Comprehensive course analysis
Who should attend
Participants should have a Bachelors degree in engineering/science/commerce/arts, with mathematics as one of the core subjects.
At least 3 years of work experience is preferred. Given that programming is critical to developing a working understanding of data science, we prefer participants with strong coding skills, who are willing to embrace a variety of languages and technology platforms. Participants must dedicate serious time to assignments that will be graded. Periodic assessments shall be made via online exams.
This certificate series in data science exposes the participants to a large set of frameworks and technologies, and prepare them for data-intensive roles in retail, healthcare, marketing, supply chain management, finance, insurance and other industry sectors. The modules in the MDPS series are suitable for managers who wish to develop a programming-oriented appreciation for their individual domains. Projects drawn from their own workplace settings will help them with the assimilation of the topics.
About the course
The triad of machine learning, big data and artificial intelligence form the pillars of the emerging discipline of data science. Managers who lead data science teams are required to possess a sound understanding of the underlying technologies. More importantly, they must also be able to communicate the strategic benefits of their efforts to stakeholders. The field of information systems provides the appropriate frameworks to build the business case for data science initiatives, and to analyse the shifts brought about by its application.
Organizations that fail to recognize the disruptive power of data science will be rapidly left behind. While there is a threat of job loss in mechanistic settings like BPO operations, there is also a tremendous potential to innovate in environments that are conducive to analytics. For example, insurance firms have begun to offer discounts to policy holders who are willing to share data recorded by their wearable devices. The collection and processing of this unstructured data is fairly complex, and yields tangible returns to business.
The Modular Programme in Data Science (MPDS) from IIM Bangalore will empower managers and other professionals to draw from the frameworks of data science and information systems, and guide their organizations along a forward-looking trajectory. The MPDS series is designed for flexibility. Factoring in fairly tight workplace requirements, a cyclical scheduling of the modules around the year allows participants to take a staged approach towards developing knowledge and skills.
The course has 3 Modules along with a Foundation Module (optional) and Project.
Module Zero: Foundations (7 weeks)
A strong knowledge of statistics is considered foundational to data science. The participants can skip this module and directly enrol for the Online Entrance exam. To kick off the MPDS program, we invite the participants to IIMB’s digital classroom for 3 days , followed by two Massive Open Online Courses (MOOC) for 3 weeks and the last 3 days for case based learning.
- Python tutorial
- Descriptive statistics – variables and their visualisation, probability
- Random variables, binomial, Poisson and normal distributions
- Sampling, confidence intervals and parameter estimation
- Statistics for Business 1 - spreadsheet-driven elaboration of descriptive statistics and probability; decision-making with Bayesian logic
- Statistics for Business 2 - R programming with a business use case, random variables, binomial, Poisson and normal distributions
- Inferential statistics – hypothesis testing framework, ANOVA, chi-squared tests
- Forecasting with time series
- Linear algebra and geometry
The final stage is an online assessment, covering the two MOOC courses and other topics of Module Zero. Depending on the outcome, suggestions will be made regarding areas of improvement. Participants who do not clear this exam will not be able to proceed to the other modules.
Participants who have prior experience in statistics and python can skip Module 0 (Foundation) and directly appear for Online entrance exam. Clearing the entrance exam will directly qualify them for the Data Science core modules.
Module One: Machine Learning (9 weeks)
- The machine learning landscape
- Machine learning in practice
- Supervised models: Regression and classification
- Decision trees and random forests
- Support vector machines
- Dimension reduction and PCA
- Unsupervised models: clustering
- Machine learning at scale
- Cases: Mission Hospital (IIMB), Scholastic Travel (Darden), Lending Club (HBS),
- Industry Talks: Entrepreneurs who have built ML firms
Module Two: Big Data (9 weeks)
- Tectonic movements in the landscape of data
- Volume: Structured data, Big Query, cloud computing
- The MapReduce construct, Hadoop
- Spark with Scala and Python – business applications
- Variety: Network analysis
- Velocity: Streaming applications
- Operational excellence, supply chain management
- The IoT movement, Industry 4.0
- Veracity: The case of Cambridge Analytica
- Cases: FinTech (NACRA), Cogent Labs (HBS), Customer Analytics at BigBasket (IIMB), CDK (Kellogg), 1920 Evil Returns (IIMB), Cognitive Analytics (Kellogg)
- Industry Talks: Speakers from Amazon, Google & Microsoft, Target, etc.
