UPES Dissertation & Synopsis Guide for MBA in Business Analytics

UPES Dissertation & Synopsis for MBA in Business Analytics

Summary

Writing a UPES Dissertation for an MBA in Business Analytics is a key academic task. This program trains you to turn data into decisions. The dissertation gives you a chance to apply models, use real datasets, and show you can solve business problems with analytics. Students from Delhi, Mumbai, Bangalore, Pune, Hyderabad, Chennai, Dehradun, Kolkata and other cities choose this path because demand for data skills is high across industries.

📑 Table of Contents

• 🔹 UPES Dissertation & Synopsis for MBA in Business Analytics
• 🔹 Program overview
• 🔹 Why choose Business Analytics?
• 🔹 Core subjects and toolset
• 🔹 Synopsis and dissertation process
• 🔹 Formatting and plagiarism rules
• 🔹 Suggested dissertation topics
• 🔹 Summary table (subjects → topics)
• 🔹 How we support students
• 🔹 Frequently Asked Questions (FAQs)

Program overview

UPES Online offers a two-year MBA with specialisation in Business Analytics for early career and working professionals. The course covers predictive modelling, data visualisation, big data methods, and business optimisation. You learn Python for data work, analytics tools, and how analytics supports strategy. The final submission requires a UPES Synopsis first, then the full dissertation after approval.

Why choose Business Analytics?

The need for analytics talent in India is rising fast. E-commerce and digital services create large daily data flows. Recruiters want graduates who can read patterns, build forecasts, and propose actions. Facts that show growth:

• E-commerce handles millions of transactions daily.
• India’s big data market is set to grow strongly by 2025.
• AI and automation create new analytics roles in every sector.

An MBA in Business Analytics gives you a mix of business sense and technical skills, so you can take analyst or manager roles in finance, retail, healthcare, and tech. A targeted dissertation proves you can design and complete a data project end-to-end.

Core subjects and toolset for UPES Business Analytics Program

The program includes subjects that directly feed dissertation choices:

• Data Environment & Management — databases, ETL, data quality.
• Programming for Business Analytics (Python) — data cleaning, modelling, libraries like pandas and scikit-learn.
• AI for Managers — practical AI applications without heavy coding.
• Social and Web Analytics — social media and web traffic studies.
• Business Optimization — linear programming, simulation and decision science.
• Big Data Analytics — Hadoop, Spark, large-scale processing.
• NLP (Natural Language Processing) — text analytics, sentiment analysis.
• Data Visualization — dashboards and storytelling with data.
• Strategic Management & Innovation — link analytics to business value.
• Dissertation — capstone project with original analysis.

Toolset: Python, Jupyter, SQL, Excel, Tableau/Power BI, basic R or SPSS depending on the method.

Synopsis and dissertation process at UPES

Start with a clear synopsis. The synopsis should state:

• Research title.
• A short problem statement.
• Objectives.
• Data sources and methods.
• Expected output.

UPES requires synopsis approval before you begin full research. The university reviews the proposal and either approves, requests changes, or asks for clarification. After approval you work on the full dissertation: literature, methodology, data work, analysis, findings and recommendations. Plan a timeline with weekly milestones for data collection, model building, writing, and revisions.

Formatting and plagiarism rules

UPES uses its own plagiarism check. Keep these rules in mind:

• Aim for similarity under 15% (Low Risk).
• Use Times New Roman, size 12, 1.5 spacing unless UPES says otherwise.
• Keep margins 1 inch and follow the chapter order UPES expects.
• Cite all sources; use APA/Harvard if required.
• Use clear captions for tables and figures and list all data sources.

Good paraphrasing, correct citations, and original explanations keep similarity low and show academic honesty.

Suggested dissertation topics

Choose topics that match your skills and available data. Here are practical ideas:

Predictive analytics

• Sales forecasting for an e-commerce category using machine learning.
• Churn prediction for subscription services with survival analysis.

Optimization & operations

• Inventory optimisation using demand forecasting and safety stock models.
• Route optimisation for last-mile delivery using vehicle routing methods.

Customer analytics & marketing

• Customer segmentation using clustering and RFM analysis.
• Personalised recommendation systems and A/B testing results.

Web & social analytics

• Sentiment analysis of product reviews and sales impact.
• Web traffic analysis to improve conversion rates.

NLP and text mining

• Topic modelling for customer feedback to guide product changes.
• Automated classification of support tickets to reduce response time.

AI for managers

• Business case study: AI adoption roadmap for a mid-size firm.
• Evaluating ROI of predictive maintenance in manufacturing.

Pick a topic with public or company data you can access. If you need primary data, build a realistic plan and backup options.

Summary table (subjects → topics)

Core Subject	Sample Dissertation Topics
Programming for Business Analytics (Python)	Sales forecasting using time-series ML
Business Optimization	Inventory optimisation with stochastic demand
NLP	Tweet sentiment and brand health analysis
Data Visualization	Dashboards for KPI tracking in retail

How we support students

Support is practical and stepwise. Help includes:

• Shortlist topics with data checks.
• Draft the synopsis so it meets UPES format.
• Suggest data sources and sampling methods.
• Review chapter drafts and give feedback on clarity and results.
• Check references and format before submission.

Contact Us

📞 +91-9911899400 (WhatsApp available)

Frequently Asked Questions

Q1. Is synopsis approval mandatory before starting the dissertation?

Yes. UPES requires an approved synopsis before you begin the full dissertation. Prepare a clear proposal with title, objectives, method and expected outcomes to get approval quickly.

Q2. What is the plagiarism limit for UPES dissertations?

UPES uses inbuilt similarity checks. Keep the similarity index below 15% to be in the Low Risk zone. Use correct citations and paraphrase properly to reduce similarity.

Q3. Which tools are best for Business Analytics dissertations?

Common tools include Python (pandas, scikit-learn), SQL, Excel and Tableau/Power BI. Use tools that match your method and that you can explain in the methodology chapter.

Q4. How long does the dissertation process usually take?

A typical timeline is 2–3 months from approved synopsis to final draft, depending on data access, analysis complexity and number of revision rounds.

Q5. How do I choose a suitable dissertation topic?

Pick a topic that fits your interest, has available data, and is feasible in the time you have. Check recent studies and confirm data sources before finalising the title.

Q6. How will you support me through the project?

We guide every step: shortlist topics, check data feasibility, draft a UPES-ready synopsis, support literature review, advise on methods and tools, review chapter drafts, help reduce plagiarism risk, and check final formatting before submission. We do not submit on your behalf.

Q7. Can working professionals complete the dissertation on time?

Yes. With a clear plan and weekly milestones, working students can complete the project. We offer flexible support and split tasks into manageable steps to fit your schedule.

Q8. Do you help with data collection and surveys?

Yes. We help design surveys, suggest sample sizes, recommend secondary data sources, and provide data-cleaning guidance so you can collect usable data for analysis.

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