🎓 The only programme in Pune that combines MBA-level business skills with a full Data Science technical track  |  Reserve Your Seat on WhatsApp →
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🎓 Business Intelligence — MBA-Level Data Science Programme

The MBA That
Actually Teaches
Data Science.

Traditional MBAs give you business theory with almost no technical skills. Data science bootcamps give you Python and ML with almost no business context. The Aapvex MBA in Data Science is the only programme in Pune that does both — rigorously. You will leave able to build machine learning models, run statistical analysis, and present data-driven business decisions to a boardroom. That combination is exactly what the market is paying Rs.18–40 LPA for.

6Months
12+Live Projects
₹6–40LSalary Range
100%Placement Support
💬 WhatsApp Us Now 📞 Call 7796731656

📋 Programme At a Glance

MBA in Data Science — 6-Month Full Programme

⏱Duration: 6 Months
📍Mode: Online + Pune Classroom
💰Fee: From ₹34,999 EMI Available
🗓Batches: Weekend & Weekday
🎓Level: Beginner to Advanced
🏆Certificate: ✓ Included
📁Projects: 12+ Live Projects
🤝Placement: 100% Support
🎓 Reserve Your Seat
94%of senior data rolesrequire business acumen + tech skills
12+real-world projectsbuilt during the programme
₹6–40Lsalary rangefor MBA Data Science graduates
200+hiring companiesin Aapvex placement network
The Problem This Solves

Why Pure Data Science Bootcamps
and Traditional MBAs Both Fall Short

Companies do not just need people who can code. They need people who can code and understand business. Here is exactly where the gaps are — and how this programme fills them.

Gap
🔧

Pure Data Scientists Miss Business Context

A data scientist who cannot connect their model to a business outcome is a tool user, not a decision-maker. Companies hiring for Rs.25L+ data science leadership roles want professionals who understand revenue, cost, competitive strategy, and how their models affect actual business results.

Gap
📚

Traditional MBAs Skip Actual Data Skills

An MBA graduate who cannot query a database, run a regression, or interpret a machine learning model is increasingly limited in what they can contribute to data-driven organisations. Telling someone to 'use data' and not knowing how to use it yourself is a credibility problem.

Solution
🎯

This Programme Bridges Both Gaps

The MBA in Data Science combines a genuine technical track — Python, ML, SQL, visualisation — with a management track covering business strategy, stakeholder communication, and data leadership. The result is a profile that qualifies for roles neither pure techies nor traditional MBA graduates can fill.

Opportunity
📈

The Market Is Paying a Premium

Data Science Manager, Head of Analytics, Chief Data Officer, Business Intelligence Lead — these roles command Rs.25-80 LPA precisely because they require both technical credibility and business acumen. Most technical candidates lack business skills. Most MBA candidates lack technical depth. You will have both.

Local Demand
🏢

Pune Companies Specifically Need This

Pune's technology sector — Infosys BPM, Wipro, KPIT, Persistent, Bajaj Finserv, Tech Mahindra — is expanding its data and analytics functions rapidly. These companies need analysts and managers who can sit in business meetings in the morning and in technical review sessions in the afternoon.

Outcome
🚀

Career Acceleration, Not Just Career Entry

For working professionals, this programme is a career accelerator — adding skills that justify promotion to senior analyst, analytics manager, or data product manager roles. The business track makes this programme genuinely different from an upskilling bootcamp.

Who Should Join

Six Profiles This Programme
Is Designed For

🎓

Recent Graduates (Any Stream)

Engineering, science, commerce, or arts graduates who want a comprehensive data science + business programme that directly qualifies them for analyst and data science roles at Indian technology companies — without spending 2 years on a full-time MBA.

💼

Working Professionals (2–5 Years Exp)

Mid-career professionals in IT, operations, finance, marketing, or HR who want to move into data and analytics roles. Your domain knowledge plus new data science skills is a powerful combination — more valuable than a fresh data science graduate with no business experience.

📊

Existing Analysts Wanting to Upgrade

Excel analysts, reporting analysts, and BI analysts who know their way around data but want to add machine learning, Python, and advanced analytics to their profile — to qualify for data scientist and analytics lead roles.