- Industry Talks: Big data in Government, Healthcare, Marketing
Module Three: AI & Deep Learning (9 weeks)
- The AI Landscape
- Biological origins
- Historical perspective
- Optimisation and search
- Single and multi-layer networks
- The TensorFlow platform
- CNNs and RNNs
- Natural language processing
- Generative Adversarial Networks
- Reinforcement learning
- Genetic algorithms, ant colony optimisation
- Artificial life systems
- The Future of AI
- Cases: Preferred Networks (Insead), Edge Networks (IIMB)
- Industry Talk: Speakers from AIndra, Google and Microsoft
Project (3 months)
Upon successful completion of all 3 modules, participant can work on a live project.
- Develop a business perspective of the dynamic discipline of data science
- Gain a foundational understanding of machine learning, big data and AI
- Solve computational problems using a personal laptop as well as a cluster
- Construct a business proposal for a data science initiative
- Identify ways in which organizations can inculcate data-driven decisions
After the completed applications are received by the EEP Office, a shortlist will be drawn up. Subsequently, the program office will reach out to the candidates, to ascertain their readiness as well as fit for the program. Any doubts may be clarified during this call.
Make no mistake, this is an intensive program, with quality gates at each stage.
Completing Module 0 is a prerequisite to proceed to any of the other modules. It is designed to strengthen the participant’s foundations in statistics, linear algebra and programming. Participants will first be brought up to speed on Python programming with the help of extensive tutorials. Next, IIMB’s MOOC division will help the participant cover the required sections of its courses dedicated to descriptive statistics (SFB-1 & SFB-2). Live sessions on the same material shall be conducted at a rapid pace. We end the module with a few more sessions on inferential statistics and linear algebra.
An exam at the end of Module 0 will assess the candidate’s suitability for the remaining modules as well as the MPDS certification, and suggest any areas for improvement.
Modules 1-2-3 can be completed in any order, as and when they are offered. Making optimal use of weekends, these modules will follow a (2 + 4) session format on Friday and Saturday. Again, each module will have an online assessment at the end.
Dr. Bandi is a Professor of Decision Sciences and Information Systems at IIMB. He started teaching during his doctoral programme at Georgia State University, Atlanta, USA. Prior to joining IIMB, he was an Assistant Professor at Florida Gulf Coast University, Fort Myers, USA. Research Areas: ...
Quantitative Methods and Information Systems area, IIMB Prof. Das has been a faculty with IIMB since 1999. He has held visiting faculty positions at various institutes/universities of international repute, including ESSEC Business School, Indian Statistical Institute Calcutta, University of Nebr...
Before joining IIMB, Professor Dé was an Associate Professor at Rider University in New Jersey, USA. Professor Dé’s research interests are in ICT for Development, Open Source and e-Government Systems. He has published two books and over 50 articles in international journals, refereed conference p...
U Dinesh Kumar’s research interest includes Business Analytics and Big Data, Artificial Intelligence, Machine Learning, Deep Learning Algorithms, Stochastic models (Reinforcement Learning Algorithms), Reliability, Optimization, Six Sigma and Performance Based Logistics. He has published several r...
Education PhD, The University of Texas at Austin Master of International Management, Thunderbird School of Global Management Post Graduate Diploma in Management, The Indian Institute of Management, Ahmedabad Bachelor of Commerce (B.Com.), R.K.M. Vivekananda College, University of Madras Biogra...
RESEARCH AREAS OF SPECIALIZATION: Optimization, Decision Sciences, Data Analytics, Operations Research JOURNAL PUBLICATIONS Zhang, D.+, Yu, C.+, Desai, J., Lau, H.Y.K., and Srivathsan, S+. (2016), “A time-space network flow approach to dynamic repositioning in bicycle sharing systems”, Transpo...
Ananth Krishnamurthy’s research focuses on stochastic modeling and optimization techniques for the design and analysis of manufacturing systems and supply chains. Topics of interest include design and analysis of supply chains, warehousing and logistics, biomanufacturing, energy supply chains, ma...
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