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Obtain a Certification in

Data Analytics

CITREP+ Funding
(70 - 100%)

* Conditions Apply




Courses are delivered by GSTF only in Singapore. For other locations, contact our authorised partners directly by clicking here.

The role of a Certified Data Analytics (R) Specialist (CDAS):

As a data analytics professional, you will be working as a strategy oriented role or can work as a very specialized deep learning scientist. The ‘strategy oriented’ role has a stronger component of the business, while the ‘deep learning’ role has a much stronger component of analytics. Therefore, a data analytics professional generally has a trade-off between these two components and you can switch between roles that have different proportions of the two components. The value which you create for yourself is a positively correlated function of business understanding and analytics.

Certified Data Analytics (R) Specialist (CDAS) course by GSTF in Singapore is designed for professionals who aspire to learn an open source R tool for Analytics. Certified R Analytics Specialist covers the concept of Business Analytics and its strategic importance to any organization. You will understand how cutting-edge businesses use data to optimize marketing, maximize revenue, make operations efficient, and make hiring and management decisions so that you can apply these strategies to your own company or business. Participants will learn how to address business needs through the use of analytics, how some organizations have done it and what has been done to achieve them. Conducted interactively with case studies and real business problems, participants can also expect to learn the basic principles, concepts, techniques and tools used in business analytics. Also, covers different types of business analytics with real life use cases including descriptive analytics and predictive analytics.

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Launching a new course on AI with Python and MongoDB
 

Data Analytics Course Outline:

Unit 1 : Machine Learning – An overview
Unit 2 : Model Selection methods in Machine Learning
Unit 3 : Scikit-learn and SciPy
Unit 4 : Libraries for scientific computation and data analysis
Unit 5 : Artificial Neural Network
Unit 6 : Theories under Deep Learning
Unit 7 : Types of Neural Network
Unit 8 : Introduction to TensorFlow
Unit 9 : Restricted Boltzman Machine and DeepBeliefNet

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