The global Big Data Analytics market size, which is currently valued around $231.43 billion, is projected to reach $549.73 billion by 2028. Despite significant market potential, there is a dearth of analytical talent – data scientists (akin to the Wall Street quants of the 1990s), analysts and managers – to leverage the economic value of big data.
Big data analysis is likely to fuel the next wave of growth in productivity, innovation, and competition in the marketplace. The organizations’ ability to unlock the potential of big data and lead in the marketplace will be largely determined by its ability to tackle major hurdles in effectively managing big data – identifying the business use case, hiring, nurturing, and retaining the right analytical talent for conducting big data analytics, and embracing data driven culture for making business decisions.
This programme aims to help the participants build a solid foundation on big data and analytics. It will enable the participants to learn, design and build big data analytic solutions to solve business problems and help improve their data-driven decision-making skills. The programme design with a right mix of cases, lectures and hands-on sessions will allow the participants to effectively leverage advanced analytical methods and tools to solve business problems. It will also help the learners understand various issues, challenges, and best practices in effectively managing data and analytics in organisations.
For more information or any questions, contact Ms. Vidya Kadamberi: firstname.lastname@example.org, +91 70690 74821
The program aims to cover the essential aspects of data and analytics in three key modules:
Managing Data and Analytics
Managing analytic lifecycle in organizations
Strategies and best practices in data and analytics
Analytic communications and storytelling with data
Analytical Models and Applications
Supervised and un-supervised machine learning models
Personalized recommendations in e-commerce
Customer behaviour analysis using text, image, and location data
Neural networks and Deep learning
Visual analytics: Basic and advanced constructs
Exploratory visual analytics for understanding large data sets
Design and development of dashboards
Technology and Data management
Python programming for data science
Fundamentals of data access using Relational and NoSQL databases
Big data technologies: DFS, Map reduce, Hadoop and Spark
Mix of pedagogies including case discussions, lectures, and hands-on exercises and group projects.
Professionals with at least 2 years of industry experience, and who are keen to build competencies in the use of data and analytics
Data science and analytics professionals, technology-oriented professionals, statisticians, data scientists, business analysts, data engineers and functional managers
The participants are expected to have a good working knowledge of descriptive statistical analytical methods and tools