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.
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.
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.
📞 +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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