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MBA in Business Analytics at Davan Institute of Advanced Management Studies
Davangere, Karnataka
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About the Specialization
What is Business Analytics at Davan Institute of Advanced Management Studies Davangere?
This Business Analytics MBA program at Davan Institute of Advanced Management Studies focuses on equipping students with advanced analytical skills to derive actionable insights from complex data. Given India''''s burgeoning digital economy and data-driven decision-making across sectors like e-commerce, finance, and healthcare, this specialization is highly relevant. It differentiates itself by integrating both theoretical knowledge and practical application of analytics tools for strategic business advantage, meeting the surging industry demand for data-savvy professionals.
Who Should Apply?
This program is ideal for fresh graduates seeking entry into the rapidly expanding data analytics field, especially those with a quantitative aptitude. It also caters to working professionals aiming to upskill and leverage data for career advancement in roles like business intelligence, marketing analytics, or financial modeling. Individuals transitioning into data-centric roles from traditional management backgrounds will find the structured curriculum beneficial, provided they have a strong interest in technology and problem-solving.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Data Analyst, Business Intelligence Analyst, Marketing Analyst, Financial Modeler, or Consultant in various Indian companies and MNCs. Entry-level salaries typically range from INR 4-7 lakhs per annum, with experienced professionals commanding upwards of INR 10-20 lakhs. The growth trajectories often lead to leadership positions like Lead Data Scientist or Head of Analytics, aligning with industry certifications such as those from Tableau, Microsoft Azure, or Google Cloud.
Student Success Practices
Foundation Stage
Master Core Quantitative & Management Fundamentals- (Semester 1-2)
Dedicate time to thoroughly understand Quantitative Methods, Managerial Economics, and Accounting for Managers. Form study groups, practice problems regularly, and utilize online resources like Khan Academy or NPTEL for conceptual clarity. Strong foundational skills are crucial for advanced analytics.
Tools & Resources
Khan Academy (Statistics, Economics), NPTEL (Management courses), Peer Study Groups
Career Connection
A solid grasp of these basics ensures you can interpret business scenarios accurately and apply analytical tools meaningfully, which is essential for any business analytics role.
Develop Proficiency in Spreadsheet Modeling- (Semester 1-2)
Beyond classroom learning, actively practice advanced Excel functions, pivot tables, and Solver for optimization. Work on case studies involving financial modeling, inventory optimization, and decision trees. This builds a practical skill highly valued in many entry-level analytics positions.
Tools & Resources
Microsoft Excel, Udemy/Coursera Excel courses, Financial Modeling books
Career Connection
Many Indian companies still rely heavily on Excel for initial data analysis and reporting. Proficiency here provides immediate employability and a stepping stone to more advanced tools.
Engage in Early Analytical Problem Solving- (Semester 1-2)
Participate in local college-level case competitions or data-oriented quizzes. Start exploring basic datasets on platforms like Kaggle and try to derive simple insights. This cultivates a problem-solving mindset and introduces real-world data challenges early on.
Tools & Resources
Kaggle (beginner datasets), Local college hackathons, Business case study books
Career Connection
Early exposure to problem-solving builds critical thinking, a core skill for any analyst, making you more attractive to recruiters looking for proactive learners.
Intermediate Stage
Master R/Python and Data Visualization Tools- (Semester 3)
Deepen your programming skills in R and Python beyond basic syntax, focusing on data manipulation (Pandas, dplyr) and statistical modeling. Simultaneously, become proficient in a data visualization tool like Tableau or Power BI by creating interactive dashboards for diverse datasets. Complete at least 2-3 personal projects.
Tools & Resources
RStudio, Anaconda Python, Tableau Public/Power BI Desktop, DataCamp/DataQuest
Career Connection
These are industry-standard tools for data scientists and analysts in India. Mastery directly translates to eligibility for specialized analytics roles and impressive portfolio projects.
Undertake Industry-Relevant Mini-Projects- (Semester 3)
Collaborate with peers to identify real-world business problems (e.g., from local SMEs, online challenges) and apply Business Analytics concepts from Data Warehousing, Data Mining, and Predictive Modeling to solve them. Document your approach, findings, and business recommendations thoroughly.
Tools & Resources
Kaggle competitions, Online business case studies, Local business contacts (if feasible)
Career Connection
Practical projects demonstrate your ability to apply learned concepts, showcasing initiative and problem-solving skills crucial for internships and job interviews.
