A Guide to Picking your Minor Specialisation (VI Semester) for 2022

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 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, 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, 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, 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 Human Resource Management.

Financial Management is a tougher subject that requires memorization of a lot of formulae and understanding 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 are what you need to course through this minor!

Course: Financial Management (HUM 4051)

Course Summary from Academic Handbook: Introduction and objectives of financial management, Evolution of corporate finance, responsibilities. Types of accounts, Golden rules of accounting, Preparation of Journal, Ledger, Trial balance and final accounts. Sources of long-term finance, Characteristics of equity capital, Preference capital, Debenture capital & Term loans. Valuation of securities, Concepts, Bond valuation and related models, Bond value theorems, Yield to maturity. Equity valuation; Dividend capitalization approach, Leverage, Operating leverage, financial leverage, Total leverage, Indifference point analysis. Working capital management, Capital budgeting: appraisal criteria, pay-back period, Average rate of return, Net present value, Benefit-cost ratio and Internal rate of return. Risk analysis in capital budgeting, Cost of capital: introduction, cost of debt capital, Preference capital and Equity capital, Weighted average cost of capital, Determination of proportions, Cash management, Dividend decisions.

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’s 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, there are many online resources that can help you get through difficult concepts too!
The teachers are well-versed with 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.

 

MATERIAL SCIENCE
The specialisation involves two subjects in physics and chemistry each. 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 fibers; Carbon nanotubes: SWNT, MWNT, synthesis, properties and applications; Carbon nanomaterials applications.

 

Written by Vaishnavi Karkare for MTTN

Edited by Anika Shukla for MTTN

Featured Image by Chirag Bansal for MTTN

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