
After watching this video, you will be able to describe how to implement SSH tunneling.
-
HDInsight: Introduction to Apache Hive
After watching this video, you will be able to describe Apache Hive.
-
HDInsight: Introduction to Apache Pig
After watching this video, you will be able to describe Apache Pig.
-
HDInsight: HDInsight Restricted Access
After watching this video, you will be able to identify how to restrict access to HDInsight data.
-
HDInsight: Manage HDInsight Users
After watching this video, you will be able to manage HDInsight users.
-
HDInsight: Hive Performance
After watching this video, you will be able to describe how to improve Hive performance.
-
HDInsight: XML with Hive
After watching this video, you will be able to use XML files with Hive.
-
HDInsight: External Hive Tables
After watching this video, you will be able to define external Hive tables.
-
HDInsight: Load Data into Hive Tables
After watching this video, you will be able to identify how to load data into Hive tables.
-
HDInsight: JSON with Hive
After watching this video, you will be able to describe how to use JSON files with Hive.
-
HDInsight: What Is HDInsight?
After watching this video, you will be able to describe HDInsight and what it is used for.
-
HDInsight: HDInsight Configuration
After watching this video, you will be able to manage HDInsight configuration.
-
HDInsight: MapReduce
After watching this video, you will be able to examine MapReduce examples such as wordcount or wordmean.
-
HDInsight: What Is Hadoop?
After watching this video, you will be able to describe Hadoop and the MapReduce process.
-
HDInsight: Install PuTTY
After watching this video, you will be able to install PuTTY on windows.
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Analyzing Big Data with Microsoft R: rxBTrees
After watching this video, you will be able to recognize Microsoft R's rxBTrees function and its important arguments that implement a stochastic gradient-boosting algorithm.
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Analyzing Big Data with Microsoft R: Neural Networks
After watching this video, you will be able to define essentials of Neural Networks algorithms and Microsoft R's xNeuralNet algorithm.
-
Analyzing Big Data with Microsoft R: Model Ensembles Metrics
After watching this video, you will be able to list important model ensembles metrics.
-
Analyzing Big Data with Microsoft R: K-means Clustering
After watching this video, you will be able to define k-means clustering analysis and its use cases.
-
Analyzing Big Data with Microsoft R: rxKmeans
After watching this video, you will be able to define Microsoft R's rxKmeans function and its important arguments used to conduct k-means clustering analysis.
-
Analyzing Big Data with Microsoft R: rxKmeans Function
After watching this video, you will be able to identify key features of rxKmeans function.
-
Analyzing Big Data with Microsoft R: What Is Ensemble Learning?
After watching this video, you will be able to describe ensemble learning and its key features.
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Analyzing Big Data with Microsoft R: rxEnsemble
After watching this video, you will be able to recognize rxEnsemble function and its important arguments used for ensemble learning.
-
Analyzing Big Data with Microsoft R: Random Forest
After watching this video, you will be able to define essentials of Random Forests algorithms and Microsoft R's rxFastForest function.
-
Analyzing Big Data with Microsoft R: Decision Forest
After watching this video, you will be able to define essentials of Decision Forests algorithms and Microsoft R's rxFastForest function.
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Analyzing Big Data with Microsoft R: rxFastTrees
After watching this video, you will be able to recognize Microsoft R's rxFastTrees function and its important arguments that implement a gradient-boosting algorithmrecognize Microsoft R's rxFastTrees function and its important arguments that implement a g
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Analyzing Big Data with Microsoft R: Unsupervised Learning
After watching this video, you will be able to describe essentials of unsupervised learning.
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Analyzing Big Data with Microsoft R: Introduction to Clustering Analysis
After watching this video, you will be able to recognize main types of clustering analysis techniques.
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Analyzing Big Data with Microsoft R: rxDTree Function
After watching this video, you will be able to identify key features of rxDTree function.
-
Analyzing Big Data with Microsoft R: Naive Bayes Classifier
After watching this video, you will be able to describe the Bayes’ Theorem and Naive Bayes classifier.
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Analyzing Big Data with Microsoft R: Support Vector Machines
After watching this video, you will be able to recognize essentials of Support Vector Machines.
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Analyzing Big Data with Microsoft R: rxOneClassSvm
After watching this video, you will be able to describe rxOneClassSvm function and its important arguments.
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Analyzing Big Data with Microsoft R: One-class Support Vector Machine
After watching this video, you will be able to identify important aspects of One-class Support Vector Machines.
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Analyzing Big Data with Microsoft R: Regression Trees
After watching this video, you will be able to describe regression tree analysis and its use cases.
-
Analyzing Big Data with Microsoft R: Classification Trees
After watching this video, you will be able to describe classification tree analysis and its use cases.
-
Analyzing Big Data with Microsoft R: rxDTree
After watching this video, you will be able to describe rxDTree function and its important arguments.
-
Analyzing Big Data with Microsoft R: Visualizing Decision Trees
After watching this video, you will be able to identify options for visualization of decision trees in Microsoft R.
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Analyzing Big Data with Microsoft R: Microsoft R's Classification Algorithms
After watching this video, you will be able to identify important machine learning algorithms that are available in Microsoft R.
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Analyzing Big Data with Microsoft R: Logistic Regression Functions
After watching this video, you will be able to recognize important functions for constructing and evaluating logistic regression models.
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Analyzing Big Data with Microsoft R: Introduction to Classification Algorithms
After watching this video, you will be able to recognize essentials of classification algorithms.
-
Analyzing Big Data with Microsoft R: Microsoft R and Linear Regression
After watching this video, you will be able to describe Microsoft R's important functions and arguments for modeling linear regressions.
-
Analyzing Big Data with Microsoft R: Linear Regression Interpretation
After watching this video, you will be able to recall how to interpret linear regression results.
-
Analyzing Big Data with Microsoft R: Nonlinear Regression
After watching this video, you will be able to describe nonlinear regression analysis and identify key differences between linear and nonlinear regression analysis.
-
Analyzing Big Data with Microsoft R: Linear Regression Functions
After watching this video, you will be able to identify important functions for constructing and evaluating linear regression models.
-
Analyzing Big Data with Microsoft R: Introduction to Logistic Regression
After watching this video, you will be able to describe fundamentals of logistic regression and its use cases.
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Analyzing Big Data with Microsoft R: Logistic Model Accuracy Measurement
After watching this video, you will be able to identify important metrics for measuring the accuracy of logistic regression models.
