Big Data Analytics
You’ll develop the cultural awareness and critical thinking skills you need to analyze and produce a broad range of discourse in a full spectrum of careers — and to make a difference in whatever you do.
In this course, students will get an introduction to working with Big data and some fundamental concepts, such as data mining and stream processing and technologies for Big Data scenarios. Students participating in this course will learn various data genres and management tools appropriate for each, how big data is driving organizational change and the key challenges organizations face when trying to analyze massive data sets and able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools. By the end of this course Students will become familiar with techniques using real-time and semi-structured data examples, techniques to extract value from existing untapped data sources and discovering new data sources and will have a better understanding of the various applications of big data methods in industry and research.
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|Introduction to Data Analytics for Business|
|Introduction to Big Data|
|Python Programming Language|
|Business Analytics for Decision Making|
|Big Data Modeling and Management Systems|
Upon successful completion of this course student will learn:
- Understanding the rate of occurrences of events in big data and a better understanding of the various applications of big data methods in industry and research.
- How to design algorithms for stream processing and counting of frequent elements in Big Data
- Knowledge and application of MapReduce
- Key technologies and techniques, including Python programming language and Apache Spark, to analyze large-scale data sets to uncover valuable business information.
- Develop your knowledge of big data analytics and enhance your programming and mathematical skills.
- Approach large-scale data science problems with creativity and initiative.
- How to use fundamental principles used in predictive analytics.
- Understand the role of Knowledge Management (KM) practitioners in creating business value
- How to use Cloud Services to derive new values and business models
- Identifying the computational tradeoffs in a Spark application, performing data loading and cleaning using Spark
- Modeling data through statistical and machine learning methods.
- How to perform supervised an unsupervised machine learning on massive datasets using the Machine Learning.
- Evaluate and apply appropriate principles, techniques and theories to large-scale data science problems.
|Graduates of this program will be able to start their career with entry-level positions in medium to large-sized companies as|
|Data Warehouse Administrator|
|Data warehouse analyst|
|Information Resource Analyst|
|Data analyst – informatics and systems|
|Data mining analyst|
|Data processing specialist|
|Database administrator (DBA)|
|Database management supervisor – computer systems|
|Electronic data processing (EDP) analyst|
|Electronic data processing (EDP) systems analyst|
|Information resource analyst|
|Course Duration:||180 Hours|