The Middle Management seeks solutions around the business and the technology
"Data is the new science. Big Data holds the answers."
Pat Gelsinger, CEO VMware
- 1Do we understand our customer well?
- 2How can we improve our company’s business performance and where are the bottlenecks?
- 3How can we boost our top-line?
- 4Which of our products are liked by customers?
Business:
- 1What kind of Big Data Infrastructure should we invest in?
- 2Should our big data solutions be insourced, outsourced or should we just follow an augmented model?
- 3What is the best way to develop the following models: customer loyalty, fraud analytics, churn analytics?
Technology:
Some of our leading corporate trainings include
Hands-on training on near real time analytics
A brief overview of big data in banking and finance ...
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Building advanced statistical and machine learning techniques
Scope of predictive analytics, rule induction, rule mining, visualization ...
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- Advanced predictive analytics and unstructured data processing using R and other open source tools.
- An interactive 2-day workshop on big data analytics.
- A deep dive into advanced machine learning techniques.
- A customized Hadoop training on Azure - HDInsight.
- Customized use-cases training - Business Analytics across multiple Industries.
- Big data analytics to handle high velocity data.
- A customized training on big data and analytics.
- Data Analysis on Hadoop – Hands on Training.
- A brief overview of big data in banking and finance.
- Data ingestion, organization and processing on Hadoop.
- Semi-structured and unstructured data processing on Hadoop.
- Using Spark, MLlib and Mahout libraries for building machine learning models on Hadoop.
- Spark streaming, Kafka, Storm, Impala, Hive and Drill.
- Data flows and components of Hadoop ecosystem.
- Handling high volume and high variety data using open source tools.
- Querying and processing large volumes of data using SQL-like tools on Hadoop.
- Using fully open source components to handle reporting requirements.
- Use-case discussion, optimizing warehouse throughput using Hadoop.
- Descriptive analytics – visual analytics.
- Scope of predictive analytics, rule induction, rule mining, visualization.
- Market basket analysis, clustering, handling big data – R and Hadoop.
- Bayesian techniques.
- Taming text data.
- Processing graphs, deep learning, use-case discussion.
Clients
