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Are you looking at data discovery to improve your top line growth?
Are you looking at platform that can help provide insights quickly?
Are you looking at open data platform/ architecture that help provide multiple data accessing and processing capabilities?
Explore DTEL Data Platform (DDP) that help provide smart data analytics and intuitive visualizations for fast and effective decision making. The platform leverages Big data technologies to deliver insights and visualizations to various industry verticals. The platform is built of open data architecture, tool agnostic and flexible to accommodate Customer proprietary tools. The platform provides business use cases/ solutions leveraging the big data technology and provide analytics with measurable outcomes
Data Cataloging, Data Quality Management, Data Collaboration, Metadata Management
Data Profiling, In-database Processing, Data Blending, Data Wrangling
In-database Analytics, Predictive Analytics, Data Mining, Prescriptive Analytics
Visualytics, Business Intelligence, Visual Analytics, Descriptive Analysis
Spatial Analytics, Demographic Analysis, Gis Mapping Integration
Technology Partners, Big Data Analytics, Sales force Analytics, Sap Analytics etc..
Open and combine simple text formats (CSV, PDF, XLS, JSON, XML, etc), unstructured data types (images, documents, networks, molecules, etc), or time series data.
Connect to a host of databases and data warehouses to integrate data from Oracle, Microsoft SQL, Apache Hive, and more. Load Avro, Parquet, or ORC files from HDFS, S3, or Azure.
Access and retrieve data from sources such as Twitter, AWS S3, Google Sheets, and Azure.
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 normalisation, 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
Build machine learning models for classification, regression, dimension reduction, or clustering, using advanced algorithms including deep learning, tree-based methods, and logistic regression.
Optimize model performance with hyperparameter optimisation, boosting, bagging, stacking, or building complex ensembles.
Validate models by applying performance metrics including Accuracy, R2, AUC, and ROC. Perform cross validation to guarantee model stability.
Explain machine learning models with LIME, Shap/Shapley values. Understand model predictions with the interactive partial dependence/ICE plot.
Make predictions using validated models directly, or with industry leading PMML, including on Apache Spark.
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.
Display summary statistics about columns in a table and filter out anything that’s irrelevant.
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.
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