Editor’s Note—The following article includes course outlines and information about the different Minor Specialisations offered by MIT to the Sixth Semester students, along with reviews from the students who them in the previous year. The information provided here will be updated as and when more of it becomes available.
Minor Specialisations for VI Semester
COMPUTATIONAL MATHEMATICS
If you’re interested in statistics, maths, and logical thinking, this specialisation is definitely for you. Math concepts are taught from scratch so even if you haven’t paid much attention to your fourth-semester Engineering Mathematics lectures, you can scrape through! The subjects are intense yet fascinating and are definitely tougher than Coursera specialisations. Nonetheless, they help with data analytics, computing and AI-related concepts.
The course is ideally taught by two professors—Dr. Indira KP and Dr Sumathi K. “It’s a joy to sit in Indira ma’am’s classes”, said a student who had taken this course. Sumathi ma’am’s notes are more complex to understand. Much of the course involves proving theorems, applying logic and applying one concept to the other. Additionally, the question papers are difficult as none of the questions bear obvious answers. Students opting for this minor are highly advised to ask the professors for the course plan and proper notes from the beginning of the semester.
Course: Applied Statistics and Time Series Analysis (MAT 4051)
Course Summary from Academic Handbook: Stochastic and deterministic dynamic mathematical models—forecasting and control, transfer function models, models for discrete control systems. Basic ideas in the model building—linear and multiple linear regression. Basic concepts in stochastic processes and Markov chains, mean square distance, mean square error prediction, prediction of covariance stationary process, ergodic theory and stationary process, applications of ergodic theory, spectral analysis of covariance stationary processes, Gaussian systems, stationary point processes, level crossing problems. ARIMA models, Autoregressive models, moving average models, duality, model properties, parameter estimates, and forecasts. Volatility models: ARCH and GARCH modelling, testing strategy for heteroscedastic models, volatility forecasts, Black Scholes model.
Course: Computational Linear Algebra (MAT 4052)
Course Summary from Academic Handbook: Matrix Analysis: Basic Ideas from Linear algebra, vector norms, matrix norms, orthogonality and SVD, Projections and CS decomposition, and the sensitivity of square linear systems. General Linear Systems: Triangular systems, The LU factorization, roundoff analysis of Gaussian elimination, Pivoting, Improving and estimating accuracy. Orthogonalization and least squares: Householder and Givens matrices, The QR factorization, the full rank LS problem, Other orthogonal factorizations, the rank deficient LS problem, Weighing and iterative improvement, square and underdetermined systems. The symmetric Eigenvalue problem: Eigenvalues properties and decompositions, Power iterations, the symmetric QR algorithm, Jacobi methods, Tridiagonal Methods, Computing the SVD, and some generalized eigenvalue problems.
BUSINESS MANAGEMENT
Offered by the Department of Humanities for sixth-semester students, this minor specialisation includes two subjects—Financial Management and Marketing Management.
Financial Management is a tougher subject that requires memorization of a lot of formulae and an understanding of new concepts. Human Resource Management primarily teaches you about managing personnel. The subject can be described as a little boring due to the enormous amount of theory in the course.
If you’re a finance freak and are inclined towards any of the business management fields (finance, HR, operations, marketing), this minor is for you. The teachers put their best into making classes more interactive and fun, taking examples from real-life applications. A flair for financial mathematics and healthy communication with your professors is what you need to course through this minor!
Course: Marketing Management (HUM 4053)
Course Summary from Academic Handbook: Introduction to marketing management: Understanding Marketing Management; Marketing Opportunities: Nature of market demand; Market environment; Competitor analysis. Consumer markets and business markets. Developing Market strategy: Segmentation, Targeting, Positioning, Developing a product strategy, and Service design. Promotion mix. Distribution channels—roles and functions, marketing research. Global marketing; Ethics in marketing. Technology and
Marketing.
Course: Human Resource Management (HUM 4052)
Course Summary from Academic Handbook: Introduction, Scope of HRM, Objectives of HRM, Functions, Activities, Roles, HRD organization and responsibilities. Evolution of HRM, Influence of various factors on HRM. Human resource planning: Introduction, Strategic considerations, Nature and scope, Human Resources Inventory, Job analysis, Job design, Job description, Job specification and Job evaluation. Employee Recruitment & Selection: Policy, Process, Tests, modern methods, Interview, Provisional selection, Medical/Physical examinations, Placement, Induction programs and socialization. Training and development: Basic concepts, Employee training Process, Planning, Preparation of trainees, Implementation, Performance evaluation and Follow-up training. Competency Mapping and Career development programmes. Performance appraisal and Merit rating, Promotion, transfers and separations, Wages and salaries administration, Discipline and grievances. Industrial and labour relations and Trade Unionism Overview: Collective bargaining and maintaining Industrial health.
BIG DATA
A minor specialisation taught by UC San Diego through Coursera, the subjects are easy to moderately difficult to learn. It’s easy to get engrossed in the subjects, they are engaging. If learning online by yourself baffles you, many online resources can help you get through difficult concepts too!
