Business Analytics Career Track courses provide essential skills in data analysis, AI/ML, and decision-making.
Real life data are often messy, unclean, or missing, and rarely in the right form to be analysed – they need to be pre-processed first before they become amenable for analytics. In fact, data pre-processing takes up a large percentage of the effort in an analytics project. This course will discuss and practice the common pre-processing techniques.
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Artificial Intelligence (AI) based on data and machine learning algorithms (ML) has become the cornerstone of Industry 5.0 in the last decade. Many novel use cases of AI and ML are being reported every day. The objective of this course is to provide participants with a comprehensive understanding of the fundamental concepts and principles of AI and machine learning, as well as the practical applications of these technologies. This course will also equip participants with the knowledge, skills, and tools necessary to understand, develop, and deploy AI and machine learning systems in a responsible and effective manner.
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User generated content (UGC) – text, images, speech, and video created by users – from myriad sources such as social media and corporate activities constitutes a large chunk of big data today. Yet, UGC is severely underutilized in decision making, and only recently has attention been focused on the powerful insights and predictions we can generate from user generated content analytics (UGCA). This workshop is designed to showcase the power and potential of UGCA in brand positioning, campaign management, new product design, and a gamut of other managerial applications. Participants will become familiar with important theoretical concepts related to big data such as the long tail of demand, diversity of consumer preferences in a digital world, competitive structure perceived by customers, etc., and learn to use appropriate analytics techniques such as advanced natural language processing, image analytics, and prediction with unstructured data for improved business outcomes. The course will teach several hands-on skills:
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The course culminates in a project competition, where teams act as analytics consultants and complete a project on UGCA, collecting primary data through scraping and API access, applying analytics methods learnt in the workshop, and extracting actionable advice (the “Aha” moments) for fictitious business clients. The grand finale will involve 10-minute presentations by each team, which will be judged by a team of faculty members. Official JAGSoM certificates will be presented to the winner, runner-up, and honourable mention teams at the end of the presentations.
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“Big data” is the massive amount of data available to organizations that—because of its volume and complexity—is not easily managed or analysed by many business intelligence tools. Cloud Computing: refers to the processing of anything, including Big Data Analytics, on the “cloud”. The “cloud” is just a set of high-powered servers from one of many providers. They can often view, and query large data sets much more quickly than a standard computer could. Cloud Computing allows us to use state-of-the-art infrastructure and only pay for the time and power that we use! Cloud application development is also fuelled by Big Data. Without Big Data, there would be fewer cloud-based applications, since there wouldn’t be any real necessity for them. Big Data is often collected by cloud-based applications, as well! This course builds on the concepts of cloud technology and discusses how big data algorithms can be run on the cloud to leverage the advantages of both technologies.
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Prescriptive analytics is the study of data and how it can help us take better business decisions. The applications of prescriptive analytics are numerous, ranging from product manufacturing decisions (how much to produce of each product) to locations of plants and warehouses, staffing decisions, inventory management, better financial investment in the face of risk, and many others. Usually in a business scenario, the decision maker would be faced with the objectives of either maximizing profit, or minimizing cost – in the presence of several constraints. It helps students to understand the importance of prescriptive analytics and optimization. Students will be able to apply the techniques successfully to appropriate business problems thereby driving successful business outcomes. Students will learn optimization techniques in different application areas.
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The course on Business Requirement Analysis is designed to equip students with the knowledge, skills, and tools required to identify business problems and opportunities, and to develop effective solutions to improve business processes, products, and services. The course covers topics such as business requirements gathering, stakeholder analysis, process modelling, data analysis, and project management. Students should be able to apply these skills to analyse business scenarios, develop business cases, and make recommendations to improve organizational performance. Upon completion of the course, students should be able to work as effective Business Analysts in various industries and contribute to the success of their organizations.
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Cloud computing is becoming an increasingly integral part of many companies’ business and technology strategy. Cloud services help companies turn IT resources into a flexible, elastic, and self-service set of resources that they can more easily manage and scale to support changing business needs. Cloud computing serves different needs for different constituents within an organization. For business leaders, cloud computing is a cost-effective way to leverage IT resources to prototype and implement strategic change. For an IT organization, the cloud is a platform that allows it to be significantly more proactive and responsive when it comes to supporting strategic business imperatives. While IT is leading the charge in focusing on best practices that support the balanced use of public, private, and data center resources — the emerging world of hybrid computing — don’t lose sight of the fact that cloud is just as much about business model transformation as it is about technology transformation. In fact, many companies find that the cloud helps to support increased collaboration between business and IT leaders enabling them to adjust more quickly to changing market dynamics.