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Analyzing Big Data with Microsoft R: Logistic Regression Interpretation
After watching this video, you will be able to recognize how to interpret logistic regression results.
-
Analyzing Big Data with Microsoft R: Microsoft R and Logistic Regression
After watching this video, you will be able to recall Microsoft R's important functions and arguments for modeling logistic regressions.
-
Analyzing Big Data with Microsoft R: Using Summary Statistics Functions
After watching this video, you will be able to identify key functions that are used to produce summary statistics.
-
Analyzing Big Data with Microsoft R: Introduction to Data Visualization
After watching this video, you will be able to describe data visualization and its importance.
-
Analyzing Big Data with Microsoft R: Data Visualization with R
After watching this video, you will be able to identify important R functions and packages for data visualization.
-
Analyzing Big Data with Microsoft R: rxHistograms
After watching this video, you will be able to recall how to create histograms in Microsoft R.
-
Analyzing Big Data with Microsoft R: rxLinePlots
After watching this video, you will be able to specify how to create line plots in Microsoft R.
-
Analyzing Big Data with Microsoft R: Using Data Visualization Functions
After watching this video, you will be able to identify key functions that are used to visualize data.
-
Analyzing Big Data with Microsoft R: Introduction to Linear Regression
After watching this video, you will be able to describe fundamentals of linear regression and its use cases.
-
Analyzing Big Data with Microsoft R: Linear Model Accuracy Measurement
After watching this video, you will be able to recognize important metrics for measuring the accuracy of linear regression models.
-
Analyzing Big Data with Microsoft R: rxSummary Function
After watching this video, you will be able to recognize the rxSummary function and its use cases.
-
Analyzing Big Data with Microsoft R: rxQuantile Function
After watching this video, you will be able to identify use cases of the rxQuantile function.
-
Analyzing Big Data with Microsoft R: rxCube Function
After watching this video, you will be able to identify use cases of the rxCube function.
-
Analyzing Big Data with Microsoft R: Summarizing Qualitative Data
After watching this video, you will be able to identify important statistics used for summarizing quantitative data.
-
Analyzing Big Data with Microsoft R: Summarizing Quantitative Data
After watching this video, you will be able to identify important statistics used for summarizing qualitative data.
-
Analyzing Big Data with Microsoft R: Summarizing Bivariate Relationships
After watching this video, you will be able to describe univariate analysis and identify important functions.
-
Analyzing Big Data with Microsoft R: rxCrossTabs Function
After watching this video, you will be able to describe the rxCrossTabs function and its use cases.
-
Python for Data Science: Using Data Containers in Python
After watching this video, you will be able to describe the various Python containers for data management.
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Python for Data Science: Lists and Dictionaries
After watching this video, you will be able to create lists, tuples, and dictionaries with Python to drive data.
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Python for Data Science: Python List Comprehensions
After watching this video, you will be able to use Python list comprehensions to create lists.
-
Python for Data Science: IPython Components
After watching this video, you will be able to describe the IPython shell and shell commands.
-
Python for Data Science: Exploring Jupyter Notebook
After watching this video, you will be able to define Python interactive computational environment and describe its use with data science.
-
Python for Data Science: The Jupyter QT Console
After watching this video, you will be able to run the Jupyter QT Console and familiarize with the basics of its user interface.
-
Python for Data Science: Data Science Architecture
After watching this video, you will be able to describe elements of data science and datasets with various modeling and prediction relationships.
-
Python for Data Science: Data Science Stages
After watching this video, you will be able to recognize the various pipelines in data science and the stages of the data science cycle.
-
Python for Data Science: Python Libraries for Data Science
After watching this video, you will be able to define and describe the various libraries and packages for data analysis.
-
Python for Data Science: Installing Anaconda for Python
After watching this video, you will be able to perform the key steps involved in installing Anaconda including all the necessary packages for this course.
-
Python for Data Science: Using Figures and Subplots
After watching this video, you will be able to use the Python matplotlib library to create and customize multiple plots in a single figure.
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Python for Data Science: Creating Box Plots
After watching this video, you will be able to use the Python matplotlib library to create and customize a box plot.
-
Python for Data Science: Creating Heat Maps
After watching this video, you will be able to use the Python matplotlib library to create and display a heat map.
-
Python for Data Science: Data Merging with pandas DataFrames
After watching this video, you will be able to perform basic merge operations with pandas DataFrames.
-
Python for Data Science: SciPy Overview
After watching this video, you will be able to describe the functionality and use of core packages and sub-packages in the SciPy stack.
-
Python for Data Science: Standardizing Data
After watching this video, you will be able to use the scikit-learn library to perform basic data standardization.
-
Python for Data Science: Normalizing Data
After watching this video, you will be able to use the scikit-learn library to perform basic data normalization.
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Python for Data Science: Performing Linear Regression
After watching this video, you will be able to use the scikit-learn library to perform simple linear regression analysis.
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Python for Data Science: Supervised Learning with scikit-learn
After watching this video, you will be able to perform supervised learning by using the scikit-learn library to perform optical recognition of hand-written digits.
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Python for Data Science: matplotlib Comprehensive 2D Plotting Tool
After watching this video, you will be able to use the Python matplotlib library to plot and display a simple 2D line plot and set its line properties.
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Python for Data Science: Various Forms of Distribution
After watching this video, you will be able to use the SciPy package to describe the various forms of distribution.
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Python for Data Science: Further Integrations in Data Science
After watching this video, you will be able to manage other concepts and processes in data science.
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Python for Data Science: Using Legends and Annotations
After watching this video, you will be able to use the Python matplotlib library to place legends and annotations on a 2D line plot.
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Python for Data Science: Creating a Scatter Plot Matrix
After watching this video, you will be able to use pandas to create a scatter plot matrix.
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Python for Data Science: Additional Python Visualization Tools
After watching this video, you will be able to use the Python matplotlib library to create a 3D plot.
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Python for Data Science: Manipulating Time Series in Python
After watching this video, you will be able to create, slice, and resample time series data in Python.
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Python for Data Science: Working with Timedeltas
After watching this video, you will be able to use pandas to create and manipulate Timedeltas in Python.
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Python for Data Science: Data Cleansing with Python
After watching this video, you will be able to identify key concepts in Python data cleansing.
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Python for Data Science: Data Preprocessing and Text Mining
After watching this video, you will be able to perform data preprocessing and text mining in Python.
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Python for Data Science: Accessing Databases from Pandas
After watching this video, you will be able to use pandas to access a MySQL database.