🔄

Career Switchers into Data

Professionals from teaching, banking, logistics, or any non-technology domain who want to transition into the data and analytics economy. The business management track makes this programme particularly suitable for career switchers because it leverages existing professional experience.

🏦

MBA Students Wanting Technical Depth

MBA students or graduates who feel their programme did not give them the technical data skills that companies are actually asking for. This programme adds Python, SQL, machine learning, and statistics to your MBA profile — making you genuinely competitive for data leadership roles.

🌐

Entrepreneurs and Startup Founders

Founders who need to understand their business data — customer analytics, revenue modelling, product metrics, growth analysis — and who want to build data-driven decision making into their organisation without depending entirely on a data team.

Full Programme Curriculum

12 Modules Across 6 Months —
Business + Technical, Fully Integrated

Every module includes real-world projects. Technical modules and business modules alternate to build both tracks simultaneously — so you are always connecting what you learn to actual business problems.

1

Python for Data Science — Programming Foundations

3.5 Weeks
  • Python syntax, data types, functions, and object-oriented programming for data work
  • NumPy: arrays, vectorised operations, broadcasting — the maths layer underneath all data science
  • Pandas: DataFrames, Series, reading CSV/Excel, filtering, groupby, merge, pivot_table
  • Matplotlib and Seaborn: histograms, scatter plots, box plots, heatmaps, pair plots
  • Exploratory Data Analysis (EDA): understanding data quality, distributions, and relationships before modelling
  • Handling missing data: imputation strategies and when to drop vs fill
  • Feature engineering: creating new variables that improve model performance
  • Jupyter Notebook best practices for reproducible, well-documented data analysis
  • Real datasets used: Indian e-commerce sales data, employee attrition dataset, credit card transactions
📁 Module Project Project: Complete EDA and visualisation report on a real Indian business dataset — uncovering 5 actionable business insights with visual evidence.
2

Business Statistics & Probability for Data Professionals

2.5 Weeks
  • Descriptive statistics: mean, median, mode, variance, standard deviation, skewness, kurtosis
  • Probability foundations: events, conditional probability, Bayes' theorem applied to business scenarios
  • Probability distributions: Normal, Binomial, Poisson — when each applies in business data
  • Sampling theory: why sample size matters, Central Limit Theorem, sampling bias
  • Hypothesis testing: null and alternative hypotheses, Type I and Type II errors, p-values
  • Statistical tests in practice: t-test (comparing means), chi-square (categorical variables), ANOVA (multiple groups)
  • Correlation analysis: Pearson, Spearman — measuring relationships without assuming causation
  • Regression analysis foundations: simple linear regression, interpretation of coefficients
  • A/B testing: designing experiments, calculating statistical significance, making decisions from test results
  • Statistical storytelling: communicating statistical findings to non-technical business stakeholders
📁 Module Project Project: Design, conduct, and present an A/B test analysis for a real business scenario — determining whether a website change significantly improved conversion rate.
3

SQL for Data Analysis — Querying and Managing Data

2 Weeks
  • Database fundamentals: tables, rows, columns, primary keys, foreign keys, entity-relationship diagrams
  • Basic SQL: SELECT, FROM, WHERE, ORDER BY, LIMIT — retrieving and filtering data
  • Aggregation: GROUP BY, HAVING, COUNT, SUM, AVG, MIN, MAX — summarising data
  • Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN — combining tables
  • Subqueries and CTEs (Common Table Expressions): writing readable, modular SQL
  • Window functions: RANK(), ROW_NUMBER(), LAG(), LEAD(), RUNNING_TOTAL — the most powerful SQL feature for analytics
  • String functions, date functions, and conditional logic (CASE WHEN) in SQL
  • SQL for business reporting: writing queries that power dashboards and management reports
  • Performance optimisation: indexing, query planning, avoiding full table scans
  • Working with real databases: practising on MySQL with actual business datasets
📁 Module Project Project: Build a complete sales analytics database query library — 15 business questions answered with well-commented SQL, including monthly cohort analysis, product performance ranking, and customer LTV calculation.
4

