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Stat & Machine Learning-Software training

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Trainings in SAS, Python and R

Please select your software preference to view a detailed description.

SAS Trainings

Choose the trainings that best suit you

Please review the table of contents and select relevant SAS topics. We can also tailor the training to meet your specific needs.

  • What is SAS
  • Writing your first SAS program
  • Reading raw data from external files
  • Creating permanent SAS datasets
  • Creating formats and labels
  • Reading and writing data from an Excel table
  • Display of Your Data
  • Creating Custom Reports
  • Summary of Your Data
  • Frequency Determination
  • Generating Tabular Reports
  • Introduction to the Output Delivery System
  • Creating High-Quality Graphics
  • Application of advanced INPUT techniques
  • Utilization of advanced functions, custom formats, and informats
  • Restructuring SAS datasets
  • Working with longitudinal data
  • Introduction to SAS Macro Language
  • Introduction to Structured Query Language (SQL)
  • Introduction to SAS Enterprise Guide
  • Data import and export
  • Data cleaning and transformation
  • Data exploration and visualization
  • Data analysis and statistical evaluations
  • Report and dashboard creation Task and workflow automation
  • Collaboration and sharing of projects
  • Working with SAS code and macros
  • Project monitoring and management
  • Performance optimization and resource management
  • Performing conditional processing
  • Conducting iterative processing: loop formation
  • Working with date values
  • Subset creation and combining SAS datasets
  • Working with numeric functions
  • Working with character string functions
  • Working with arrays
  • Introduction to SAS Macro Programming
  • Macro Variables and Macro Functions
  • Creation and Execution of Macros
  • Macro Programming Techniques
  • Advanced Macro Concepts
  • Efficient Utilization of the SAS Macro Language
  • Einführung in SQL
  • Datenbankgrundlagen
  • Datenabfragen mit SELECT
  • Filtern von Daten mit WHERE
  • Sortieren von Daten mit ORDER BY
  • Aggregatfunktionen und Gruppierung
  • Joins und Verknüpfungen von Tabellen
  • Unterabfragen und verschachtelte Abfragen
  • Datenmanipulation mit INSERT, UPDATE und DELETE
  • Tabellen erstellen, ändern und löschen
  • Datenbankadministration und -optimierung
  • Introduction to SAS Viya
  • Data Exploration and Processing with SAS Viya
  • Feature Engineering with SAS Viya
  • Data Partitioning with SAS Viya
  • Classification with SAS Viya
  • Cluster Analysis with SAS Viya
  • Model Evaluation and Validation with SAS Viya
  • Hyperparameter Optimization with SAS Viya
  • Model Documentation and Deployment with SAS Viya
  • Advanced Topics in Machine Learning with SAS Viya
Book a Non-binding Free Initial Consultation

Python Training for Data Science

Please review the table of contents and select relevant Python topics. We customize the training to meet your needs

  • Foundations of Python
  • Data Types and Structures in Python
  • Control Structures and Loops
  • Functions and Modules in Python
  • Working with NumPy Arrays
  • Data Cleaning and Preprocessing with Pandas
  • Handling Missing Data
  • Data Aggregation and Transformation
  • Introduction to Visualization Libraries (Matplotlib, Seaborn)
  • Creating basic charts and plots
  • Customizing visualizations
  • Visualization of categorical and temporal data
  • Introduction to Text Analysis and Natural Language Processing
  • Text Preprocessing with Python
  • Sentiment Analysis and Emotion Detection
  • Text Classification and Recognition
  • Language Generation and Text Comprehension
  • Applications of NLP in Practice
  • Advanced Topics in Text Analysis and NLP
  • Practical Application and Case Studies
 
 
 
  • Introduction to Data Visualization with Plotly and Dash
  • Installation and Setup of Plotly and Dash
  • Fundamentals of Data Visualization with Plotly
  • Creating Interactive Charts with Plotly
  • Customization and Formatting of Charts with Plotly
  • Data Visualization with Dash
  • Creating Interactive Dashboards with Dash
  • Data Binding and Updating in Dash Dashboards
  • Advanced Techniques and Visualization Types with Plotly and Dash
  • Integration of Plotly and Dash in Web Applications
  • Best Practices for Data Visualization with Plotly and Dash
  • Introduction to Big Data Processing with PySpark
  • Installation and Configuration of PySpark
  • Fundamentals of PySpark and Spark
  • Data Acquisition and Preprocessing with PySpark
  • Data Manipulation and Transformation with PySpark
  • Data Analysis and Exploration with PySpark
  • Machine Learning with PySpark
  • Streaming Processing with PySpark
  • Optimization and Scaling of PySpark Jobs
  • Integration of PySpark in Big Data Environments
  • Best Practices for Big Data Processing with PySpark
  • Introduction to Deep Learning with Python
  • Python Fundamentals for Deep Learning
  • Introduction to TensorFlow and Keras
  • Data Preparation for Deep Learning
  • Building and Training Deep Learning Models with Python
  • Fine-Tuning and Optimization of Deep Learning Models
  • Evaluation and Analysis of Deep Learning Models
  • Practical Application Examples in Python
  • Introduction to Model Deployment and Productionization
  • Model Packaging and Export with Python
  • Implementation of RESTful APIs for Model Access
  • Scaling and Performance Optimization of Models
  • Monitoring and Troubleshooting of Models in Production
  • Integration of Models into Production Environments
  • Automation of Model Deployment and Updates
  • Security and Data Privacy in Model Deployment
  • Introduction to Machine Learning with scikit-learn
  • Fundamentals of scikit-learn
  • Supervised Learning with scikit-learn
  • Unsupervised Learning with scikit-learn
  • Advanced Techniques with scikit-learn
  • Practical Application Examples with scikit-learn
  • Model Deployment and Integration with scikit-learn
  • Best Practices and Advanced Tips for scikit-learn
  • Introduction to TensorFlow and Keras
  • Installation and configuration of TensorFlow and Keras
  • Fundamental concepts of TensorFlow and Keras
  • Creating and compiling neural networks with Keras
  • Data preparation and preprocessing with TensorFlow and Keras
  • Training Deep Learning models with TensorFlow and Keras
  • Evaluation and validation of Deep Learning models
  • Optimization and improvement of Deep Learning models
  • Advanced techniques with TensorFlow and Keras
  • Model deployment and integration with TensorFlow and Keras
  • Best practices and tips for using TensorFlow and Keras