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Python for Data Science: Creating NumPy Arrays
After watching this video, you will be able to describe different ways of creating NumPy arrays.
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Python for Data Science: Reading and Writing Data Using Pandas
After watching this video, you will be able to describe how Pandas library may be used to read and write various formats of data.
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Python for Data Science: Reading and Writing CSV Data Using Pandas
After watching this video, you will be able to use Pandas library to read data from a CSV file and write data out to a CSV file.
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Python for Data Science: Reading JSON Data
After watching this video, you will be able to use Python's standard JSON package to read JSON data.
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Python for Data Science: Capturing Output in Jupyter Notebook
After watching this video, you will be able to capture Python code output in Jupyter Notebooks.
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Python for Data Science: Debugging and Error Handling
After watching this video, you will be able to use IPython to perform debugging and error management on Python code.
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Python for Data Science: NumPy Overview
After watching this video, you will be able to basic access and usage of the NumPy package in a Python development environment.
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Python for Data Science: NumPy Components
After watching this video, you will be able to describe the various components of NumPy.
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Python for Data Science: NumPy ndarray Objects
After watching this video, you will be able to describe ndarray object attributes.
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Python for Data Science: NumPy Operations
After watching this video, you will be able to describe the various NumPy array operations applicable to data science.
-
Python for Data Science: pandas Data Structures Overview
After watching this video, you will be able to use Pandas to describe its primary data structures.
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Python for Data Science: Hierarchical Indexing with pandas
After watching this video, you will be able to use Pandas to describe hierarchical indexing.
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Python for Data Science: Querying Data in pandas
After watching this video, you will be able to perform basic data query operations on a pandas DataFrame.
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Python for Data Science: Data Aggregation Using pandas DataFrames
After watching this video, you will be able to perform aggregation operations on a pandas DataFrame.
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Python for Data Science: Generating and Parsing Dates Using Pandas
After watching this video, you will be able to use the pandas library to generate and parse date values.
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Python for Data Science: Cleaning Up Data Arrays
After watching this video, you will be able to perform data clean up by handling missing and erroneous data.
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Python for Data Science: Loading a Dataset from a URL
After watching this video, you will be able to download and load a sample dataset into Python from a URL .
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Python for Data Science: Handling Large Datasets
After watching this video, you will be able to load a large dataset as smaller chunks by obtaining an iterator for the dataset.
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Python for Data Science: Python Concepts in Data Science
After watching this video, you will be able to recognize the main concepts in data science using Python.
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Python for Data Science: Basic Functionality of Pandas
After watching this video, you will be able to use Pandas to describe the basic and common functionalities of Pandas for Data Science.
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Data Science Using R: Using substitute, quote, and deparse
After watching this video, you will be able to use the R substitute, quote, and deparse functions.
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Data Science Using R: Exporting a LaTeX Document
After watching this video, you will be able to use the xtable library to export a LaTeX document.
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Data Science Using R: Using ast
After watching this video, you will be able to use the R ast function.
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Data Science Using R: Using class
After watching this video, you will be able to use the R class function.
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Data Science Using R: Using SQL to R Translation
After watching this video, you will be able to translate a SQL query to R syntax.
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Data Science Using R: Exporting HTML Tables
After watching this video, you will be able to use the tableHTML library to create HTML tables in R.
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Data Science Using R: Using str
After watching this video, you will be able to use the R str function.
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Data Science Using R: Lazy Evaluation in R
After watching this video, you will be able to work with lazy evaluation in R.
-
Data Science Using R: Addressing Memory with pryr
After watching this video, you will be able to trace address and reference information in R using pryr.
-
Data Science Using R: Debug with Browser
After watching this video, you will be able to add the browser function to some R code to debug it.
-
Data Science Using R: Implementing Handlers
After watching this video, you will be able to implement handlers for debugging.
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Data Science Using R: Turning Warnings into Errors
After watching this video, you will be able to set your R program to report warnings as errors for debugging.
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Data Science Using R: Debugging with Browser
After watching this video, you will be able to use browser to step through R code.
-
Data Science Using R: Displaying Warnings and Messages
After watching this video, you will be able to use R warning and message functions.
-
Data Science Using R: Programming with Asserts
After watching this video, you will be able to implement asserts in R.
-
Data Science Using R: Using pryr
After watching this video, you will be able to use the pryr library to examine memory use in R.
-
Data Science Using R: Benchmarking in R
After watching this video, you will be able to use the microbenchmark library to benchmark R performance.
-
Data Science Using R: Methods of Defensive Programming
After watching this video, you will be able to identify methods of defensive programming in R.
-
Data Science Using R: Using Traceback
After watching this video, you will be able to use the traceback function to examine the call stack.
-
Data Science Using R: Combine dplyr Verbs
After watching this video, you will be able to manipulate a data set using multiple dplyr verbs.
-
Data Science Using R: Debugging with RStudio
After watching this video, you will be able to debug R code with RStudio.
-
Data Science Using R: Arranging with dplyr
After watching this video, you will be able to order rows in tabular data with dplyr's arrange function.
-
Data Science Using R: Data Manipulation with tidyr
After watching this video, you will be able to identify the features of the tidyr library for data wrangling in R.
-
Data Science Using R: Set Operations with dplyr
After watching this video, you will be able to apply set operations to tables using dplyr.
-
Data Science Using R: Using readr
After watching this video, you will be able to use the readr library to extract csv data.
-
Data Science Using R: Using readxl
After watching this video, you will be able to use the readxl library to extract Excel data.
-
Data Science Using R: Gathering with tidyr
After watching this video, you will be able to use tidyr's gather function.
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Data Science Using R: Separating with tidyr
After watching this video, you will be able to use tidyr's separate function.
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Data Science Essentials: Estimators
After watching this video, you will be able to describe the difference between an unbiased and biased estimator.
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Data Science Essentials: Identify Data Sets by Type
After watching this video, you will be able to identify the given data set descriptions by their types.
-
Data Science Essentials: Introduction to Supervised Learning
After watching this video, you will be able to identify problems in which supervised learning techniques apply.
-
Data Science Essentials: Hypothesis Tests
After watching this video, you will be able to carrying out hypothesis tests and working with p-values.
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Data Science Essentials: Chi-Square
After watching this video, you will be able to apply the chi-square test for categorical values.
-
Data Science Essentials: Working with Predictors
After watching this video, you will be able to use predictors in machine learning.
-
Data Science Essentials: Understanding Logistic Regression
After watching this video, you will be able to apply logistic regression to machine learning problems.