Machine Learning — Supervised Learning Algorithms

3 Weeks
  • ML workflow: problem framing, data preparation, feature selection, model training, evaluation, and deployment
  • Linear Regression: ordinary least squares, assumptions, regularisation (Ridge, Lasso, ElasticNet)
  • Logistic Regression: binary and multinomial classification, sigmoid function, log-odds interpretation
  • Decision Trees: splitting criteria (Gini impurity, entropy), overfitting, pruning
  • Random Forests: bootstrap aggregation, feature importance, out-of-bag error estimation
  • Gradient Boosting: XGBoost, LightGBM, CatBoost — the algorithms that win Kaggle competitions
  • Support Vector Machines: the hyperplane concept, kernel trick, soft margin SVM
  • k-Nearest Neighbours: distance metrics, the curse of dimensionality
  • Model evaluation: accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix interpretation
  • Cross-validation: k-fold, stratified k-fold, time-series cross-validation
  • Hyperparameter tuning: Grid Search, Random Search, Bayesian Optimisation
  • Feature selection: mutual information, correlation-based selection, recursive feature elimination
📁 Module Project Project: Build a customer churn prediction model for a telecom company — comparing 5 algorithms, selecting the best using ROC-AUC, and presenting business recommendations based on the model's output.
5

Machine Learning — Unsupervised Learning & Feature Engineering

2 Weeks
  • K-Means clustering: the algorithm, choosing k (elbow method, silhouette score), business applications
  • Hierarchical clustering: agglomerative approach, dendrograms, cutting the tree
  • DBSCAN: density-based clustering for non-spherical clusters and outlier detection
  • Principal Component Analysis (PCA): variance explained, loadings, dimensionality reduction for visualisation
  • t-SNE and UMAP for high-dimensional data visualisation
  • Feature engineering for ML: polynomial features, interaction terms, target encoding, embedding categorical variables
  • Handling class imbalance: SMOTE, class weights, threshold adjustment
  • Anomaly detection: isolation forests, one-class SVM, z-score methods for fraud and quality control
  • Market basket analysis: association rules, support, confidence, lift — product recommendation logic
  • Customer segmentation project: RFM analysis (Recency, Frequency, Monetary) for e-commerce customer base
📁 Module Project Project: Complete customer segmentation analysis for a retail company — identifying 4-6 distinct customer segments, characterising each with business language, and recommending a differentiated marketing strategy for each segment.
6

Data Visualisation — Tableau & Power BI for Business

2 Weeks
  • Why visualisation matters: the difference between data and insight, and how charts communicate both
  • Tableau fundamentals: connecting to data, building views, calculated fields, parameters
  • Tableau advanced: LOD expressions (FIXED, INCLUDE, EXCLUDE) for complex aggregations across different granularities
  • Tableau dashboards: layout, interactivity, filters, actions, and device-specific design
  • Power BI fundamentals: the Power BI ecosystem (Desktop, Service, Embedded), data modelling in Power BI
  • Power Query (M language): data transformation, unpivoting, merging queries
  • DAX fundamentals: CALCULATE, FILTER, ALL, RELATED — the formulas that make Power BI powerful
  • Power BI dashboards: report pages, KPI cards, conditional formatting, drill-through
  • Storytelling with data: choosing the right chart type for each message, avoiding misleading visualisations
  • Dashboard design principles: whitespace, colour psychology, hierarchy, and information density
📁 Module Project Project: Build a complete executive sales dashboard in both Tableau and Power BI for the same dataset — presenting regional performance, trend analysis, product mix, and forward-looking projections.
7

