Statistics and Data Analysis: Learning foundational statistical concepts and techniques to extract meaningful insights from data
Data Visualization: Understanding how to create visual representations of data to communicate findings effectively. They also learn how to effectively communicate insights drawn from data through visualizations and presentations
Data Mining: Exploring methods to discover patterns, trends, and relationships within large datasets
Machine Learning and Deep Learning: Introducing machine learning and deep learning algorithms to solve business problems and automate decision-making processes
Big Data Analytics: Focuses on dealing with large and complex datasets using technologies like Hadoop and Spark
Business Intelligence: Applying data-driven insights to solve business problems, improve strategies, and enhance decision-making
Predictive and Prescriptive Analytics: Using historical data to make predictions about future outcomes and recommending optimal actions. This course covers advanced techniques for using historical data to predict future outcomes and trends
Text and Social Media Analytics: This course focuses on analyzing unstructured data like text from social media, customer reviews, or surveys. Techniques like NLP, Sentiment Analysis, etc., are covered during the course
Business Intelligence and Reporting: Train the students on how to design and develop dashboards and reports to monitor key performance indicators (KPI) and track business metrics
Functional Electives: Students explore to how data analytics can be applied in different functional domains like Finance, HR, Marketing, Operations and Healthcare
Ethical and Legal Aspects: Discuss the ethical considerations and legal implications of using data for business decisions
Case Studies and Projects: Engaging in hands-on projects and case studies to apply learned concepts to real-world scenarios
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