-
Data Science Essentials: Introduction to Unsupervised Learning
After watching this video, you will be able to identify problems in which unsupervised learning techniques apply.
-
Data Science Essentials: Understanding Linear Regression
After watching this video, you will be able to apply linear regression to machine learning problems.
-
Data Science Essentials: Sampling Distributions
After watching this video, you will be able to describe sampling distributions and recognize the central limit theorem.
-
Data Science Essentials: Confidence Intervals
After watching this video, you will be able to define confidence intervals and work with margins of error.
-
Data Science Essentials: Discrete Probability Distributions
After watching this video, you will be able to identify common discrete probability distributions.
-
Data Science Essentials: Merge Two CSV Documents into One
After watching this video, you will be able to use csvjoin to merge two compatible CSV documents into one.
-
Data Science Essentials: Working with Events
After watching this video, you will be able to describe basic properties of outcomes.
-
Data Science Essentials: Working with Probability
After watching this video, you will be able to define probability rules for compound probabilities.
-
Data Science Essentials: Data Formation
After watching this video, you will be able to identify different forms of data.
-
Data Science Essentials: Introduction to Probability
After watching this video, you will be able to describe probability in terms of events and sample space size.
-
Data Science Essentials: Sampling Data
After watching this video, you will be able to apply random sampling to A/B tests.
-
Data Science Essentials: Statistical Measures
After watching this video, you will be able to identify and describe various statistical measures.
-
Data Science Essentials: Continuous Probability Distributions
After watching this video, you will be able identify common continuous probability distributions.
-
Data Science Essentials: Introduction to Bayes Theorem
After watching this video, you will be able to apply bayes theorem and describe how it is used in email spam algorithms.
-
Data Science Essentials: Basic Data Science Math
After watching this video, you will be able to perform basic math operations required by data scientists.
-
Data Science Essentials: Changing CSV Delimiters
After watching this video, you will be able to change delimiters in a csv file from commas to tabs.
-
Data Science Essentials: Normalizing Data
After watching this video, you will be able to normalize data from unstructured sources.
-
Data Science Essentials: Merging XML Data
After watching this video, you will be able to merge separate xml files into a single schema.
-
Data Science Essentials: Aggregating Data
After watching this video, you will be able to aggregate data from csv file into a table of summarized values.
-
Data Science Essentials: Identifying Outliers in Data
After watching this video, you will be able to identify outliers in a data set.
-
Data Science Essentials: Homogenizing Rows
After watching this video, you will be able to insert missing values in a data set.
-
Data Science Essentials: Denormalizing Data
After watching this video, you will be able to denormalize data from a structured source.
-
Data Science Essentials: Pivoting Data Tables
After watching this video, you will be able to use pivot tables to cross tabulate data.
-
Data Science Using R: Selecting with dplyr
After watching this video, you will be able to use dplyr's select function and its features.
-
Data Science Using R: Combining Data Sets with dplyr
After watching this video, you will be able to combine data sets using dplyr's join functions.
-
Data Science Using R: Mutating Data with dplyr
After watching this video, you will be able to mutate tabular data with dplyr to compute new columns.
-
Data Science Using R: Summarizing Data with dplyr
After watching this video, you will be able to use dplyr's summary functions.
-
Data Science Essentials: Linear Algebra Matrix Math
After watching this video, you will be able to perform basic matrix math operations required by data scientists.
-
Data Science Using R: Introduction to Data Wrangling in R
After watching this video, you will be able to recognize common tasks and libraries for data wrangling in R.
-
Data Science Using R: Filtering with dplyr
After watching this video, you will be able to examine subsets of data using dplyr's filtering functions.
-
Data Science Using R: Piping with dplyr
After watching this video, you will be able to use dplyr's pipe operator "%>%" to compose functions.
-
Data Science Using R: Data Wrangling with dplyr
After watching this video, you will be able to identify the features of the dplyr library for data wrangling in R.
-
Data Science Using R: Exploring Data with dplyr
After watching this video, you will be able to use dplyr and related functions to explore data frames.
-
Data Science Essentials: Create a Scatter Plot
After watching this video, you will be able to find an appropriate data set in which a scatter plot represents it visually and plot it.
-
Data Science Essentials: Parsing robots.txt
After watching this video, you will be able to parse robots.txt from a website to decide what should and shouldn't be crawled nor indexed.
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Data Science Essentials: Creating Box Plots
After watching this video, you will be able to use box plots.
-
Data Science Essentials: Creating Network Visualizations
After watching this video, you will be able to create a network visualization.
-
Data Science Essentials: Linear Algebra Vector Math
After watching this video, you will be able to perform basic vector math operations required by data scientists.
-
Data Science Essentials: Visual Data Exploration
After watching this video, you will be able to implement strategies for effective data communication.
-
Data Science Essentials: Creating Bar Charts
After watching this video, you will be able to use bar charts.
-
Data Science Essentials: Creating Histograms
After watching this video, you will be able to use histograms.
-
Data Science Essentials: Creating Scatter Plots
After watching this video, you will be able to use scatter plots.
-
Data Science Essentials: Plotting Line Graphs
After watching this video, you will be able to use line graphs.
-
Data Science Essentials: Correlation Versus Causation
After watching this video, you will be able to describe the difference between correlation and causation.
-
Data Science Essentials: Simpson's Paradox
After watching this video, you will be able to define Simpson's paradox.
-
Data Science Essentials: Choose a Machine Learning Method
After watching this video, you will be able to choose the appropriate machine learning method for the given example problems.
-
Data Science Essentials: Effective Communication and Visualization
After watching this video, you will be able to choose appropriate visualization techniques.
-
Data Science Essentials: Presenting Data
After watching this video, you will be able to communicate data science results informally.
-
Data Science Essentials: Documenting Data Science
After watching this video, you will be able to communicate data science results formally.
-
Data Science Essentials: Using Neural Networks
After watching this video, you will be able to describe fall-forward and back-propagation in neural networks.
-
Data Science Essentials: Support Vector Machines (SVM)
After watching this video, you will be able to describe SVM and their use.
-
Data Science Essentials: Defining Overfitting
After watching this video, you will be able to describe overfitting.
-
Data Science Essentials: Using K-folds Cross Validation
After watching this video, you will be able to apply k-folds cross validation.
-
Data Science Essentials: K-means Clustering
After watching this video, you will be able to describe K-means clustering.
-
Data Science Essentials: Using Cluster Validation
After watching this video, you will be able to define cluster validation.
