SP Jain's Professional Program in Big Data & Visual Analytics is a highly stimulating and application-based program that combines technical and business knowledge to help you provide solutions to the challenges that Information Sciences and Big Data pose on corporations in the 21st century.


8-month program in Mumbai
Program includes a 2-month internship
Specialise in Banking & Financial Analytics or Marketing Analytics
Ideal for graduates with 0-3 years of work experience
From Data Mining, Machine Learning and Cloud Computing to Linux, Algorithmic Training and Internet of Things – over the course of 8 months, SP Jain’s BDVA program takes you deep into every aspect of Big Data. And, not simply from a theoretical perspective, but also through practical, real-world projects and industry internships that help students, especially those with little or no work experience, supplement their academic knowledge with real-life industry experience, make meaningful contributions to companies, build strong professional contacts and explore full-time career opportunities.
Big Data is not just another fad. In an increasingly digital world, Big Data plays a very important role in driving decisions, innovation and productivity in large multinationals, non-profits and governments. It is being used to analyse social media trends to formulate election strategy, evaluate meteorological data to predict the weather or even to analyse retail data to drive more sales.
S P Jain’s one-of-a-kind professional program enables students to develop a thorough understanding of Data Analysis, rapidly adapt to the changing role of Information Sciences and bring in creative solutions to tackle the challenges that arise in modern business.
There is real value that can be extracted from analysing data and on graduating from this program, you will be able to help organisations:
  • Develop better strategies, improve operational efficiencies, reduce costs and terminate risks
  • Make information transparent and usable at much higher frequencies
  • Enable the collection of more accurate and detailed performance information on products, services and therefore, expose variability and boost performance
  • Allow finer segmentation of customers and enable customisation of products and services
  • Help in the development of the next generation of products and services

We offer an intellectually stimulating classroom environment steered by front-runners in business and academia who nurture participants to initiate, develop and launch ideas. These include industry experts, business leaders, visionary thinkers, innovators, strategists, researchers, entrepreneurs, pioneers and executives who have been the key enablers of innovation at leading multinational organisations.

In the last seven years of our short but decorated fourteen-year-old history, SP Jain and its programs have been ranked by reputed international publications. Our 1-year Global MBA program has been ranked by three of the world’s top four MBA rankings – Forbes, Financial Times and The Economist. In each of these rankings, we are amongst the youngest schools featured – an accomplishment we attribute to our unique and innovative model of business education.

The 8-month professional program in Big Data & Visual Analytics includes the following components: 
  • Quantitative Methods
  • Statistics
  • Databases
  • Data Visualisation
  • Data Structures & Algorithms Using Python
  • Machine Intelligence & Deep Learning
  • Natural Language Processing
  • Recommender Systems
  • Data Mining
  • Functional Programming in Scala
  • Data Engineering (Hadoop & Apache Spark)
  • Cloud Computing & DevOps
  • Specialisations:
    Option 1: Banking & Financial Analytics
    Option 2: Marketing Analytics
  • Curricular Practical Training (Project/Internship)

The program is truly your gateway to exploring, analysing and unravelling the complex and unstructured data-driven world. Given the need for specialist knowledge, we provide a range of specialisations in cutting-edge topics like Banking & Financial Analytics and Marketing Analytics.
On completion of the program, students would have learnt to apply quantitative modelling and data analysis techniques to solve real-world business problems, successfully present results using data visualisation techniques, demonstrate knowledge of statistical data analysis techniques utilised in business decision-making, apply principles of data science to the analysis of business problems, use data mining software to solve real-world problems and employ cutting-edge tools and technologies to analyse Big Data.
Quantitative Methods

Linear Algebra; Matrices, Calculus: Differentiation, Integration (single variate), Optimisation, Discrete Mathematics, Simplex Methods, Assignment & Transportation, Game Theory, Graph Theory, Brownian Motion, Lebesgue Sample Space, Measure Theory, Martingale


