This Data Science with SQL Server and R Training in Sweden introduces R programming, statistics, data mining, and machine learning, and shows how to apply data science within SQL Server and the Microsoft BI stack.
R is the most popular environment and language for statistical analysis, data mining, and machine learning. Its scalable version runs inside SQL Server, Power BI, and Azure ML. While the main focus of the course is R, it also covers how to integrate other Microsoft BI tools like Python, T-SQL, Power BI, Azure ML, and Excel for data science tasks.
Labs focus on R, but demos also feature other languages.
Compare R and Python
Learn unsupervised learning techniques
Perform matrix operations
Visualize relationships between variables
Prepare data for analytics
Understand supervised learning models
Basic understanding of data analysis
Familiarity with SQL Server tools
By the end of this Data Science with SQL Server and R Training in Sweden, you will have gained knowledge and skills in the following areas:
Program in R from scratch using R Engine and RStudio
Understand the lifecycle of a data science project
Perform data exploration and preparation
Analyze relationships between variables using intermediate statistics
Apply linear modeling and Bayesian inference
Use R in SQL Server, Power BI, and Azure ML
Explore how Python can be used across all tools via demos
Definitions: statistics, data mining, machine learning
Data science project lifecycle
Introduction to R and its tools
R data structures
Lab 1
Basic syntax and objects
Data manipulation with NumPy and Pandas
Visualizations with Matplotlib and Seaborn
Machine learning with Scikit-Learn
Lab 2: R vs Python
Datasets, cases, and variables
Variable types
Discrete and continuous variable statistics
Basic graphs and visualizations
Sampling, confidence levels and intervals
Lab 2
Derived variables
Handling missing values and outliers
Smoothing and normalization
Time series
Training and test set creation
Lab 3
Covariance and correlation
Contingency tables and chi-squared test
T-tests and ANOVA
Bayesian inference
Linear modeling
Lab 4
Feature selection in linear models
Basic matrix algebra
Principal component analysis (PCA)
Exploratory factor analysis
Lab 5
Hierarchical clustering
K-means clustering
Association rules
Lab 6
Neural networks
Logistic regression
Decision and regression trees
Random forests
Gradient boosting
K-nearest neighbors
Lab 7
Support vector machines
Time series forecasting
Text mining
Deep learning
Reinforcement learning
Lab 8
ML Services (In-Database) architecture
Executing external scripts in SQL Server
Model storage and native prediction execution
Using R in Azure ML and Power BI
Lab 9
Join our public courses in our Sweden facilities. Private class trainings will be organized at the location of your preference, according to your schedule.