Big Data Analytics
“Unlock Data Possibilities: Turn Big Data into Big Revenue” – Oliver Halter
“Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming.” – Chris Lynch[dt_button size=”small” animation=”none” icon=”” icon_align=”left” color=”” link=”http://nishantdas.com/register?courseregistration=Big_Data_Analytics” target_blank=”true”]Register Here![/dt_button]
Big data is now a reality! The disciplines of Big Data analytics are evolving so rapidly that businesses need to wade in or take the risk of being left behind other businesses. There is huge volume, variety and velocity of data coming into organisation and their interpretation is essential for future growth.Big data is sending ripples through all sectors of society. We track everything and this explosion of data in the digital universe is creating huge skill gap and opening up multiple opportunities in data analysis and management.
Data is useless if it’s not “mined,” which means optimally collected, analyzed, organized, and activated. Every organization – small or big needs a data scientist for managing their data and charter their growth.
The objective of the course is to help participants to understand the basic concepts of Big Data with Hadoop and R language. The course would provide hands-on data interpretation, data visualisation and exploration, statistical concepts including linear & logistic regression, cluster analysis and forecasting, Hadoop platform and the whole ecosystem of Big Data.
On the successful completion, learners would be able to interpret huge volumes of data in real-time analytics. The insight provided by the analytics would empower the participants to take better and accurate business decisions.
Developing Value through Business Analytical Applications
Marketing & Retail Analytics
Apply analytical techniques to various retail and marketing problems; including determining customer value and using the concept to aid in business decision making by allocating marketing expenditures between customer acquisition and customer retentions.
Application of Analytics in Finance – Financial Ratios, Valuations; and develop models to solve financial problems.
Supply Chain Analytics
Application of Analytics in Supply Chain Management – Vertical Integration, Demand forecast & planning; Develop inventory models under uncertainty including service level and reorder point models.
Lean & Six Sigma, Linear Programming; Deploy analytics, such as aggregate planning models in production planning and scheduling.
Application of Analytics in Human Resource – Recruitment & Performance Measurement.
Measure Hospitals Operational KPIs for various healthcare problems.
Risk Assessment Analytics
Improved predictive power and stability of risk models using real time risk intelligence, improving monitoring of risk and reducing noise-to-signal ratios. Strengthen evidence based decision-making capacities across a number of key domains and enjoy significant cost savings in risk management.