-
Data Science Essentials: Creating a Bubble Plot
After watching this video, you will be able to create a bubble plot.
-
Data Science Essentials: Creating Interactive Plots
After watching this video, you will be able to create an interactive plot.
-
Data Science Essentials: Defining Underfitting
After watching this video, you will be able to describe underfitting.
-
Data Science Essentials: Using Principal Component Analysis
After watching this video, you will be able to define principal component analysis.
-
Data Science Essentials: Introduction to Errors
After watching this video, you will be able to describe machine learning errors.
-
Data Science Essentials: Using Naive Bayes Classification
After watching this video, you will be able to use naive bayes classification techniques.
-
Data Science Essentials: Working with Decision Trees
After watching this video, you will be able to work with decision trees.
-
Data Science Essentials: Understanding Dummy Variables
After watching this video, you will be able to describe the use of dummy variables.
-
Data Science Essentials: Converting Numbers
After watching this video, you will be able to convert numeric formats within a csv document.
-
Data Science Essentials: Plotting from the Command Line
After watching this video, you will be able to use gnuplot to quickly plot data on the command line.
-
Data Science Essentials: Extracting Text from PDF Files
After watching this video, you will be able to use optical character recognition (OCR) to extract text from a pdf document.
-
Data Science Essentials: Rounding Numbers
After watching this video, you will be able to round floating point decimals to two places within a csv document.
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Data Science Essentials: OCR JPEG Images
After watching this video, you will be able to use optical character recognition (OCR) to extract text from a jpeg image.
-
Data Science Essentials: Exploring CSV Statistics
After watching this video, you will be able to use csvstat to explore values in csv data.
-
Data Science Essentials: Querying CSV Data
After watching this video, you will be able to use csvsql to query csv data like an SQL database.
-
Data Science Essentials: Convert Dates to ISO 8601
After watching this video, you will be able to read various date formats and convert to standard compliant ISO 8601 format.
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Data Science Essentials: Exploring CSV Data
After watching this video, you will be able to use csvgrep to explore data in csv data.
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Data Science Essentials: Converting SQL to CSV
After watching this video, you will be able to extract csv data from SQL.
-
Data Science Essentials: Converting Dates
After watching this video, you will be able to convert basic date formats to standard ISO 8601 format.
-
Data Science Essentials: Filtering PDF Files
After watching this video, you will be able to use pdfgrep to extract data from searchable pdf files.
-
Data Science Essentials: Filtering for Invalid Data
After watching this video, you will be able to detect invalid or impossible data combinations.
-
Data Science Essentials: Dropping Duplicate Data
After watching this video, you will be able to drop duplicate records from data.
-
Data Science Essentials: Working with JPEG Headers
After watching this video, you will be able to extract headers from a jpeg image.
-
Data Science Essentials: Converting XML to JSON
After watching this video, you will be able to convert XML data to json format.
-
Data Science Essentials: Converting CSV to SQL
After watching this video, you will be able to create SQL inserts from csv data.
-
Data Science Essentials: Cull Old Data
After watching this video, you will be able to drop records from a CSV file based on date range.
-
Data Science Essentials: Converting CSV to JSON
After watching this video, you will be able to convert csv data to json format.
-
Data Science Essentials: Replacing Values with sed
After watching this video, you will be able to use sed to replace values in a text data stream.
-
Data Science Essentials: Filtering HTTP Headers
After watching this video, you will be able to parse content types in HTTP headers.
-
Data Science Essentials: Filtering CSV Data
After watching this video, you will be able to use csvcut to filter csv data.
-
Data Science Essentials: Connecting to Remote Data
After watching this video, you will be able to perform a secure shell connection to a remote server.
-
Data Science Essentials: Copying Remote Data
After watching this video, you will be able to copy remote data using a secure copy.
-
Data Science Essentials: Working with Email Headers
After watching this video, you will be able to work with metadata in email headers.
-
Data Science Essentials: Data Filtering Techniques and Tools
After watching this video, you will be able to identify common filtering techniques and tools.
-
Data Science Essentials: Processing Date Formats
After watching this video, you will be able to extract date elements from common date formats.
-
Data Science Essentials: Synchronizing Remote Data
After watching this video, you will be able to synchronize data from a remote server.
-
Data Science Essentials: Exploring HTML Tables
After watching this video, you will be able to download an HTML file and iterate over table elements.
-
Data Science Essentials: Working with HTTP Headers
After watching this video, you will be able to extract data from particular tags in an HTML document.
-
Data Science Essentials: Working with Linux Log Files
After watching this video, you will be able to work with Linux log files.
-
Data Science Essentials: Extracting HTML Data
After watching this video, you will be able to extract data from particular tags in an HTML document.
-
Data Science Essentials: Gathering Metadata
After watching this video, you will be able to distinguish between metadata and data.
-
Data Science Essentials: Explore Your Data Science Needs
After watching this video, you will be able to reflect on problems you would like to solve with data science tools.
-
Data Science Essentials: Basic Data Gathering
After watching this video, you will be able to describe problems and software tools associated with data gathering.
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Data Science Essentials: Extracting Spreadsheet Data with agate
After watching this video, you will be able to use agate to extract data from spreadsheets.
-
Data Science Essentials: Extracting Legacy Data from dBASE Tables
After watching this video, you will be able to use agate to extract tabular data from dbf files.
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Data Science Essentials: Gathering Web Data
After watching this video, you will be able to use curl to gather data from the web.
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Data Science Essentials: Extracting Spreadsheet Data with in2csv
After watching this video, you will be able to use in2csv to convert spreadsheet data to csv format.
-
Data Science Essentials: Counting Words
After watching this video, you will be able to use wc to count words, characters, and lines within a text file.
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Data Science Essentials: Exploring Directory Trees
After watching this video, you will be able to explore a subdirectory tree from the command line.
-
Data Science Essentials: Finding the Top Rows
After watching this video, you will be able to find the top rows by value and percent in a data set.
-
Data Science Essentials: Finding Repeated Records
After watching this video, you will be able to find repeated records in a data set.
-
Data Science Essentials: Determining Word Frequencies
After watching this video, you will be able to use natural language processing to count word frequencies in a text document.
-
Data Science Essentials: Taking Random Samples
After watching this video, you will be able to take random samples from a list of records.
-
Data Science Essentials: Concatenating Log Files
After watching this video, you will be able to use the cat function to concatenate separate logs into a single file.
-
Data Science Essentials: Sorting Text Files
After watching this video, you will be able to sort lines in a text file.