R Training for Data Science

Please review the table of contents and select appropriate R topics. We will tailor the training to your needs.

  • Introduction to R
  • Getting to know the R environment
  • Data management in R
  • Data visualization with R
  • Statistical analysis in R
  • Introduction to programming with R
  • Practical application of R
  • Resources and further development in R
  • Introduction to Data Visualization and Exploratory Data Analysis
  • Fundamentals of Data Visualization with R
  • Visualization of Numerical Data
  • Visualization of Categorical Data
  • Visualization of Temporal Data
  • Advanced Visualization Techniques
  • Exploratory Data Analysis with R
  • Presentation and Documentation of Data Visualization Results
  • Introduction to the mlr3 R package
  • Data management and preparation
  • Machine learning pipeline with mlr3
  • Model development and selection with mlr3
  • Model evaluation with mlr3
  • Advanced techniques and extensions in mlr3
  • Automation and workflow management with mlr3 Advanced topics in mlr3
  • Einführung in R und RStudio für maschinelles Lernen
  • Datenmanipulation und -vorbereitung mit dem dplyr-Paket
  • Datenvisualisierung mit dem ggplot2-Paket
  • Lineare Regression und Generalisierte lineare Modelle mit dem glm-Paket
  • Entscheidungsbaummodelle mit dem rpart-Paket
  • Random Forests mit dem randomForest-Paket
  • Support Vector Machines mit dem e1071-Paket
  • Neuronale Netze und Deep Learning mit dem keras-Paket
  • Clusteranalyse mit dem cluster-Paket
  • Bewertung von Modellen und Leistungsmetriken mit dem caret-Paket
  • Introduction to Text Analysis and Natural Language Processing (NLP) with R
  • Text Preprocessing and Tokenization using the ‘tm’ package
  • Text Normalization and Stopword Removal using the ‘tm’ package
  • Word Frequency Analysis and Distributions using the ‘tm’ package
  • Text Classification and Sentiment Analysis using the ‘caret’ package
  • Named Entity Recognition (NER) and Entity Relationships using the ‘openNLP’ package
  • Topic Modeling and Latent Dirichlet Allocation (LDA) using the ‘topicmodels’ package
  • Text Clustering and Document Similarity using the ‘text’ or ‘tm’ package
  • Word Vectors and Word Embeddings using the ‘text2vec’ or ‘word2vec’ package
  • Advanced Techniques in Text Analysis and NLP with R packages
  • Introduction to Database Integration and Big Data Analytics with R
  • Connecting to Databases and Data Import with the ‘DBI’ Package
  • Data Querying and Manipulation with the ‘dplyr’ Package
  • Data Visualization and Exploratory Data Analysis with the ‘ggplot2’ Package
  • Data Analysis with Apache Hadoop and the ‘rhdfs’ Package
  • Data Analysis with Apache Spark and the ‘sparklyr’ Package
  • Data Analysis with Relational Databases and the ‘RSQLite’ Package
  • Data Analysis with NoSQL Databases and the ‘mongolite’ Package
  • Parallel Data Processing and Scalability with the ‘doParallel’ Package
  • Advanced Big Data Analytics and Machine Learning with R Packages
  • Introduction to Time Series Analysis and its Applications
  • Data Import and Preparation for Time Series Analysis using the ‘ts’ package
  • Time Series Visualization and Exploratory Data Analysis with the ‘ggplot2’ package
  • Time Series Component Analysis and Trend Detection with the ‘forecast’ package
  • Seasonal Adjustment and Decomposition of Time Series with the ‘forecast’ package
  • Time Series Forecasting and Modeling with ARIMA Models and the ‘forecast’ package
  • Time Series Modeling with GARCH Models and the ‘rugarch’ package
  • Time Series Analysis with State Space Models and the ‘dynamichazard’ package
  • Time Series Clustering and Pattern Recognition with the ‘dtw’ package
  • Advanced Techniques in Time Series Analysis with R
  • Bayesianische lineare Regression mit dem ‘rstanarm’-Paket
  • Hierarchische Modelle und Multilevel-Analyse mit dem ‘brms’-Paket
  • Bayesianische Zeitreihenanalyse mit dem ‘bsts’-Paket
  • Bayesianische Entscheidungsanalyse und -optimierung mit dem ‘BayesOpt’-Paket
  • Bayesianisches Maschinelles Lernen mit dem ‘BART’-Paket
  • Fortgeschrittene Techniken in der Bayesianischen Datenanalyse mit R
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