Uttrakhand UKPSC ICC ASO Syllabus 2024 and Exam Pattern

Uttrakhand UKPSC ICC ASO Syllabus 2024: The Uttrakhand Public Service Commission has released the official notification for the recruitment of Investigator cum Computer and Assistant Statistical Officers (ICC ASO). In this article, we provide the Complete syllabus and Exam Pattern for UKPSC ICC ASO Recruitment 2024.

किसी भी परीक्षा के लिए उसे परीक्षा को अच्छे से तैयार करने के लिए उसका सिलेबस और एग्जाम पैटर्न समझना बहुत ही आवश्यक होता है आज के इस पोस्ट में हम आपको उत्तराखंड लोक सेवा आयोग द्वारा आयोजित सांख्यिकी अधिकारी तथा इन्वेस्टिगेटर कम कंप्यूटर भर्ती 2024 के लिए एग्जाम पैटर्न और कंप्लीट विस्तृत सिलेबस आज की इस पोस्ट में हम आपको प्रोवाइड करवाएंगे. यदि आप हमारी वेबसाइट पर नए हैं तो हमारे टेलीग्राम चैनल को ज्वाइन कर ले क्योंकि हम कंपटीशन एग्जाम से संबंधित सभी जानकारी समय-समय पर हमारी वेबसाइट और टेलीग्राम चैनल पर देते हैं.

Overview

Important Dates

Selection Process for Uttrakhand UKPSC ICC ASO Syllabus 2024

• Stage-1: Written Exam
• Basic knowledge of Computer operation Test
• Typing Test
• Stage-2: Document Verification
• Stage-3: Medical Examination

Exam Pattern for UKPSC ICC ASO Recruitment 2024

• UKPSC ICC ASO भर्ती परीक्षा में 2 पेपर होंगे
• दोनों पेपर में प्रश्न वस्तुनिष्ठ प्रकार (MCQ) के होंगे.
• पहले प्रश्न पत्र में 100 प्रश्न पूछे जाएंगे जो कुल 100 अंक के होंगे तथा समय 2 घंटे का होगा.
• दूसरे प्रश्न पत्र में से पांच वैकल्पिक विषय होंगे जिनमें से एक विषय का पेपर देना है जिसमें कुल 200 क्वेश्चन होंगे और कुल अंक 200 होंगे और कुल समय 3 घंटे का होगा
• प्रत्येक प्रश्न एक अंक का होगा.
• Negative Marking: Yes, प्रत्येक गलत उत्तर के लिए 1/4 या 0.25 नकारात्मक अंकन किया जाएगा.

Also Check: APPLY online for UKPSC ICC ASO Recruitment 2024

Basic Knowledge of Computer Operation Practical
Examination Ability Test (Qualifying Nature)

Time Allowed: 1 Hour || Maximum Marks: 100

Microsoft Windows Operating system and Microsoft Office (Maximum Marks – 100;
Minimum Qualifying Marks to be obtained – 40; Time allowed: One Hour)

The paper shall be set from the given syllabus broadly taking one question from each i.e. – (1) Windows and internet (2) M.S. – Word (3) M.S. – Access (4) M.S. – Excel and (5) M.S. – Power Point.

Note -1- Each question shall have one action to be performed on the system each having 20 marks.

Note -2 – Printout of the output shall be taken and given for evaluation.

Note-3: उक्त परीक्षा अर्हकारी प्रकृति (Qualifying Nature) की होगी जिसमे न्यूनतम 40% अंक लेना अनिवार्य है. उक्त परीक्षा में प्राप्त अंकों को अंतिम चयन परिणाम में सम्मिलित नहीं किया जायेगा.

Detailed Syllabus for UKPSC ICC ASO Recruitment 2024

जैसा की एग्जाम पैटर्न से ज्ञात है उत्तराखंड सांख्यिकी अधिकारी तथा इन्वेस्टिगेटर कम कंप्यूटर परीक्षा 2024 में दो पेपर होंगे जिनका विस्तृत सिलेबस निम्न प्रकार है-

पेपर – 1 Syllabus

उत्तराखंड सांख्यिकी अधिकारी तथा इन्वेस्टिगेटर कम कंप्यूटर परीक्षा 2024 मैं पहले पेपर में दो विषय होंगे जिसमें पहले सामान्य हिंदी जिसमें कुल प्रश्न 50 तथा कुल अंक भी 50 होंगे दूसरा सांख्यिकी तकनीकी है जिसमें भी कुल 50 प्रश्न और कुल 50 अंक होंगे.

