RIT School of Technology is introducing Certification in Data Science in MCA – First time in Roorkee.
Data science is repeatedly referred to as one of the most important and critical industries of the future. Without data engineers, most of data analysis would be difficult or even impossible. There is simply too much data being created for the old methods to remain relevant.
Data engineering is a fundamental part of the new world of big data, not only increasing the amount of data collected, but also ensuring that is clean, consistent, and high quality. It’s not always a visible part of the data science process and undoubtedly can be frustrating but without it, businesses would never be able to keep up with the influx of data or obtain reliable results from their analysis.
MCA program focuses on hands on skills in Python programming and its libraries, statistical analysis of data, concepts of linear algebra and mainly focuses on building expertise in data analysis concepts. As per the industry stats, here are some of the popular DS and DA related job profiles like Software Engineer, Data Analyst, Senior Software Engineer, Data Scientist, Software Developer, Business Analyst and IT Data Analyst. Companies which are hiring for DS and DA are Amazon, LinkedIn, IBM, Walmart Labs, Sigmoid and Flipkart.
Course Objectives
The main goal of this course is to help students learn, understand, and practice big data analytics and machine learning approaches, which include the study of modern computing big data technologies and scaling up machine learning techniques focusing on industry applications. Mainly the course objectives are: conceptualization and summarization of big data and machine learning, trivial data versus big data, big data computing technologies, machine learning techniques, and scaling up machine learning approaches.
Learning Outcomes
The students learning outcomes are designed to specify what the students will be able to perform after completion of the course:
- Ability to identify the characteristics of datasets and compare the trivial data and big data for various applications.
- Ability to select and implement machine learning techniques and computing environment that are suitable for the applications under consideration.
- Ability to solve problems associated with batch learning and online learning, and the big data characteristics such as high dimensionality, dynamically growing data and in particular scalability issues.
- Ability to understand and apply scaling up machine learning techniques and associated computing techniques and technologies.
- Ability to recognize and implement various ways of selecting suitable model parameters for different machine learning techniques.
- Ability to integrate machine learning libraries and mathematical and statistical tools with modern technologies like hadoop and mapreduce.