The teachers are well-versed in the subject and have a good grasp of the topic. If you’re interested in Data Analysis and Machine Learning, these courses will definitely help you!
Course: Big Data Modelling And Management Systems (CRA 4055)
Course Summary from Coursera: Introduction to Big Data Modelling and Management: Data Ingestion, Storage, Quality, Operations, Scalability and Security, Energy Data Management Challenges at ConEd, Gaming Industry Data Management, Flight Data Management at FlightStats; Big Data Modelling: Data Model: Structures, Operations, Constraints; Introduction to CSV Data, Semistructured Data Model, Array Data Model of an Image, Sensor Data, Vector Space model, Graph data Model, Lucene Search Engine’s Vector Data Model, Gephi, Data Model vs Data Format, Data Stream, Data Lakes, Streaming Sensor Data
Course: Big Data Integration and Processing (CRA 4056)
Course Summary from Coursera: Why is Big Data Processing Different; What is Data Retrieval; Querying two relations; subqueries; querying relational data with Postgres; querying JSON with MongoDB; aggregation functions; querying Aerospike; Querying documents in MongoDB; exploring Pandas DataFrames; Bid data processing pipelines; Aggregation operations in Big Data Pipelines; typical analytical operations in Big Data Pipelines; Integration and Processing Layer; Introduction to Apache Spark; Spark Core: Programming In Spark using RDDs in pipelines, transformations, actions, SQL, Streaming, MLLib, GraphX. Exploring SparkSQL and Spark DataFrames; Analyzing Sensor Data with Spark Streaming
DIGITAL MARKETING
This specialization in Digital Marketing offered by Coursera explores several aspects of the new digital marketing environment. The subjects are fairly easy to learn though there’s a lot of theory and mugging up for the exams. The teachers are nice and explain well. You don’t need any prerequisites to learn digital marketing, so if you’re interested in learning about it, go ahead and opt for it without hesitation!
Course: Marketing in a Digital World—Digital Marketing (CRA 4051)
Course Summary from Coursera: Overview of Marketing; Product; Offering Product Ideas; Customer Co-Creation; Sharing Economy; Promotion; Product Reviews; User-Generated Content; Case Study Introduction: GoPro; Doppelganger Brand Images; Placement; Online Shopping; New Retail; Self Manufacturing; Price Overview; Online Price Search; Pay What You Want; Freemium
Course: Digital Analytics for Marketing (CRA 4052)
Course Summary from Coursera: Introduction and history of Digital Analytics; online video, online search, display media, social media, Consumer Decision Journey; digital data infrastructure; brand measurement; consumer outcomes; customer value; attribution; Analytics and Dataviz Tools; Evaluating the Tool Landscape; Digital Marketing Maturity; The Issue of Privacy; Gies Online Programs.
DATA SCIENCE
With a rigorous course structure, this specialisation will provide you with great insight into Data Science and R programming. It has a lengthy syllabus, but people who go through the course emerge with excellent knowledge of the topics covered. It’s recommended if the coursework genuinely excites you—you cannot muddle through with this one! You’ll need in-depth knowledge before appearing for assessments here.
Course: Data Scientists Toolbox and R Programming (CRA 4059)
Course: Introduction to Data Science (CRA 4060)
FUNDAMENTALS OF COMPUTING
Based on Python, this specialisation will teach you about data structures and game development in the language. It is perceived as a doable course, and those who are interested in the language will surely enjoy it! A note—you may find it really easy if you prefer coding in Python.
Course: Introduction to Interactive Programming in Python (CRA 4063)
Course: Mathematical Problem Solving Using Python (CRA 4064)
MATERIAL SCIENCE
The specialisation involves two subjects physics and chemistry. The physics courses are pretty interesting for those especially interested in nanoscale phenomena, though the chemistry courses can be a little boring as they are much of a repeat of class 12 chemistry. The subjects aren’t difficult as such, but physics subjects have a chunk of mathematical derivations.
All the teachers are fine and are interested in what they’re teaching, though their teaching may not be so good. The courses are taught from a research perspective, so research papers and areas are often discussed in classes. This specialization is ideal for people interested in pursuing a research career (MS/PhD) in the areas of Nanotechnology/Physics/Chemistry/Electronics. The minor in itself is valuable for applications for higher studies and people looking to work in nearby areas.
Course: Physics of Low-Dimensional Materials (PHY 4051)
Course Summary from Academic Handbook: Thin films: Thick and Thin Film Materials, preparation by physical and chemical methods. Thickness measurement techniques. Theories of nucleation – Capillarity and atomistic theory, the effect of deposition parameters on nucleation and growth of thin films. Epitaxial growth. Reflection and Transmission at the interface between isotropic transparent media. Reflectance and Transmittance in thin films. Antireflection coatings. Electrical conduction in discontinuous metal films – Quantum mechanical tunnelling model. Conduction in continuous metal and semiconducting films. Thermoelectric power in metal films. thin film resistors, thermopiles. Quantum well devices.