सामान्य हिंदी

कुल प्रश्न – 50 || कुल अंक – 50

1. वर्ण विचार : स्वर एवं व्यंजन, वर्णों का वर्गीकरण, वर्णों का उच्चारण स्थान।
2. हिन्दी शब्द-समूह : तत्सम, तद्भव, विदेशी, देशज, रूढ़, यौगिक, योगरूढ़ |
3. हिन्दी शब्द-रचना : उपसर्ग, प्रत्यय, संधि, समास।
4. हिन्दी अर्थ-विधान : पर्यायवाची, विलोम, एकार्थी, अनेकार्थी, श्रुतिसम भिन्‍नार्थक
   शब्द |
5. वाक्यांश के लिए एक शब्द।
6. अशुद्धि शोधन : शब्दगत वर्तनी अशुद्धि शोधन, वाक्यगत अशुद्धिशोधन।
7. लोकोक्ति एवं मुहावरे।
8. विराम चिहन।
9. व्याकरण विचार : संज्ञा, सर्वनाम, विशेषण, क्रिया, कारक, लिंग वचन, काल, अव्यय, एवं क्रिया-विशेषण।

सांख्यिकी विश्लेषण की तकनीकें

प्रश्नों की संख्या: 50 अधिकतम अंक: 50

1. सांख्यिकी: परिभाषा, कार्यक्षेत्र, फलन, सीमाएँ, अविश्वास और दुरुपयोग। (Statistics, Definition, Scope, Functions, Limitations, Distrust & Misuse.)
2. आंकड़ों का संग्रह: प्राथमिक आंकड़ें, द्वितीयक आंकड़ें, प्रतिचयन तकनीकों की प्रारंभिक धारणा, वर्गीकरण, सारणीकरण और बारंबारता-बंटन, आंकड़ों की प्रस्तुति: आलेखी और आरेखी।
3. केंद्रीय प्रवृत्ति के माप: अंकगणितीय माध्य, ज्यामितीय माध्य, हरात्मक माध्य, माध्यिका एवं बहुलक | चतुर्थक, दशमक और शतमक।
4. परिक्षेपण की माप: परास, चतुर्थक विचलन, माध्य विचलन, मानक विचलन, विचरण गुणांक, आधूर्ण, वैषम्य, ककुदता और उनकी माप।
5. सूचकांक: अर्थ, महत्व, लास्पेयारे, पाशे, मार्शल और फिशर आदर्श सूचकांक। सूचकांक का निर्माण और उनकी गणना, निर्वाह व्यय सूचकांक। आधार का परिवर्तन, विभाजन सूचकांक, अपस्फीति सूचकांक ।
6. सरल सहसंबंध: रैखिक और कोटि, सरल समाश्रयण विश्लेषण।
7. गणित के मौलिक सिद्धांत: प्रारंभिक समुच्चय सिद्धांत, संख्या प्रणाली, प्रारंभिक बीजगणित, निर्देशांक ज्यामितिय, सरल रेखाएं, दर, अनुपात, प्रतिशत एवं लघुगणक, क्रमचय एवं संचय।
8. प्रारंभिक प्रायिकता सिद्धांत: प्रायिकता की योज्य एवं गुणक प्रमेय, सप्रतिबंध प्रायिकता। भारतीय सांख्यिकी: जनसंख्या, श्रम, धन एवं बैंकिंग, राष्ट्रीय आय, कृषि उत्पादन और
9. औद्योगिक उत्पादन, भारतीय सांख्यिकीय प्रणाली, सी.एस.ओ., एन.एस.एस.ओ., अर्थशास्त्र एवं सांख्यिकी निदेशालय (डी.ई.एस.), उत्तराखंड सरकार, संरचना, कार्य ।

पेपर – 2 Syllabus

OPTIONAL SUBJECT

Note: ऊपर एग्जाम पैटर्न में हमने पांच ऑब्जेक्टिव सब्जेक्ट दिखाएं आपको इन पांच सब्जेक्ट में से केवल एक सब्जेक्ट का चुनाव करना है और उसी का पेपर होगा यहां हम आपको पांचो पेपर का सिलेबस देंगे.

(NOTE :Choose / Select one subject and give answers on the following subjects.)