-
Data Science Essentials: Count Word Frequencies in a Classic Book
After watching this video, you will be able to perform a word frequency count on a classic book from Project Gutenberg.
-
Data Science Essentials: Joining CSV Data
After watching this video, you will be able to use csvjoin to concatenate csv data.
-
Data Science Essentials: Data Communication
After watching this video, you will be able to recognize ways to communicate results of your data science.
-
Data Science Essentials: Data Science Pipeline
After watching this video, you will be able to recall the steps in data science analysis.
-
Data Science Essentials: What is Machine Learning
After watching this video, you will be able to describe the machine learning aspect of data science.
-
Data Science Essentials: Data Science Terminology
After watching this video, you will be able to use common data science terminology.
-
Data Science Essentials: Data Science Tools
After watching this video, you will be able to compare various tools and software libraries used for data science.
-
Data Science Essentials: Linear Algebra Matrix Decomposition
After watching this video, you will be able to perform a matrix decomposition.
-
Data Science Essentials: What is Big Data
After watching this video, you will be able to describe the big data aspect of data science.
-
Data Science Essentials: What is Data Science
After watching this video, you will be able to define data science and what it is to be a data scientist.
-
Data Science Essentials: What is Data Wrangling
After watching this video, you will be able to describe the data wrangling aspect of data science.
-
Analyzing Big Data with Microsoft R: An Overview of The R Language
After watching this video, you will be able to recognize important characteristics of the R language.
-
Analyzing Big Data with Microsoft R: List Components of Microsoft R Server
After watching this video, you will be able to identify important components of Microsoft R server.
-
Analyzing Big Data with Microsoft R: Supported Platforms for Microsoft ML Server
After watching this video, you will be able to list supported platforms for Microsoft Machine Learning Server.
-
Analyzing Big Data with Microsoft R: Microsoft ML Server in the Cloud
After watching this video, you will be able to identify important issues with provision of Microsoft Machine Learning Server in the cloud.
-
Analyzing Big Data with Microsoft R: Compute Context
After watching this video, you will be able to recognize what compute context is and its use cases.
-
Analyzing Big Data with Microsoft R: R Studio Interface
After watching this video, you will be able to identify important features of RStudio interface and recognize how to navigate in Rstudio.
-
Analyzing Big Data with Microsoft R: Visual Studio for R Interface
After watching this video, you will be able to identify important features of Visual Studio for R interface and recognize how to navigate in Visual Studio for R.
-
Analyzing Big Data with Microsoft R: Key Features of Microsoft R
After watching this video, you will be able to list key features of Microsoft R.
-
Analyzing Big Data with Microsoft R: Overview of Microsoft Machine Learning Server
After watching this video, you will be able to identify important features of Microsoft Machine Learning Server.
-
Analyzing Big Data with Microsoft R: Microsoft ML Server Components
After watching this video, you will be able to identify important components of Microsoft Machine Learning Server.
-
Analyzing Big Data with Microsoft R: Operationalize Analytics
After watching this video, you will be able to describe what operationalize analytics is and its applications.
-
Analyzing Big Data with Microsoft R: Operationalize Analytics Using Microsoft ML Server
After watching this video, you will be able to identify important features of Microsoft Machine Learning Server for operationalizing analytics.
-
Analyzing Big Data with Microsoft R: Creating a Variable
After watching this video, you will be able to identify how to subset and transform data in Microsoft R.
-
Analyzing Big Data with Microsoft R: Converting Data Types
After watching this video, you will be able to identify how to create or modify variables in Microsoft R.
-
Analyzing Big Data with Microsoft R: Data Transformation
After watching this video, you will be able to identify functions to transform data.
-
Analyzing Big Data with Microsoft R: Using Predictive Analytics
After watching this video, you will be able to describe what predictive analytics is.
-
Analyzing Big Data with Microsoft R: Predictive Analytics Applications
After watching this video, you will be able to identify use cases of machine learning.
-
Analyzing Big Data with Microsoft R: Merging Data
After watching this video, you will be able to identify important functions used to merge data in Microsoft R.
-
Analyzing Big Data with Microsoft R: Subsetting Data
After watching this video, you will be able to list key functions that are used to subset data in Microsoft R.
-
Analyzing Big Data with Microsoft R: Sort and Merge Functions
After watching this video, you will be able to identify functions that are used for sorting and merging data.
-
Analyzing Big Data with Microsoft R: Data Transformation
After watching this video, you will be able to describe the process of data transformation in Microsoft R.
-
Analyzing Big Data with Microsoft R: Modifying Data
After watching this video, you will be able to identify important functions and arguments used to modify data in Microsoft R.
-
Analyzing Big Data with Microsoft R: Importing SQL Server Data
After watching this video, you will be able to recognize how to connect to a SQL Server using Microsoft R.
-
Analyzing Big Data with Microsoft R: Importing HDFS Data
After watching this video, you will be able to recognize important steps in importing data from Hadoop.
-
Analyzing Big Data with Microsoft R: Importing ODBC Data
After watching this video, you will be able to identify important considerations for importing ODBC data into Microsoft R.
-
Analyzing Big Data with Microsoft R: Import Text Data into Microsoft R
After watching this video, you will be able to practice importing data into Microsoft R.
-
Analyzing Big Data with Microsoft R: Introduction to Data Manipulation
After watching this video, you will be able to recognize what data manipulation is.
-
Analyzing Big Data with Microsoft R: Sorting Data
After watching this video, you will be able to list key functions used to sort data in Microsoft R.
-
Analyzing Big Data with Microsoft R: rxImport Function
After watching this video, you will be able to identify important arguments for the rxImport function.
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Analyzing Big Data with Microsoft R: Importing Text Data
After watching this video, you will be able to recognize how to import text data into Microsoft R and modify data during the import.
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Analyzing Big Data with Microsoft R: Importing Multiple Files
After watching this video, you will be able to recognize how to import multiple files into Microsoft R.
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Analyzing Big Data with Microsoft R: Importing SAS and SPSS Data
After watching this video, you will be able to identify how to import SAS and SPSS data into Microsoft R.
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Analyzing Big Data with Microsoft R: Microsoft R and Big Data Analytics
After watching this video, you will be able to recognize key features of Microsoft R for analysis of big data.
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Analyzing Big Data with Microsoft R: Considerations for Big Data Analysis
After watching this video, you will be able to identify important considerations when analyzing big data.