Nanomaterials: Chemical Synthesis of Nanoparticles: Bottom-up approach. Functionalized nanoparticles in different mediums. Size control. Self-assembly. Nanoparticle arrays. Semiconductor nanoparticles- synthesis, characterization and applications of quantum dots. Magnetic nanoparticles- assembly and nanostructures. Manipulation of nanoscale biological assemblies. Carbon nanotubes and fullerene as nanoclusters. Nanostructured films. Physical Methods of Nanostructure Fabrication: Top-down approach. Nanopatterning Lithography- Optical, X-ray and Electron beam lithography. Ion-beam lithography.
Course: Chemistry of Carbon Compound (CHM 4052)
Course Summary from Academic Handbook: Introduction to Organic Compounds: Classification, Nomenclature; Alkanes: Homologous series, Preparation; Cycloalkanes: Ring size and strain, Applications; Alkenes: Markovnikov and anti-Markovnikov addition reactions, Reduction, applications; Alkynes: Acidity, preparation, Reduction of alkynes, applications; Alkyl halides: SN1, SN2, E1 and E2 reaction mechanisms; Alcohols: Classification, Acidity, organo-metallic reagents; Aromatic compounds: Electrophilic and nucleophilic substitution reactions; Mechanism of some named reactions; Carbonyl compounds: aldehydes and ketones, carboxylic acids and carboxylic acid derivatives; Heterocyclic compounds: Nomenclature, synthesis and reactivity of thiophene, pyrrole and furan; Carbon materials: Fullerenes, carbon thin films, nanotubes and carbon fibres; Carbon nanotubes: SWNT, MWNT, synthesis, properties and applications; Carbon nanomaterials applications.
ENTREPRENEURSHIP DEVELOPMENT
Course: Entrepreneurship (HUM 4062)
Course Summary from Academic Handbook: The Entrepreneurial Mind-Set, Corporate Entrepreneurship, Generating And Exploiting New Entries, Creativity And The Business Idea, Identifying And Analysing Opportunities(Domestic And International), Protecting The Idea And Other Legal Issues For The Entrepreneur, The Business Plan, The Marketing Plan, The Organizational Plan, The Financial Plan, Sources Of Capital, Informal Risk Capital, Venture Capital, Going Public, Strategies For Growth, Managing The Implications Of Growth, Accessing Resources For Growth From External Sources, Succession Planning, Strategies For Harvesting, Ending The Venture.
Course: Design Thinking (HUM 4060)
Course Summary from Academic Handbook: History of Design Thinking, Value of Design Thinking, Design Thinking as a solution, Design Thinking for Strategy, Revisiting the Business Model Canvas as a Common Language, Strategy Project Set-up, Target Industry, Guiding Principles, Process Overview, The Business Model Layer, The Competition Layer, Shaping the Strategy by Designing Business Model Prototypes, Designing Objectives, The Designing Process, Documenting the Current Detailed Business Model, Generating Innovative Ideas, Transforming Ideas into Business Model Prototypes, Design thinking is a toolbox, Systems Thinking. Systems thinking approach for design thinking: The laws of the fifth discipline: Introduction, Disciplines of the learning organization, The fifth discipline, Learning disabilities.
FINANCIAL TECHNOLOGY
Course: Fintech Services (HUM 4059)
Course Summary from Academic Handbook: Financial services and FinTech the changing environment and digital transformations, FinTech introduction, history and stages, FinTech initiatives ecosystem, and challenges. FinTech model and classifications, the Business model for FinTech. Innovations and FinTech, Types and examples of innovations in FinTech, product innovation, process innovation, business model innovation, Technology acceptance model. Critical success factors for FinTech. Responses of traditional players, the challenges, cooperation model and open innovation to traditional players. Regulations importance, the role of regulators. Insights into disruptive technologies and drivers of disruption. Deciphering crowdfunding, Addressing Information Asymmetries in Online Peer-to-Peer Lending. Digital technologies and their role in FinTech, payment gateway.
Course: Technology for Finance (HUM 4058)
Course Summary from Academic Handbook: Technologies in FinTech—The Fintech ecosystem, The Fintech business models. Regulatory aspects of FinTech In India. Blockchain, Big data, Artificial Intelligence, Applications Programming Interfaces. The mechanics of e-cash, Cryptography types. Distributed ledger technology – Bitcoin, DLT framework and Bitcoin, Mechanics of Bitcoin Network, Architecture of the Bitcoin Blockchain, Ethereum. Artificial Intelligence – Machine learning, supervised learning, semi-supervised learning, unsupervised learning, reinforcement learning, deep learning, natural language processing, and Applying AI to markets. Application of Blockchain, cryptocurrency and AI in Finance. Application of big data with FinTech.
Written by Vaishnavi Karkare for MTTN
Updated by Parva Mehrotra and Suhani Kabra for MTTN
Featured Image by Chirag Bansal for MTTN
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