Deep Learning & Neural Networks

2.5 Weeks
  • Neural network architecture: layers, neurons, weights, biases, activation functions (ReLU, Sigmoid, Tanh, Softmax)
  • Forward pass and backpropagation: the mathematical process of neural network learning
  • Loss functions: MSE for regression, binary cross-entropy, categorical cross-entropy for classification
  • Optimisers: SGD, Adam, RMSprop — what each does and when to prefer each
  • Regularisation: Dropout, Batch Normalisation, L2 weight decay — preventing overfitting in deep networks
  • Convolutional Neural Networks (CNNs): convolution layers, pooling, feature map visualisation, image classification
  • Transfer learning: fine-tuning pre-trained models (VGG16, ResNet50, EfficientNet) for new tasks with limited data
  • Recurrent Neural Networks (RNNs) and LSTMs: sequence data, time series forecasting with deep learning
  • TensorFlow 2.0 and Keras: building, training, and evaluating models; callbacks, model checkpointing
  • PyTorch: tensors, autograd, custom model building — the research-focused framework
  • Time series with deep learning: building an LSTM model for stock price or sales forecasting
📁 Module Project Project: Build an image classification model using transfer learning (fine-tuned ResNet) and a time-series LSTM model for monthly sales forecasting — both deployed as simple APIs.
8

Natural Language Processing (NLP)

2 Weeks
  • Text preprocessing: tokenisation, stopword removal, stemming, lemmatisation, regular expressions
  • Bag of Words and TF-IDF: converting text to numerical features for ML models
  • Word embeddings: Word2Vec, GloVe — learning semantic relationships between words
  • Sentiment analysis: document-level, sentence-level, aspect-based sentiment for business intelligence
  • Named entity recognition (NER): extracting people, organisations, locations, dates from text
  • Text classification: categorising customer feedback, support tickets, email routing
  • Topic modelling: Latent Dirichlet Allocation (LDA) for discovering themes in large document collections
  • BERT and transformer models: the revolution in NLP and how fine-tuning BERT works for specific tasks
  • Hugging Face ecosystem: pre-trained models, the model hub, inference API
  • Generative AI and LLMs: GPT model family, prompt engineering, building RAG (Retrieval Augmented Generation) pipelines for business applications
  • NLP applications in business: customer review analysis, email classification, document summarisation
📁 Module Project Project: Build a complete customer feedback analysis system — processing 5,000 product reviews, extracting sentiment by product aspect, identifying recurring themes, and presenting business recommendations in an executive report.
9

Business Strategy & Data-Driven Decision Making

2 Weeks
  • Business strategy frameworks: Porter's Five Forces, SWOT, competitive moats — using data to support strategic analysis
  • Metrics that matter: defining KPIs for different business functions — marketing (CAC, LTV, ROAS), product (DAU, retention, NPS), sales (pipeline velocity, conversion rates), operations (unit economics, throughput)
  • Data-driven decision making: when to use data, when to trust intuition, and how to avoid common cognitive biases
  • Product analytics: funnel analysis, cohort analysis, A/B testing, feature flag analysis
  • Growth analytics: acquisition channels, retention curves, churn analysis, and the maths of sustainable growth
  • Unit economics: understanding and improving CAC/LTV ratio, payback period, contribution margin
  • Market sizing and competitive intelligence: using data for TAM/SAM/SOM analysis
  • Business case writing: structuring a recommendation that data supports — the pyramid principle
  • Presenting to the C-suite: structuring data presentations for executive audiences who want conclusions, not methodology
  • Translating model outputs to business decisions: how to frame a machine learning result as a business recommendation
📁 Module Project Project: Build a complete data strategy recommendation for a fictional Pune-based startup — analysing their current metrics, identifying the three most important problems to solve with data, and recommending specific analytics initiatives with expected ROI.
10

Data Engineering Fundamentals & Cloud Analytics

1.5 Weeks
  • Data pipeline fundamentals: what ETL (Extract, Transform, Load) means and how modern data pipelines work
  • Introduction to cloud platforms: AWS, Azure, GCP — which services matter for data science professionals
  • AWS for data science: S3 for storage, SageMaker for model training, Athena for SQL on S3, Redshift for data warehousing
  • Google Cloud for data science: BigQuery (the world's most popular cloud data warehouse), Colab, Vertex AI
  • Data lakehouse concept: why Databricks and Snowflake are changing how companies store and process data
  • Apache Spark fundamentals: why it exists (processing data that does not fit in memory), RDDs vs DataFrames, PySpark basics
  • Real-time vs batch processing: when to use streaming (Kafka, Kinesis) vs batch pipelines
  • Data quality and governance: testing pipelines, data lineage, documentation, and access control
  • MLOps basics: model versioning, feature stores, model monitoring, and the production ML lifecycle
  • Docker and containerisation: packaging a data science application for deployment
📁 Module Project Project: Deploy a machine learning model as a REST API on AWS — from model training in SageMaker through containerisation with Docker to live API endpoint accessible from a web application.
11