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Analyzing Big Data with Microsoft R: Creating an XDF File
After watching this video, you will be able to recognize what XDF file format is and identify how to create an XDF file in Microsoft R.
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Analyzing Big Data with Microsoft R: Splitting an XDF into Multiple Files
After watching this video, you will be able to identify options for splitting XDF into multiple file formats.
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Analyzing Big Data with Microsoft R: Chunking Algorithms
After watching this video, you will be able to recognize chunking algorithms in Microsoft R.
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Analyzing Big Data with Microsoft R: FeaturizeText
After watching this video, you will be able to identify features of FeaturizeText function.
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Analyzing Big Data with Microsoft R: Importing Data in Microsoft R
After watching this video, you will be able to recognize key topics regarding importing in Microsoft R.
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Analyzing Big Data with Microsoft R: Introduction to Big Data
After watching this video, you will be able to describe what big data is and list big data's main characteristics.
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Analyzing Big Data with Microsoft R: Big Data Analytics
After watching this video, you will be able to recognize big data analytics and common techniques in analysis of big data.
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Analyzing Big Data with Microsoft R: Applications of Big Data Analytics
After watching this video, you will be able to identify applications of big data in various industries.
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Analyzing Big Data with Microsoft R: Control Structures
After watching this video, you will be able to list control structures in R.
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Analyzing Big Data with Microsoft R: Loops
After watching this video, you will be able to list R loops and identify their applications.
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Analyzing Big Data with Microsoft R: Subsetting Vectors and Lists
After watching this video, you will be able to identify important techniques for subsetting vectors and lists in R.
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Analyzing Big Data with Microsoft R: Subsetting Matrices and Data Frames
After watching this video, you will be able to recognize approaches for subsetting matrices and data frames in R.
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Analyzing Big Data with Microsoft R: Subsetting Operators
After watching this video, you will be able to identify subsetting operators in R.
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Analyzing Big Data with Microsoft R: Dates and Times
After watching this video, you will be able to recognize different classes of dates and times in R.
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Analyzing Big Data with Microsoft R: Debugging R Functions
After watching this video, you will be able to recognize how to debug R functions.
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Analyzing Big Data with Microsoft R: Control Structures in R
After watching this video, you will be able to identify features of control structures.
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Analyzing Big Data with Microsoft R: Operators
After watching this video, you will be able to recognize operators in the R language.
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Analyzing Big Data with Microsoft R: Expressions
After watching this video, you will be able to define expressions in R and list key characteristics of R expressions.
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Analyzing Big Data with Microsoft R: R Functions
After watching this video, you will be able to define R functions and list their key components.
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Analyzing Big Data with Microsoft R: Introduction to Data Summarization
After watching this video, you will be able to recognize the concept of data summarization and its importance.
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Analyzing Big Data with Microsoft R: Predictive Models
After watching this video, you will be able to recognize main branches and use cases of predictive models.
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Analyzing Big Data with Microsoft R: Introduction to Machine Learning
After watching this video, you will be able to recognize what machine learning is and identify its use cases.
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Analyzing Big Data with Microsoft R: Supervised and Unsupervised Learning
After watching this video, you will be able to describe supervised and unsupervised learning techniques.
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Analyzing Big Data with Microsoft R: Machine Learning Techniques
After watching this video, you will be able to list important techniques in predictive analytics.
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Analyzing Big Data with Microsoft R: Process of Developing Predictive Models
After watching this video, you will be able to list the main steps in building a predictive analytics solution.
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Analyzing Big Data with Microsoft R: Machine Learning Techniques
After watching this video, you will be able to identify important machine learning techniques.
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Analyzing Big Data with Microsoft R: Microsoft R Interface
After watching this video, you will be able to identify important features of Microsoft R interface and recognize how to navigate in Microsoft R.
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Analyzing Big Data with Microsoft R: Overview of R
After watching this video, you will be able to describe the R programing language, environment, and open-source software.
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Analyzing Big Data with Microsoft R: Overview of Microsoft R
After watching this video, you will be able to recognize what Microsoft R is.
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Analyzing Big Data with Microsoft R: Microsoft R Products
After watching this video, you will be able to list Microsoft R products and their features.
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Analyzing Big Data with Microsoft R: Introduction to Microsoft R Client
After watching this video, you will be able to describe important features of Microsoft R client.
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Analyzing Big Data with Microsoft R: Installing Microsoft R Client
After watching this video, you will be able to install Microsoft R client.
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Analyzing Big Data with Microsoft R: Integrated Development Environments (IDEs) for R
After watching this video, you will be able to list important integrated development environments for R.
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Data Science Using R: Using list2env
After watching this video, you will be able to use list2env in R.
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Data Science Using R: Creating Reference Classes
After watching this video, you will be able to create a reference class in R.
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Data Science Using R: Defining a Binary Operator
After watching this video, you will be able to define a binary operator.
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Data Science Using R: Simulating a Unary Operator
After watching this video, you will be able to simulate a unary operator using binary operator syntax.
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Data Science Using R: Using eval
After watching this video, you will be able to use the R eval function.
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Data Science Using R: Using paste Functions
After watching this video, you will be able to use the R paste family of functions.
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Data Science Using R: Using Markdown in R
After watching this video, you will be able to explore the elements of an R Markdown file (.rmd).
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Data Science Using R: Rendering HTML
After watching this video, you will be able to render an html document from R Markdown.
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Data Science Using R: Create and Publish from R Markdown
After watching this video, you will be able to create an R Markdown file (.rmd) and render the output as html.
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Data Science Using R: Using cfunction in R
After watching this video, you will be able to use cfunction in R to call inline C.
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Data Science Using R: Connecting Inline C++ to R
After watching this video, you will be able to write C++ inline in an R program.
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Data Science Using R: Calling External C++ with R
After watching this video, you will be able to use Rcpp to call C++ from R.
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Data Science Using R: Manipulating Strings with the stringr Library
After watching this video, you will be able to use the stringr library to manipulate strings.
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Data Science Using R: Using the forcats Library
After watching this video, you will be able to use the forcats library for factors in R.
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Data Science Using R: Output an R Table as LaTeX
After watching this video, you will be able to use xtable to output a table in LaTeX format.
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Data Science Using R: Exploring the tidyverse Collection of Libraries
After watching this video, you will be able to explore the libraries included in the tidyverse collection.
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Data Science Using R: Rendering a Plot in shiny
After watching this video, you will be able to create a plot using R and the shiny library.
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Data Science Using R: Displaying a JavaScript DataTable
After watching this video, you will be able to use the DT library in R to use a JavaScript DataTable.