Network and Seek Mentorship- (Semester 3)
Attend industry webinars, guest lectures, and local data science meetups (online or offline if available in Davangere/nearby cities). Connect with professionals on LinkedIn, especially alumni working in analytics. Seek guidance on career paths and skill development.
Tools & Resources
LinkedIn, Meetup.com (for local tech groups), Industry webinars
Career Connection
Networking opens doors to internship opportunities, valuable industry insights, and potential job referrals in the competitive Indian job market.
Advanced Stage
Build a Strong Portfolio of End-to-End Analytics Projects- (Semester 4)
Develop 3-5 comprehensive projects, integrating skills from Big Data Analytics, Web & Social Media Analytics, and Machine Learning. Each project should cover data collection, cleaning, modeling, visualization, and actionable insights. Host these on GitHub for recruiters to review.
Tools & Resources
GitHub, Cloud platforms (AWS/Azure/GCP free tier), Advanced ML libraries
Career Connection
A robust project portfolio is paramount for showcasing your expertise, especially when applying for roles at top analytics firms and tech companies in India.
Intensive Interview and Aptitude Preparation- (Semester 4)
Practice quantitative aptitude, logical reasoning, and verbal ability rigorously for placement tests. Prepare for technical interviews by reviewing core analytics concepts, common algorithms, SQL, and case study discussions. Participate in mock interviews.
Tools & Resources
Online aptitude platforms (IndiaBix), LeetCode/HackerRank (for SQL/Python), Placement training cells
Career Connection
This focused preparation significantly increases your chances of clearing written tests and technical rounds, leading to successful placements in desired companies.
Explore Specialised Industry Applications and Certifications- (Semester 4)
Identify a specific industry (e.g., Finance, Retail, Healthcare) or domain (e.g., NLP, Computer Vision) within analytics that interests you. Pursue relevant online certifications (e.g., Google Data Analytics, IBM Data Science, AWS Machine Learning Specialty) to demonstrate expertise in that niche.
Tools & Resources
Coursera/edX (specialized courses), Industry-specific forums, Certification exam guides
Career Connection
Specialization and certifications enhance your resume, making you a more targeted candidate for specific roles and industries, potentially commanding higher starting salaries in the Indian market.
Program Structure and Curriculum
Eligibility:
- Any graduate with 50% marks (45% for SC/ST/Cat-1 candidates of Karnataka) from a recognized university.
Duration: 4 semesters / 2 years
Credits: 104 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MBA 1.1 | Management Process and Organizational Behaviour | Core | 4 | Management concepts and functions, Organizational behaviour fundamentals, Individual behaviour and perception, Group dynamics and team building, Motivation theories and leadership, Organizational change and development |
| MBA 1.2 | Managerial Economics | Core | 4 | Introduction to managerial economics, Demand and supply analysis, Production and cost analysis, Market structures and competition, Pricing strategies and decisions, Macroeconomic indicators and business cycles |
| MBA 1.3 | Accounting for Managers | Core | 4 | Financial accounting principles, Cost accounting concepts and methods, Management accounting tools, Financial statement analysis, Budgeting and budgetary control, Working capital management |
| MBA 1.4 | Quantitative Methods for Business | Core | 4 | Probability distributions and theory, Statistical inference and hypothesis testing, Regression and correlation analysis, Time series analysis and forecasting, Decision theory and analysis, Operations research techniques (LP, queuing) |
| MBA 1.5 | Business Environment | Core | 4 | Economic environment and policy, Socio-cultural and demographic factors, Political, legal and regulatory framework, Technological environment and innovation, Global business environment, Corporate social responsibility |
| MBA 1.6 | Business Communication | Core | 4 | Communication process and barriers, Oral communication skills, Written communication in business, Business correspondence and reports, Presentation skills and public speaking, Cross-cultural communication |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MBA 2.1 | Human Resource Management | Core | 4 | HRM functions and strategic role, Human resource planning and job analysis, Recruitment, selection and onboarding, Training and development, Performance appraisal and management, Compensation and benefits management |
| MBA 2.2 | Financial Management | Core | 4 | Goals of financial management, Sources of finance and capital structure, Investment decisions and capital budgeting, Dividend policy decisions, Risk and return analysis, Leverage analysis |