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| S.No. | Course Type | Course Name | Course Snapshot |
|---|---|---|---|
| 1 | Elective | Business Data Visualization | This course is covered in three modules. The first module contains the introduction to descriptive analytics in which students will learn about data pre-processing and business intelligence. The second module gives the overview of Tableau data visualization, in which students will perform data cleaning, data transformation and data visualization using SPSS/Tableau/Excel software. The last module is based on drawing insights from statistical measures in which students will cover summarization, dash boards and storytelling part using tableau/excel. |
| 2 | Elective | Coding Business Applications Using R And Python | This course is covered in eight modules. First module based on understanding python and familiarizing with jupyter notebook. Second module consists of fundamentals of python- data structures and libraries and third module consist of control structures and functions of python interface. Fourth module is based on string manipulation and visualization in which students will learn about negative indexing, concatenation, and different string methods. Fourth module is on visualization in which students will plot different graphs such as bar graph, scatterplot and histogram etc. using python interface. In fifth module students will explore the R interface by learning the fundamentals of R -data structures. In sixth module students will learn Control Statements and Loops in R interface. In seventh module students will work on group manipulation and data reshaping on different data frames. In eighth module students will plot different graphs as histogram, bar plot, boxplot and scatterplot using ggplot 2 library in R studio. |
| 3 | Elective | Project Management |
Knowledge in Project management helps professionals to take up job role involving leading, directing, managing, and executing projects for achieving the organizational goals. The scope is spread wide in various industries such as Consulting, IT and Telecom, Marketing and Sales, eBusiness, Service and Outsourcing and many more for professionals with project management as their role in the organization. On Completion of this course, Job experienced students can drive projects by taking up roles such as Project manager, consultant, Program manager, Operational Excellence head, Owner. |
| 4 | Elective | Business Forecasting | This course is covered in two modules. First module consists of the introduction to types of forecasting model. Conditional Formatting and charting options are also covered in this module. In the second module linear regression model, problems caused by multicollinearity eliminating or reducing spurious trends, non-linear regression, formulating a multivariate regression equation, methods of smoothing data, methods of seasonal adjustments and ARIMA, ARMA, GARCH model will be covered. Students will also learn about Univariate time series methods, Time-series decomposition model and Linear and Non-linear trends in the second module. |
| 5 | Elective | Data Management Systems And Data Engineering | This course is covered in five modules. The first module gives the introduction to data and database management. In the second module, students will learn about the database design as data models, ER diagrams, entity types and they will also learn to create different databases in MS Access/SQL Server/Oracle. In the third module, students will learn about the Structured Query Language (SQL) and joins to query multiple tables in Ms access/SQL server/oracle. In the fourth module, students will learn about Extract-Transform-Load (ETL), multidimensional data model and different types of schemas. In fifth module students will explore Advanced data models by learning about the XML databases and its applications in business. Students will also learn big data Analytics and Hadoop/MapReduce briefly. |
| 6 | Elective | Mathemetical Foundations Of Analytics | Content will be updated soon! |
| S.No. | Name of Core Course | Course Snapshot |
|---|---|---|
| 1 | Introduction To Digital Business |
Introduction to Digital Business allows students to Understand business transformation, platform business and shared economy and Marketing 4.0. Digital business models. Strategies to create Network Value learning how to unlock customer value chain. Remapping the industry and the enterprise for digital transformation. Customer in Digital age and digitization of customer experiences. Understand Digital business specific customer journeys and buying behavior. Learn Digital engagement. Critically examine how firms are responding to customers. Digital Infrastructure and Emerging Technologies. Understanding the requirements of digital infrastructure. Learning the aspects of building an e-commerce platform. Analyze use of servers, software, and choosing the hardware for digital business. Understand the impact of emerging technologies leading industries in a state of constant flux and competition. Challenges of Digital Business and Future. Learn how digital creates challenges of non-linear value creation and a disproportionate competitive edge. Understand the strategic opportunities through innovative practices. Understand the threats factors of Digital Business. |
| 2 | Managerial Decision Making |
Introduction to Data Driven Decision Making teaches students to understand the concept of information and information systems. This module enables students to form decisions from data, and using Excel to solve optimization problems and what-if analyses. Review of Probability Distribution; Sampling Methods. Here the concept of Normal distribution and its application are taught. Introduction to various sampling methods and its theory. Concepts of standard error of mean and central limit theorem are revisited. Decision making through data visualization and data interpretation. The main objective of this module is to cover the concept of confidence interval for mean and proportion. Data interpretation for decision making through hypothesis testing – Single sample tests, Two sample tests and multiple samples tests. Simple Linear Regression Model and its application in different industries are taught. Understanding the concept behind least square methods through excel, correlation, regression, coefficient of determination and testing the model for significance is covered. The concept of resource optimization. Different types of optimization techniques. Decision variables, constraints, and objective function. Formulation of an optimization problem – algebraic model and SOLVER model. Using Excel SOLVER to optimize the functions. Sensitivity analysis and its interpretation. |
JAGSoM is amongst the select few Business Schools in India with an International Profile: AACSB Accredited & QS Ranked for Marketing, Finance and Analytics programs.
