Data Science is the future of Artificial Intelligence and a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. Data Science is a concept to bring together ideas, data examination, Machine Learning, and their related strategies to comprehend and dissect genuine phenomena with data. The digital transformation deals with the voluminous data that needs to analyse as an asset for the organizational growth. There is growing demand from the industry to analyse their data and discover the knowledge, from the data generated through their operational system. Data Science is the upcoming technology crawling into all sectors such as Trading, Finance, Production, Manufacturing, Retailing, Medical, Banking, Stock Market, Sports, Logistics etc. The industry demands around 4-5 lakhs professionally trained data scientist, data analyst, data engineer with 40% average salary hike within 8-12 months of graduation from the program
Bachelor of Science (Data Science) program is a three year undergraduate degree course with each year having two semesters.
Data analysts are responsible for a variety of tasks including data analysis, visualisation, munging, and processing of massive amounts of data. One of the most important skills of a data analyst is optimization. Here onse hould have the knowledge / skill to apply and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.
Big Data engineers build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimized. Data engineers also update the existing systems with newer or upgraded versions of the current technologies to improve the efficiency of the databases.
The job profile of a database administrator is pretty much self-explanatory- they are responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on their requirements. They are also responsible for database backups and recoveries.
Machine learning engineers are in high demand today. Apart from having in-depth knowledge in some of the most powerful technologies such as SQL, REST APIs, etc. machine learning engineers are also expected to perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc.
Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, they are expected to perform predictive analysis and run a fine-toothed comb through an “unstructured/ disorganized” data to offer actionable insights which would help the companies in making better decisions.
A data architect creates the blueprints for data management so that the databases can be easily integrated, centralized, and protected with the best security measures. They also ensure that the data engineers have the best tools and systems to work with.
A statistician, as the name suggests, has a sound understanding of statistical theories and data organization. Not only do they extract and offer valuable insights from the data clusters, but they also help create new methodologies for the engineers to apply.
The role of business analysts is slightly different than other data science jobs. While they do have a good understanding of how data-oriented technologies work and how to handle large volumes of data, they also separate the high-value data from the low-value data to identify how the Big Data can be linked to actionable business insights for business growth.
A data and analytics manager oversees the data science operations and assigns the duties to their team according to skills and expertise. Their strengths should include technologies like SAS, R, SQL, etc. and of course management.
Business analysts provide solutions that are often technology-based to enhance business processes. Organizations need these “information conduits” for a plethora of things such as gap analysis, requirements gathering, knowledge transfer to developers, defining scope using optimal solutions, test preparation, and software documentation.
Data storytelling is about finding the narrative that best describes the data and uses it to express it. A data storyteller needs to take on some data, simplify it, focus it on a specific aspect, analyze its behavior, and use his insights to create a compelling story that helps people better understand the data.
The course will be offered with blended learning model with innovative learning methodologies. The methodology comprises of learning theoretical concepts with practical experience and live projects. The project includes, practising the data science techniques using data science tools. Students will be encouraged to learn MOOC based online courses for additional qualification.
The Department of Data Science has a wonderful mix of experienced and young enthusiastic faculty members. The faculty members have specializations in various domains such as Artificial Intelligence, Machine Learning, Cloud Computing, Software Engineering, Mobile Computing, Web development and many more. We have a few faculty members who have completed their PhDs and are now PhD guides. Many of our faculty members are completing their PhDs from various reputed Universities and Institutions. Many of the faculty members have published books and research papers in reputed National and International journals. Faculty Members of the department are also members Board of Studies and Syllabus committees in various institutions including University of Mumbai.
I welcome students, parents, alumni and community members to the department of Data Science of VSIT. Data Science can be defined as a blend of Mathematics, business acumen, tools, algorithms and Machine Learning techniques, all of which help us in finding out hidden insights or patterns from raw data which can be of major use in the formation of big business decisions. It involves predictive analysis of past data which helps a data scientist to find a trend based on historical data which can be useful for present decisions. It is an amalgamation of Statistics, Tools and Business knowledge. So, it becomes imperative for a data scientist to have good knowledge and understanding of these.
To empower students with domain knowledge of Information Technology and interpersonal skills to cater to the industrial and societal need.