Shape your Data
Derive statistics, including mean, quantiles, and standard deviation, or apply statistical tests to validate a hypothesis. Integrate dimensions reduction, correlation analysis, and more into your workflows.
Aggregate, sort, filter, and join data either on your local machine, in-database, or in distributed big data environments.
Clean data through normalization, data type conversion, and missing value handling. Detect out of range values with outlier and anomaly detection algorithms.
Extract and select features (or construct new ones) to prepare your dataset for machine learning with genetic algorithms, random search or backward- and forward feature elimination. Manipulate text, apply formulas on numerical data, and apply rules to filter out or mark samples.
Leverage Machine Learning & AI
Build machine learning models for classification, regression, dimension reduction, or clustering, using advanced algorithms including deep learning, tree-based methods, and logistic regression.
Access any popular ML library like TensorFlow, Keras, H2O, and more and cutting-edge techniques in text mining, image processing, and more.
Explain machine learning models with LIME, Shap/Shapley values. Understand model predictions with the interactive partial dependence/ICE plot.
Validate models by applying performance metrics including Accuracy, R2, AUC, and ROC. Perform cross validation to guarantee model stability.
Discover and Share Data Insights
Visualize data with classic (bar chart, scatter plot) as well as advanced charts (parallel coordinates, sunburst, network graph, heat map) and customize them to your needs.
Export reports as PDF, PowerPoint, or other formats for presenting results to stakeholders.
Store processed data or analytics results in many common file formats or databases.
Scale Execution with Demands
Scale workflow performance through in-memory streaming and multi-threaded data processing.
Exercise the power of in-database processing or distributed computing on Apache Spark to further increase computation performance.
AI Data Analytics
Course Duration: 24 Hours
Blend Data from Any Source
Blend different data types: strings, integers, images, text, networks, sound, molecules, and more.
Connect to all major databases and data warehouses such as SQL Server, Postgres, MySQL, Snowflake, Redshift, BigQuery, and more.
Blend large data volumes: import and export HDFS data and perform SQL analytics within Hive and Impala, or create and run Apache Spark applications withinplatform.
Shape your Data
Derive statistics, including mean, quantiles, and standard deviation, or apply statistical tests to validate a hypothesis. Integrate dimensions reduction, correlation analysis, and more into your workflows.
Aggregate, sort, filter, and join data either on your local machine, in-database, or in distributed big data environments.
Clean data through normalization, data type conversion, and missing value handling. Detect out of range values with outlier and anomaly detection algorithms.
Extract and select features (or construct new ones) to prepare your dataset for machine learning with genetic algorithms, random search or backward- and forward feature elimination. Manipulate text, apply formulas on numerical data, and apply rules to filter out or mark samples.
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