Capstone Project — End-to-End Data Science Business Case

3 Weeks
  • Problem framing: defining a real business problem, success metrics, and the analytics approach
  • Data collection and preparation: sourcing real data, cleaning, feature engineering
  • Exploratory analysis: full EDA with visualisations and business insight documentation
  • Model development: selecting algorithms, training, evaluating, and comparing approaches
  • Business interpretation: translating model outputs into business recommendations
  • Dashboard development: building a Tableau or Power BI dashboard presenting the analysis
  • Written report: 15-20 page business analytics report covering methodology, findings, and recommendations
  • Presentation: 20-minute executive presentation with Q&A
  • Project topics (choose one): customer lifetime value prediction for an e-commerce company, employee attrition prediction and prevention strategy, demand forecasting for retail inventory optimisation, credit default prediction for a lending company
📁 Module Project Project: Complete, assessed capstone project with written report, dashboard, and oral presentation — the centrepiece of the student's data science portfolio.
12

MBA Business Track — Leadership, Communication & Career Strategy

1.5 Weeks
  • Data team leadership: managing data scientists, setting technical standards, hiring and onboarding
  • Agile for data teams: sprints, standups, backlog management, and delivering data projects iteratively
  • Stakeholder management: navigating competing priorities, managing expectations, and communicating delays professionally
  • Data ethics and governance: bias, fairness, privacy (GDPR, PDPB India), and responsible AI deployment
  • Interview preparation for senior data roles: Data Science Manager, Analytics Lead, Head of Data interviews
  • Resume and LinkedIn for senior data science and management roles: what to include, how to quantify impact
  • Networking in India's data community: conferences (ODSC India, Analytics Vidhya), communities, and professional visibility
  • Salary negotiation for data roles: how to negotiate Rs.20L+ offers and what leverage you actually have
  • Career roadmap: CDS.ai certification, Databricks certifications, AWS ML Specialty — what matters and what does not
  • Building a public portfolio: GitHub, Kaggle, personal blog — how to create professional visibility that attracts recruiters
📁 Module Project Final Deliverable: Complete professional profile package — updated resume, LinkedIn profile, GitHub portfolio with capstone project, and a 5-year career roadmap with specific milestones.
Tools & Technologies

The Full Stack You Will Master

Every tool covered is used by data professionals at companies hiring today. No deprecated technologies, no classroom-only tools — everything here appears in real data science job descriptions.

🐍

Python

Primary language for all data science work
NumPy / Pandas Scikit-learn TensorFlow / PyTorch Matplotlib / Seaborn
🗄️

SQL & Databases

Data querying and management
MySQL PostgreSQL BigQuery Window functions & CTEs
📊

Tableau & Power BI

Business intelligence and dashboards
Tableau Desktop Power BI Desktop DAX LOD Expressions
🤗

NLP & Deep Learning

Advanced AI and language models
Hugging Face BERT / GPT LangChain TensorFlow 2.0
☁️

Cloud & MLOps

Production data science infrastructure
AWS SageMaker Google BigQuery Docker Vertex AI
📈

Statistics & Analytics

Analytical foundations
Hypothesis Testing A/B Testing Regression Clustering & PCA
What You Will Build

12+ Real Projects That Prove
Your Data Science Capability

Every project is built with real data, tackles a real business problem, and ends with a business recommendation — not just a model metric. Your portfolio will look like the work of a professional, because it is.

