Data Science with R and SQL Server
|Training level||BI Course|
|Duration||4 days / 32 Hours|
|Delivery method||In Class|
|List price||1050 Euro VAT Excl.|
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Introducing the language, statistics, data mining, and machine learning with R and using R in SQL Server and Microsoft BI stack.
R is the most popular environment and language for statistical analyses, data mining, and machine learning. Managed and scalable version of R runs in SQL Server and Azure ML.
Attendees should have basic understanding of data analysis and basic familiarity with SQL Server tools.
Attendees of this course learn to program with R from the scratch. Basic R code is introduced using the free R engine and RStudio IDE. A lifecycle of a data science project is explained in details. The attendees learn how to perform the data overview and do the most tedious task in a project, the data preparation task. After data overview and preparation, the analytical part begins with intermediate statistics in order to analyze associations between pairs of variables. Then the course introduces more advanced methods for researching linear dependencies.
Too many variables in a model can make its own problem. The course shows how to do feature selection, starting with the basics of matrix calculations. Then the course switches more advanced data mining and machine learning analyses, including supervised and unsupervised learning. The course also introduces the currently modern topics, including forecasting, text mining, and reinforcement learning. Finally, the attendees also learn how to use the R code in SQL Server, Azure ML, and Power BI.
- Introducing data science and R (What are statistics, data mining, machine learning, Data science projects and their lifetime; Introducing R; R tools; R data structures)
- Data overview (Datasets, cases and variables; Types of variables; Introductory statistics for discrete variables; Descriptive statistics for continuous variables; Basic graphs; Sampling, confidence level, confidence interval)
- Data preparation (Derived variables; Missing values and outliers; Smoothing and normalization; Time series; Training and test sets)
- Associations between two variables and visualizations of associations (Covariance and correlation; Contingency tables and chi-squared test; T-test and analysis of variance; Bayesian inference; Linear models)
- Feature selection and matrix operations (Feature selection in linear models; Basic matrix algebra; Principal component analysis; Exploratory factor analysis; Lab 5)
- Unsupervised learning (Hierarchical clustering; K-means clustering; Association rules;)
- Supervised learning (Neural Networks; Logistic Regression; Decision and regression trees; Random forests; Gradient boosting trees; K-nearest neighbors)
- Modern topics (Support vector machines; Time series; Text mining; Deep learning; Reinforcement learning)
- R in SQL Server and MS BI
Next planned dates:
Trainer: Dejan Sarka
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