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11372 Introduction to Data Science course is designed to develop detailed knowledge regarding data science management. Collectively, data science management entails a range of dimensions including collation of data, identifying and extracting critical patterns of data, streamlining and filtering of data, and obtaining a key range of information from a range of available data sets. Data science has gained prominence in the recent past based on the complex patterns of emerging data associated with global business operations. One of the fundamental learning outcomes of data science is related to the collection of the relevant data sets.

The course introduces the students to the range of techniques associated with the collection and consolidation of the emerging set of data, in real-time. The core objective, in this case, is to extract a range of information that is relevant, comparable and provide a platform for a future range of strategic directions for a business enterprise. The assessment of the scale of information and data generated by an organization should ideally consider the parameter, in terms of establishing a feasible data science infrastructure. The course helps the students to get familiarized with the aspect of the feasibility of establishing data science infrastructure within an organization.

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As a discipline, data science can be associated with a wide range of techniques and concepts. One of the essential concepts, in this case, is the range of statistical tools that will be relevant in terms of implementing effective data science infrastructure. 11372 assessment answers further create the scope to be aware of the sophisticated range of technology integration that is essential to maintain a coordinated data science infrastructure.

Big data analytics is one of the evolving and specialized fields of study that is intricately associated with the implementation of the data science system. The students are initially made aware of the association between technological integration and the implementation of data science.  The students will need to apply the key knowledge related t the recognition of key data patterns, statistical integration. Moreover, data science will involve a clear understanding of the scope and implementation of security, as part of the overall infrastructure. The fundamentals, in this context, will help to develop a range of guidelines regarding implementation.

Unit Details Of 11372 Introduction To Data Science

Unit details of this course include the following:

Location - University of Canberra, Canberra

Study Level - Undergraduate

Unit Code - 11372 

The 11372 solutions focus on creating an alignment among the theoretical and practical dimensions related to data science. The fundamental theoretical aspects are related to the principles and underlying concepts related to the design and implementation of data science. The practical application of the knowledge base acquired through the unit will be among the key learning outcomes of the overall unit. The application of relevant modeling and analytical tools will be considered among the key factors that will shape the overall assessment of the knowledge base and expertise. Fundamental knowledge related to technology integration will be among the key units of the overall unit. Moreover, the unit will focus on the application of cloud computing, big data analytics.

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Brief On 11372 Introduction To Data Science

The students will be primarily evaluated based on their knowledge base related to the fundamental aspects of data science. The theoretical and practical assessments will be designed to ensure a platform for evaluating the ability of the students in terms of aligning the theoretical concepts and the practical implementation. The design of data and documentation protocols will be among the key evaluation parameters in this case. The overall expertise in terms of data modeling will be considered critical expertise, related to data science infrastructure designing.

The evaluation will particularly focus on the basic understanding and implementation of the tools and technologies related to big data analytics. The appropriate application of big data analytics will reflect a comprehensive understanding of the basics of data science infrastructure. A range of practical assessments will test the aptitude of the students, in terms of the application of artificial intelligence and machine learning infrastructures. The ability to integrate technology and key statistical modeling will also be included as part of the overall evaluation criteria. The alignment of internal resources and providing effective solutions to data science-related critical issues will be considered among the evaluation criteria.

Weightage Of This 11372 Course Code In Their Semester

The overall assessment will consist of a range of modules. The core modules will include fundamentals of data science, practical application of data science knowledge base, application of statistical and data science modeling techniques, application of digital technology, and automation in data sciences. Each of the modules will be associated with a weightage of 25% each. The qualifying criterion for each of the modules will be 50%. Overall, the students will be expected to secure a minimum parameter of 50% to qualify. Each of the modules will be sub-divided into theoretical assessment (60%) and practical knowledge-based evaluation (40%).

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