📊

EDA and Business Insights Report — Complete exploratory analysis of a real Indian business dataset with 5 quantified, actionable insights backed by visualisations

🧪

A/B Test Analysis — Designing, analysing, and presenting results of a real-world website conversion experiment with statistical significance determination

🗄️

SQL Analytics Library — 15 business questions answered with production-quality SQL including cohort analysis, product rankings, and customer LTV calculation

🤖

Customer Churn Prediction Model — 5-algorithm comparison with ROC-AUC evaluation, final model selection, and business recommendations for reducing churn

👥

Customer Segmentation Analysis — RFM-based and ML-based segmentation for a retail company with differentiated marketing strategy recommendations

📈

Executive Sales Dashboard — Dual-platform dashboard (Tableau + Power BI) covering regional performance, trends, and forward projections

🧠

Transfer Learning Image Classifier — Fine-tuned ResNet model for a real classification task, deployed as a REST API

⏰

LSTM Sales Forecasting Model — Time-series deep learning model predicting monthly sales with uncertainty bounds

💬

Customer Feedback NLP System — Sentiment analysis and topic extraction from 5,000 product reviews with business insight report

☁️

Cloud ML Deployment — Model trained in AWS SageMaker, containerised with Docker, deployed as a live REST API endpoint

🏆

Capstone Business Case — Full 15-20 page analytics report with executive dashboard, model, and oral presentation for a real business problem

📋

Professional Portfolio Package — GitHub portfolio, updated resume, LinkedIn profile, and 5-year career roadmap as the final deliverable

Career Outcomes

Roles Our Graduates Move Into

The MBA in Data Science combination qualifies graduates for roles that require both technical credibility and business communication — the highest-value positions in the data economy.

📊

Data Scientist

Building ML models, running experiments, and turning data into product and business insights at technology companies
₹8–20 LPA
📈

Analytics Manager

Leading a team of analysts, owning the analytics roadmap, and presenting insights to senior leadership
₹15–30 LPA
💡

Business Intelligence Lead

Owning dashboards, reports, and data infrastructure for a business unit — the bridge between data and operations
₹10–22 LPA
🎯

Data Product Manager

Defining the product roadmap for data-powered products — combining ML expertise with product management skills
₹14–28 LPA
🔬

ML Engineer

Building, deploying, and maintaining machine learning systems in production — the technical delivery of data science
₹12–28 LPA
🏦

Quant Analyst

Applying statistical modelling and ML to financial data at banks, AMCs, fintech companies, and trading firms
₹10–25 LPA
🌱

Chief Data Officer (CDO)

Senior data leadership role — data strategy, governance, team building, and executive representation of data function
₹40–80 LPA
🚀

Data Science Consultant

Independent or Big 4 consulting — solving data science problems across industries and companies as an external expert
₹12–35 LPA
Why Aapvex

How This Programme Compares

Feature Aapvex MBA in Data Science Online Bootcamp (Great Learning, etc.) Full-Time MBA
Business strategy + data science combined✓✗~
Deep learning and NLP modules✓~✗
Real project portfolio (12+)✓~✗
Cloud deployment (AWS/GCP)✓~✗
Live trainer (not pre-recorded videos)✓✗✓
100% placement support✓~~
Duration suitable for working professionals✓~✗
Fee accessible with EMI✓~✗
Student Stories

What Our Students Say

★★★★★

I had an MBA from a tier-2 college and was stuck as a reporting analyst because I could not write code. The Aapvex MBA in Data Science programme taught me Python, SQL, machine learning, and Tableau in 6 months while I kept my job by attending weekend batches. The business strategy modules were sharp and directly applicable — not textbook stuff. I moved from Rs.5.5 LPA to Rs.13 LPA as a Data Scientist at a Pune fintech company within 2 months of finishing.

VN
Varun N. Data Scientist, Fintech Company, Pune
★★★★★

As a commerce graduate with no tech background, I was nervous about whether I could survive the Python and machine learning modules. The Aapvex trainers are genuinely patient — they explain concepts from first principles without making you feel slow. By Month 3 I was building ML models from scratch. The capstone project was the hardest thing I ever did but it is also the thing that got me my job. The interviewer spent 20 minutes going through it and offered me the role the same day.

SB
Shweta B. Business Analytics Analyst, MNC, Hinjewadi, Pune
★★★★★

I was a senior developer at a Pune IT firm wanting to move into data science leadership — not just coding but managing data teams and working with business stakeholders. The MBA in Data Science programme was exactly right for that. The business modules on stakeholder communication and data strategy were things I genuinely had not thought about systematically before. Got promoted to Analytics Lead at my current company 4 months after completing the programme.