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Data Science Using R: Using the purrr Library
After watching this video, you will be able to use the purrr library for functional programming in R.
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Data Science Using R: Creating a shiny Application
After watching this video, you will be able to use the shiny library for building web apps from R.
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Data Science Using R: Referencing with BibTex
After watching this video, you will be able to use bibtex citations from R Markdown.
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Data Science Using R: Passing knitr Parameters
After watching this video, you will be able to use knitr to dynamically generate reports in R Markdown.
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Data Science Using R: Rendering LaTeX
After watching this video, you will be able to render a LaTeX document from R Markdown.
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HDInsight: Apache Phoenix Real-time Data Use Cases
After watching this video, you will be able to identify how to use Apache Phoenix for analytics.
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HDInsight: Apache HBase Cluster Performance
After watching this video, you will be able to describe how to optimize performance of an HBase cluster.
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HDInsight: Apache HBase Clusters
After watching this video, you will be able to monitor Apache HBase clusters.
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HDInsight: Apache HBase Shell
After watching this video, you will be able to use HBase shell to create updates.
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HDInsight: Build Apache Solutions
After watching this video, you will be able to build solutions using Apache Kafka and Hbase.
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HDInsight: Apache HBase Replication
After watching this video, you will be able to create a replication in Apache HBase.
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HDInsight: Apache Phoenix Indexes
After watching this video, you will be able to describe Apache Phoenix indexes.
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HDInsight: Apache Phoenix Functions
After watching this video, you will be able to describe Apache Phoenix functions.
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HDInsight: Apache Phoenix Transactions
After watching this video, you will be able to describe how to configure Apache Phoenix transactions.
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HDInsight: Use Apache Phoenix
After watching this video, you will be able to use Apache Phoenix.
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HDInsight: Sharing Metastore between Clusters
After watching this video, you will be able to describe when to share metastores between clusters.
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HDInsight: Interactive Processing on HBase
After watching this video, you will be able to describe HBase for interactive processing.
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HDInsight: Apache Phoenix Performance
After watching this video, you will be able to identify Apache Phoenix performance.
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HDInsight: Streaming Applications
After watching this video, you will be able to identify how to start and stop Spark streaming applications.
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HDInsight: Resilient Distributed Datasets (RDDs)
After watching this video, you will be able to describe Resilient Distributed Datasets (RDDs).
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HDInsight: Introduction to DStream
After watching this video, you will be able to describe Dstream.
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HDInsight: Spark Streaming from Event Hub
After watching this video, you will be able to stream data from Event Hub.
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HDInsight: Spark Streaming from Kafka
After watching this video, you will be able to stream data from Apache Kafka.
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HDInsight: Long-Term Data Storage Options
After watching this video, you will be able to identify long-term data stores in HBase and SQL.
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HDInsight: DStream Transformations
After watching this video, you will be able to describe DStream Transformations.
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HDInsight: Stateful and Stateless Operations
After watching this video, you will be able to describe Stateful and Stateless operations.
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HDInsight: Event Time
After watching this video, you will be able to create window operations on Event Time.
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HDInsight: DataFrame and Dataset APIs
After watching this video, you will be able to use Spark DataFrames and Dataset APIs to stream.
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HDInsight: Spark Streaming in a PowerBI Dashboard
After watching this video, you will be able to describe Spark streaming in a PowerBI dashboard.
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HDInsight: Spark Structured Streaming Storage Options
After watching this video, you will be able to identify the Spark structured streaming storage options.
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HDInsight: Window Functions in Spark SQL
After watching this video, you will be able to describe window functions in Spark SQL.
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HDInsight: Spark Structured Streaming from Apache Kafka
After watching this video, you will be able to stream data from Apache Kafka using Spark structured streaming.
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HDInsight: Apache Storm Clusters for Real-time Jobs
After watching this video, you will be able to create Apache Storm clusters for real-time jobs.
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HDInsight: Stream Spark Applications
After watching this video, you will be able to stream Spark applications.
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HDInsight: Spark Structured Streaming in a PowerBI Dashboard
After watching this video, you will be able to describe Spark structured streaming in a PowerBI real-time dashboard.
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HDInsight: Spark Structured Streaming from Event Hub
After watching this video, you will be able to stream data from Event Hub using Spark structured streaming.
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HDInsight: Event Windows in Apache Storm
After watching this video, you will be able to configure event windows in Apache Storm.
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HDInsight: Apache Storm Streaming from Event Hub
After watching this video, you will be able to stream Apache Storm from Event Hub.
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HDInsight: Apache Storm Streaming from Kafka
After watching this video, you will be able to stream Apache Storm from Kafka.
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HDInsight: Apache Storm Storage Options
After watching this video, you will be able to describe the Apache Storm storage options.
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HDInsight: Apache Storm Streaming in PowerBI Dashboard
After watching this video, you will be able to stream Apache Storm data in a PowerBI real-time dashboard.
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HDInsight: Apache Storm Streams
After watching this video, you will be able to create and conduct streaming jobs using Apache Storm.
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HDInsight: Apache Storm Topologies
After watching this video, you will be able to describe Storm topologies and computation graph architecture.
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HDInsight: Apache Storm Jobs
After watching this video, you will be able to debug and monitor Apache Storm jobs.
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HDInsight: Apache Stream Groupings
After watching this video, you will be able to set Apache Stream groupings.
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HDInsight: Apache Storm Applications
After watching this video, you will be able to describe how to configure Apache Storm applications.
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HDInsight: Apache Storm Topologies in Local Mode
After watching this video, you will be able to describe how to run Storm topologies in local mode.
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HDInsight: MirrorMaker with Apache Kafka
After watching this video, you will be able to configure MirrorMaker with Apache Kafka.
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HDInsight: Apache Kafka Resource Management
After watching this video, you will be able to manage partitions using Apache Kafka resource management.
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HDInsight: Apache Spark and Storm Clusters in a Virtual Network
After watching this video, you will be able to create Spark and Storm clusters in a virtual network.
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HDInsight: Stream Apache Storm
After watching this video, you will be able to stream using Apache Storm.
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HDInsight: Apache HBase Use Cases
After watching this video, you will be able to identify HBase use cases in HDInsight.
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HDInsight: Apache Kafka Topics
After watching this video, you will be able to describe how to manage Apache Kafka topics.
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HDInsight: Apache Kafka Services through Ambari
After watching this video, you will be able to manage Apache Kafka services through Ambari.