Online MBA in Data Science

Oversees procurement, logistics, vendor management, and inventory flow while ensuring cost efficiency and smooth supply chain operations.

Interprets business data, identifies operational trends, prepares analytical reports, and supports data-driven decision-making across departments.

Evaluates business processes, analyzes performance gaps, designs improvement strategies, and aligns operations with organizational goals.

Manages end-to-end operational processes, streamlines workflows, enhances productivity, and ensures efficient delivery of products and services.

Uses analytical tools to monitor operational performance, optimize processes, and enhance overall efficiency within business functions.

Leads data science initiatives, supervises analytical teams, develops predictive models, and provides insights that support strategic decisions.

Plans and executes projects, allocates resources, monitors progress, and ensures timely and cost-effective project completion.

A data architect defines and designs an organization's data systems, acting as a blueprint for how data is collected, stored, managed, and accessed. They translate business needs into technical data strategies, ensuring data is secure, accurate, and accessible to support business goals. Their responsibilities include creating data models, establishing data management processes, and collaborating with other teams to implement and maintain these systems.

A data scientist job description involves collecting, cleaning, and analyzing large datasets to identify trends, build predictive models using machine learning, and communicate insights to stakeholders. Key responsibilities include performing statistical analysis, creating data visualizations, collaborating with cross-functional teams, and developing data-driven solutions for business problems. The role requires a combination of technical skills in programming (like Python and R), statistics, and machine learning, along with strong problem-solving and communication abilities.

A Machine Learning (ML) Engineer/AI Engineer designs, builds, and deploys self-learning AI systems and models to automate predictive tasks and solve complex business problems. This role requires a blend of software engineering, data science, and analytical skills.

A data engineer job description involves designing, building, and maintaining scalable data systems like databases and pipelines to collect, store, and process raw data. Key responsibilities include ensuring data quality and security, collaborating with data scientists and analysts, and transforming data into a usable format for analytics and business decisions. Common technical skills required are proficiency in SQL, programming languages like Python, and big data technologies such as Hadoop and Spark.

A quantitative analyst, or "quant," uses advanced mathematical, statistical, and computer science skills to build and implement financial models. They apply these models to analyze markets, price securities, manage risk, and develop trading strategies for financial firms like investment banks and hedge funds. Quants help firms make data-driven decisions by providing key insights and tools for trading, risk management, and portfolio management.

Builds dashboards and analytics report

A data administrator is responsible for the maintenance, security, integrity, and availability of a company's databases. Key duties include designing and creating database structures, implementing security and backup protocols, monitoring performance, troubleshooting issues, and assisting users with database access. They also work with developers and other teams to ensure database solutions meet business needs.

Prepares daily/weekly reports, maintains data sheets, and tracks KPIs.

Builds dashboards in Power BI/Tableau and presents insights

Leads analytics teams, defines data strategy, monitors KPIs, and drives data-driven decision-making across departments.

Develops BI reports, dashboards, and visualizations using tools like Tableau, Power BI, and SQL to monitor business performance.

Designs, implements, and maintains machine learning models to support predictive analytics and automation.

Bridges the gap between business requirements and technical solutions; analyzes processes and data to drive business improvements.

Advises companies on data-driven strategies, analytics projects, and process optimization.

Analyze product performance, user behavior & metrics.