MK
Manish K. Analytics Lead, IT Services Company, Pune
FAQ

Frequently Asked Questions

Real questions from students who joined or enquired about the MBA in Data Science programme at Aapvex Technologies, Pune.

The MBA in Data Science programme starts from Rs.34,999. EMI is available — approximately Rs.6,000 per month over 6 months. Call 7796731656 for current batch pricing, scholarship opportunities, and group discounts for organisations sending multiple employees.

The Aapvex MBA in Data Science is an industry-focused professional programme, not a UGC-recognised university degree. It issues an Aapvex Technologies industry certificate that is valued by hiring companies for the skills it verifies — similar to how AWS certifications, CFA, or ACCA are valued for what they prove you can do, not because of regulatory recognition. If you need a degree-granting programme, this is not it — but if you need skills and placement, this programme is specifically designed to deliver those outcomes.

Yes — the programme starts from absolute Python fundamentals. Students from commerce, arts, humanities, and non-IT engineering branches all join and complete successfully. The early modules move deliberately through Python basics before accelerating into data science applications. The one thing that helps most is comfort with basic mathematics — percentages, ratios, and simple algebra.

Weekend batches meet Saturday and Sunday, 9 AM to 1 PM (4 hours each day) — 8 hours of instruction per week. With 6 months of consistent weekend attendance, students complete the full curriculum including all 12 module projects. Most working professionals can manage this schedule without it affecting their weekday work performance. Recorded sessions are provided for any class missed due to travel or work commitments.

Graduates who complete all projects and the capstone are competitive for analyst and junior data scientist roles immediately. For senior analytics manager or data science lead roles, most employers expect 1-2 years of post-programme work experience. The MBA in Data Science programme positions you for accelerated growth — the combination of business and technical skills means you move faster through the career ladder than either pure technical or pure MBA candidates.

A standalone Python/ML course gives you technical skills without business context — you will know how to build a model but struggle to explain why it matters to a non-technical stakeholder, define the right business problem to solve, or understand how to align a data project with company strategy. This programme builds both tracks together — every technical module connects explicitly to business application. The business strategy, stakeholder communication, and data leadership modules are what allow graduates to qualify for managerial and leadership roles that pure technical data scientists cannot.

Yes — the NLP module includes a section on transformer models, BERT fine-tuning, prompt engineering, and building RAG (Retrieval Augmented Generation) pipelines using LangChain. Generative AI is no longer a specialisation — it is a mainstream data science skill that every professional needs to understand and apply.

Placement support includes: complete resume building for data science and analytics management roles, LinkedIn profile optimisation reviewed by our team, 2-3 mock interviews covering both technical (SQL, Python, ML concepts) and business/behavioural questions, data science portfolio review with specific improvement feedback, and direct referrals to our 200+ company hiring network including IT services, product companies, BFSI, and consulting firms in Pune, Bangalore, and Mumbai.

Yes — the complete 6-month programme is available as live interactive Zoom sessions with identical curriculum, same trainer, and same placement support as the Pune classroom. Students join from Bangalore, Mumbai, Hyderabad, Delhi NCR, Chennai, and Nagpur. The capstone project and mock interviews are conducted online identically to the classroom experience.

Recorded backups of all live sessions are provided — if you miss a class or need to revisit a module, recordings are available for the duration of the programme. The trainer holds doubt-clearing sessions every Saturday morning specifically to help students who are working through difficult concepts. Students who are genuinely struggling are offered one-on-one revision sessions — we do not leave students behind without support.

📍 Training Near You

Find a Batch in Your Area

We conduct classroom and online training across Pune and major Indian cities. Click your area to see batch schedules, fees, and availability.

🏙️ Pune — Training Areas

🇮🇳 Other Cities

All locations offer live online training. Call 7796731656 for batch availability.

The Skill Set That Gets You to Rs.20L+
Is Both Technical and Managerial

Data Science alone gets you in the door. MBA-level business skills get you to the top. This programme gives you both — in 6 months, while working.

💬 WhatsApp 7796731656 📞 Call Now 🎓 Enrol Online
🔥 Next Batch Starting Soon — Limited Seats  ·  Fees from ₹34,999  ·  EMI Available  ·  100% Placement Support
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