R-studio Fundamentals, Programming in R, Data Handling, Transformations, Descriptive Statistics, Probability Theory; Central Tendencies, Distributions, Regression, Stochastic Processes, Hypothesis Testing, Time Series


ER Modelling, SQL, Indexes and Constraints, Relational Databases, MySQL Workbench, NoSQL Theory and Clustered Databases, Document Databases such as MongoDB, Graph Databases, Google Firebase / AWS Redshift

Data Visualisation

Visual Cognition, Perception, Analytical Design, Dashboard and Storytelling, Tableau, Work Sheet & Dashboard Actions, Guided Analytics, JavaScript, React-REDUX

Data Structures & Algorithms Using Python

Python Programming; Data Representation such as JSON and XML, Python Scripting, Objects & Data Types, Functions, Strings, Boolean Logic, Data Libraries; SciPy, NumPy, Pandas, Matplotlib
Algorithmics: Searching & Sorting, Divide & Conquer, Dynamic Programming, Augmented Search Structure, Amortised Analysis, Monte Carlo Simulation

Machine Intelligence & Deep Learning

Neural Networks, Perceptron, Confusion Matrix, Kernel Trick, Supervised Learning: Linear and Logistic Regression, Decision Trees and Random Forests, Support Vector Machines, Unsupervised Learning: Hierarchical Clustering, K-means Gaussian Mixture Model, Dimension Reduction, Linear Discriminant Analysis, Ensemble Methods: Bias-variance Decomposition, Over-fitting, Random Forest, AdaBoost Algorithm, Gradient Boosting, Reinforcement Learning. Packages – Keras / Theano / TensorFlow

Natural Language Processing

Naïve Bayes Theorem, Markov Model, Support Vectors, Probabilistic Language Modelling, N-Grams, NLTK (Python Libraries), POS Tagging, Parsing, Semantics, Information Retrieval and Extraction, Sentiment Analysis

Recommender Systems

Collaborative Filtering, Content-based Filtering, Hybrid Models

Data Mining

Artificial Neural Nets, Data Exploration & Visualisation, Classification, Association Analysis, Clustering, Anomaly Detection

Functional Programming in Scala

Scala Basics, Types, Classes, Special Methods, Currying, Types, Implicits, Anonymous Classes, Var Args, Partial Functions, Recursion, Collections, For Loops

Data Engineering (Hadoop & Apache Spark)

Design, Hadoop: Introduction to Big Data, Hadoop Setup, Map Reduce, Yarn Architecture, Hive, Pig, Sqoop, Flume - Data Ingestion, SQL, NoSQL, H-base, Kafka, Zookeeper, Oozie
Apache Spark: Stream Processing, Spark Streaming Library with PySpark, Using SQL & Dataframe

Cloud Computing & DevOps

AWS EC-2, S3, Virtualisation, CI/CD with Jenkins, Google Kubernetes


Option 1: Banking & Financial Analytics

Stochastic Calculus, Value at Risk. CAPM, Black-Scholes Theorem, Volatility Estimation: Exotic Options. Stochastic and Local Volatility Models. RSI, Equivalent Martingale Measure Approach. Interest Rate Derivatives, Risk Analysis, Credit Research

Option 2: Marketing Analytics

CRM with Salesforce, Customer Segmentation, HubSpot Digital Marketing Models, ML-based customer segmentation and prediction for cross-sell / up-sell, Review of Algorithms, Digital Banking and Channel Effectiveness, Digital Media, Game Theory, Competitive Marketing Strategy, Mining social data to segment, target, create campaigns, execute, visualise, predict customer churns, Analytics

The program equips students to fill the need for sophisticated expertise in varied domains such as IT, Consulting, BFSI, Telecom and Media, and in specialisations such as data mining, data modelling, data architecture, extraction, transformation, loading development and business intelligence development. The acquired techniques are essentially required for roles such as Data Scientists, Analysts, Developers and Consultants.