| MBA 2.3 | Marketing Management | Core | 4 | Marketing concepts and philosophies, Market segmentation, targeting, positioning, Product and brand management, Pricing strategies and decisions, Promotion (IMC) and advertising, Distribution channels and logistics |
| MBA 2.4 | Research Methods for Management | Core | 4 | Research design and types, Data collection methods (primary/secondary), Sampling techniques and design, Data analysis and interpretation, Report writing and presentation, Ethical considerations in research |
| MBA 2.5 | Production and Operations Management | Core | 4 | Operations strategy and productivity, Product and process design, Facility location and layout, Capacity planning and aggregate planning, Inventory management techniques, Quality management and control |
| MBA 2.6 | Entrepreneurship and Ethics | Core | 4 | Entrepreneurial process and motivation, Business plan development, Venture creation and financing, Small business management, Business ethics and values, Corporate governance and social responsibility |
| MBA 2.7 | Internship and Viva-Voce | Practical | 4 | Industry exposure and practical learning, Application of theoretical concepts, Problem identification and solution, Report writing on internship experience, Presentation of findings, Viva-voce examination |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MBA 3.1 | Strategic Management | Core | 4 | Strategic analysis and external environment, Internal analysis and core competencies, Strategy formulation (corporate, business, functional), Strategy implementation and control, Strategic alliances and mergers, Global strategic management |
| MBA 3.2 | Legal Aspects of Business | Core | 4 | Indian Contract Act, 1872, Sale of Goods Act, 1930, Consumer Protection Act, 1986, Companies Act, 2013, Intellectual Property Rights (IPR), Cyber Law and E-commerce |
| MBA E3.1 | Data Warehousing & Data Mining | Elective (Business Analytics) | 4 | Data warehousing architecture, ETL process and data quality, OLAP operations and data cube, Introduction to data mining techniques, Classification algorithms (Decision Trees, Naive Bayes), Clustering algorithms (K-Means, Hierarchical) |
| MBA E3.2 | R & Python for Analytics | Elective (Business Analytics) | 4 | Introduction to R programming, Data manipulation in R (dplyr, tidyr), Introduction to Python programming, Data manipulation in Python (Pandas, NumPy), Statistical analysis with R and Python, Basic machine learning libraries (scikit-learn) |
| MBA E3.3 | Spreadsheet Modeling for Business Decisions | Elective (Business Analytics) | 4 | Advanced Excel functions for data analysis, What-if analysis and Goal Seek, Optimization models (Solver), Simulation modeling (Monte Carlo), Financial modeling in spreadsheets, Decision trees and sensitivity analysis |
| MBA E3.4 | Data Visualization | Elective (Business Analytics) | 4 | Principles of effective data visualization, Types of charts and graphs, Visualizing relationships and distributions, Designing interactive dashboards, Storytelling with data, Tools for data visualization (Tableau, Power BI concepts) |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MBA 4.1 | International Business | Core | 4 | Theories of international trade, Foreign direct investment (FDI), Global financial markets and institutions, International marketing strategies, Cross-cultural management, International business environment and trade agreements |
| MBA E4.1 | Predictive Modeling | Elective (Business Analytics) | 4 | Linear and logistic regression, Time series forecasting models (ARIMA, Exponential Smoothing), Decision trees and Random Forests, Ensemble methods (Bagging, Boosting), Model evaluation metrics, Cross-validation techniques |
| MBA E4.2 | Big Data Analytics | Elective (Business Analytics) | 4 | Introduction to Big Data concepts, Hadoop ecosystem (HDFS, MapReduce), Spark architecture and applications, NoSQL databases (MongoDB, Cassandra concepts), Real-time data processing, Big Data applications in various industries |
| MBA E4.3 | Web & Social Media Analytics | Elective (Business Analytics) | 4 | Web analytics metrics and KPIs, Google Analytics implementation and reporting, Social media platforms and data sources, Social media listening and monitoring, Sentiment analysis and opinion mining, Measuring social media campaign effectiveness |
| MBA E4.4 | Machine Learning for Business | Elective (Business Analytics) | 4 | Supervised learning (Classification, Regression), Unsupervised learning (Clustering, Dimensionality Reduction), Reinforcement learning basics, Feature engineering and selection, Model deployment and API integration, Ethical considerations in AI and ML |
| MBA 4.7 | Project Report and Viva-Voce | Project | 8 | Identification of a business research problem, Literature review and theoretical framework, Research methodology design, Data collection, analysis, and interpretation, Comprehensive project report writing, Presentation of findings and viva-voce examination |
