Focuses on extracting patterns from large datasets using statistical and machine learning methods. Participants learn techniques such as classification, clustering, and regression to predict future trends and behaviors.

Covers statistical methods and their application in business contexts. Topics include hypothesis testing, confidence intervals, ANOVA, and regression analysis to support data-driven decisions.

Emphasizes the importance of visual representation of data to communicate insights effectively. Participants learn to use visualization tools and design principles to create impactful charts and dashboards.

Introduces technologies and frameworks for handling and processing large-scale data. Topics include Hadoop, Spark, and NoSQL databases, focusing on their application in business analytics.

Explores systems and tools that support business decision-making processes. Participants learn about data warehousing, OLAP, and performance management systems to analyze business operations.

Covers machine learning algorithms and their practical applications in business scenarios. Topics include supervised and unsupervised learning, model evaluation, and implementation using relevant software.

Focuses on mathematical modeling techniques to optimize business processes. Participants learn linear programming, simulation models, and decision analysis to improve operational efficiency.

Examines analytical methods used to assess and improve marketing strategies. Topics include customer segmentation, campaign analysis, and pricing strategies using data-driven approaches.

Explores the application of analytics in financial decision-making. Participants study risk assessment, portfolio optimization, and financial forecasting models.

Analyzes data-driven strategies to enhance supply chain operations. Topics include demand forecasting, inventory optimization, and logistics planning.

Focuses on applying analytics to improve healthcare services. Participants learn about patient data analysis, healthcare operations, and outcome prediction models.

Examines the use of data to manage and optimize human resources. Topics include employee performance analysis, retention strategies, and workforce planning.

Addresses ethical considerations and data privacy issues in analytics. Participants explore regulations, ethical frameworks, and best practices for responsible data use.

Provides hands-on experience by applying analytical techniques to real-world business problems. Participants work on projects that integrate knowledge from previous courses to deliver actionable insights.