The Programme in Data Science and Analytics is meticulously designed to empower participants with the expertise and skills required to analyze and interpret complex data. It combines rigorous academic theory with practical, real-world applications, covering a comprehensive curriculum that includes statistics, machine learning, data mining, and big data technologies. Through hands-on projects and case studies, students gain invaluable experience, preparing them for a successful career in the burgeoning field of data science. The programme aims to produce graduates who are not only proficient in the technical aspects of data analysis but also possess the ability to make data-driven decisions and communicate their findings effectively to stakeholders. This multidisciplinary approach ensures that graduates are well-equipped to meet the demands of an increasingly data-driven world.
ABOUT THE PROGRAMME:
Data Science and Analytics is delivered in both lecture-based and hands-on lab learning environments where students can develop and apply their skills to complex, real-world datasets and data science and analytics problems.Data Science with Analytics” is a program that typically combines the fields of data science and analytics to equip individuals with the skills and knowledge needed to extract insights from data. Here’s a breakdown of what this program might entail:Data science involves using various techniques, algorithms, and systems to extract insights and knowledge from structured and unstructured data. This often includes skills in programming languages like Python or R, statistical analysis, machine learning, data visualization, and data manipulation. Analytics involves the process of analyzing data to uncover patterns, trends, and insights that can be used to make data-driven decisions. This may involve techniques such as descriptive analytics (summarizing data), diagnostic analytics (identifying causes of events), predictive analytics (forecasting future trends), and prescriptive analytics (suggesting actions based on data analysis).Skills Development: program in data science with analytics would likely focus on developing practical skills in areas such as data collection, data cleaning, exploratory data analysis, statistical modeling, machine learning algorithms, data visualization, and interpretation of results.Tools and Technologies: in such a program would likely gain proficiency in tools and technologies commonly used in data science and analytics, such as Python libraries, R programming language, SQL databases, data visualization tools, , and machine learning frameworks.InReal-world Applications: The program may include case studies, projects, and real-world applications to provide students with hands-on experience in applying data science and analytics techniques to solve practical problems in various domains such as finance, healthcare, marketing, and others. Ethics and Privacy Given the sensitive nature of data and the potential impact of data-driven decisions, an emphasis on ethics, privacy, and responsible data handling practices may also be included in the curriculum.