Tools & technologies
"If you do not know how to ask the right question, you discover nothing."
W. Edward Deming, American Engineer & Professor
- 1What tools / technologies should I learn?
- 2I have expertise in RDBMS how do I transition to Mongo DB or Cassandra etc.?
- 3I have expertise in SAS how do I quickly gain expertise in R?
- 4Is real time streaming analytics relevant for me and what is the difference between Kafka, Storm, Spark Streaming?
- 5What are the different types of algorithms and techniques available to build a recommendation engine?
- 6How can I ramp up my text analytics skills?
- 7How can I leverage my data and derive powerful insights from them?
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