Highest Salary: INR 33 lakhs

Average Salary: INR 8.25 lakhs

Top Recruiters: Colgate, Deloitte, Deutsche Bank, eClerx, Ernst & Young, Fractal Analytics, Franklin Templeton, HDFC Bank, Jubilant FoodWorks (Domino’s Pizza), Maersk, Nielsen, RBL Bank, S&P Global, Tarnea Technology Solutions (Microsoft), Tech Mahindra, YES Bank and many more


Tarnea Technology Solutions (Microsoft)

Ernst & Young

Ernst & Young




Uma Mounika K
Fractal Analytics


Students of our Big Data & Visual Analytics program are guided and motivated by a world-class body of faculty comprising highly-skilled industry experts and leaders like:

Anupam Mondal
Phd Scholar (NTU Singapore); M.Tech; B.Tech (Jadavpur University)
Area of Expertise: Text Mining 
Debashis Guha
Ph.D (Columbia University); B.E (IIT, Kharagpur)
Area of Expertise: Advanced Machine Learning 
Dipankar Das
Ph.D; B.E
Area of Expertise: NLP 
Manish Khati
B.Tech in Computer Science (APJAKTU)
Area of Expertise: Python
Narasimha Karumanchi
MS (IIT Bombay)
Areas of Expertise: Data Structure & Algorithm (Python)  
Nikhil Gujar
M.L (UniSA); MBA (NITIE); B.Tech (IIT Bombay)
Areas of Expertise: Data Structure, Algorithm 
Prakash Jhawar
Masters in Mathematics and Computing, IIT Guwahati
Areas of Expertise: Mathematics and statistics for Data Science 
R Vivekanand
MBA (Monash University- Melbourne, VIC)
Area of Expertise: Tableau   
Raghavshyam (Shaam) Ramamurthy
MBA (Syracuse University)
Area of Expertise: Visualization
Satish Patil
PhD (University of Minnesota); B.Tech, EEP - Business Analytics and Intelligence (IIM, Bangalore)
Areas of Expertise: Machine Learning, Statistics 
Srivatsa Srinath
MBA (IIM, Bangalore), MS (Penn State University), B.Tech (IIT, Madras)
Area of Expertise: Machine Learning 
Suneel Sharma
M.Sc.(BITS, Pilani), PhD (Rajasthan University), CAP (Lancaster University)
Areas of Expertise: Visual Analytics, Computational Finance, Cloud Analytics 
Sunil D Lakdawala
PhD (Yale University); M.Sc.(IIT Bombay)
Areas of Expertise: Data Warehousing, Time Series 
Yogesh Parte
PhD (Applied Mathematics) France; M.Sc (IISc) Bangalore; B.E (Pune University)
Areas of Expertise: Mathematics, Machine Learning, Time Series 
To apply for the Big Data & Visual Analytics, you must* have:
  • An undergraduate/postgraduate degree in a discipline with a strong quantitative component like: Engineering (any discipline), Mathematics, Physics, Statistics, Economics or Commerce
  • Ideal for graduates with 0-3 years of work experience
*Minimum eligibility criteria may be waived for exceptionally qualified candidates.
Click here to submit your application.
Final selection of candidates will be on the basis of their performance in:
  • A one-hour aptitude test with numerical, quantitative reasoning, mathematics and statistical components
  • A personal interview for candidates who have performed well in the aptitude test
This is a very intense program and will require full concentration on all topics covered by specialists in a short time thus, each candidate will be assessed comprehensively to determine his/her eligibility for the program. Candidates will be evaluated based on their in-depth scientific knowledge, analytical skills and computational knowledge and expertise.
(Aptitude Test + Evaluation Fee: INR 3,000)
Total Tuition Fees: INR 500,000 (plus GST)



Admissions are open to our June 2019 intake.


Learn the key information technologies that are used to analyse Big Data, uncover the answer to the toughest of business challenges, discover patterns and pursue breakthrough ideas in today’s ever-changing businesses scenarios. Start at SP Jain.