(01) MATHEMATICS/INDUSTRIAL
MATHEMATICS/APPLIED MATHEMATICS

(Written Nature -Objective Type)

Total Questions- 200 || Max. Marks – 200 || Time – 03 Hours

1. ABSTRACT ALGEBRA:
   (i) Theory of Equations: Existence of roots of the general equation. Descartes’ rule
   of signs. Real and Complex roots of polynomial equations. Relations between the roots and
   coefficients of the equations. Symmetric functions of the roots and transformation of
   equations. Algebraic solution of cubic and biquadratic equations.
   (ii) Groups and Rings: Sets, Relations, Equivalence relations. Groups, Order of
   group, Sub-groups, Centre of a group, Normalizer/Centralizer of an element and a
   subgroup, Cosets of a sub-group, Lagrange’s theorem. Cyclic groups. Normal subgroups,
   Quotient groups. Homomorphisms and Isomorphisms of groups, Kernel of
   homomorphism, Fundamental theorems of group homomorphisms. Permutation groups,
   Cyclic notation, Symmetric and Alternating groups, Cayley’s theorem. Rings,
   Characteristic of a ring, Sub-rings, Ideals and Algebra of ideals.
2. LINEAR ALGEBRA:
   (i) Vector Spaces: Vector spaces and its examples, Subspaces, Linear and Direct
   sums, Quotient space, Linear span of a subset of a vector space. Linear dependence and
   independence of vectors and subsets of vector space. Basis and Dimension of a vector
   space/subspace, Linear and Direct sums, Examples of infinite dimensional spaces, Ordered
   bases and coordinates.
   (ii) Linear transformations: Null spaces and Ranges, Rank and Nullity theorem,
   Invertibility and Isomorphisms of linear transformations, Algebra of linear
   transformations, Matrix representation of a linear transformation, Change of basis, Linear
   functional, Dual spaces, Canonical forms, Eigen values and vectors of linear operators,
   Eigen spaces, Minimal polynomial, Diagonalization of square matrices.
   (iii) Inner Product Spaces: Inner products and norms, Orthogonal and Orthonormal
   vectors, Orthogonal complement of a set. Gram -Schmidt process for orthogonalization.
3. NUMERICAL ANALYSIS:
   (i) Errors: Exact and Approximate numbers, Rounding-off of numbers, Significant
   and Correct digits. Various types of errors encountered in computations, Propagation of
   errors.
   (ii) Solution of equations: Solution of linear simultaneous equations by LU-decomposition method, Gauss elimination method, Jacobi and Gauss- Seidel iterative methods. Roots of non-linear equations by Newton-Raphson and fixed-point iterative methods with their convergence criteria for one variable problems.
   (iii) Interpolation: Finite difference operators, Newton’s forward and backward Interpolation formulae, Bessel and Stirling’s interpolation formulae. Lagrange formula for unequal intervals.
   (iv) Numerical differentiation and Integration: First and Second order differentiation by various interpolation formulae. Trapezoidal, Simpson’s 1/3rd and 3/8th rules with errors.
   (v) Solution of ODE: Solution of first order ordinary differential equation by Picard’s method, Euler and modified Euler method, Runge- Kutta fourth order method.
4. CALCULUS:
   Limit, continuity and differentiability of a function of one variable. Maclaurin and Taylor’s theorems. Maxima and Minima of a function. Limit, Continuity, Differentiability of functions of two variables, Partial differentiation. Euler’s theorem for homogeneous functions. Jacobians, Taylor’s theorem, Maxima and Minima of functions of two variables, Lagrange’s multipliers method.
   Rectification and Quadrature. Surface and Volume formed by revolution of plane curve. Beta and Gamma functions, Double and Triple integrals, Dirichlet integral. Differentiation under integral sign.
5. ANALYTICAL GEOMETRY:
   Two-dimensional geometry of straight lines and conics referred to Cartesian and Polar coordinates systems. Three-dimensional geometry of Planes, Straight lines, Spheres,
   Cones, Cylinders
6. DIFFERENTIAL EQUATIONS:
   Solution of ordinary differential equations of first order, Singular solution. Solution of ordinary linear differential equations of second order with constant coefficients. Euler- Cauchy equations. Solution of second order differential equation by Order reduction method, Change of dependent variable and independent variable methods and method of variation of parameters.
7. COMPLEX ANALYSIS:
   Algebra of complex numbers, Limit, Continuity, Differentiability and Analyticity of a function of a complex variable. Cauchy- Riemann equations, Harmonic functions, Method for finding the harmonic conjugate, Milne’s method for analytic function. Power series, Radius of convergence, Uniform convergence. Cauchy’s theorem and Cauchy integral formula. Taylor’s and Laurent series. Zeros and Poles of a function. Residues at singular points. Cauchy- Residue Theorem, Contour integration of simple proper and improper integrals. Bilinear transformation with applications.
8. MECHANICS:
   (i) Statics: Conditions of equilibrium for a system of forces in 3-dimensions. Virtual work. Centre of gravity of a system. Common Catenary.
   (ii) Dynamics: Tangential and Normal, Radial and Transverse velocities and accelerations. Motion of a particle in a straight line, Simple harmonic motion and its applications. Central forces and Orbit, Constrained motion in circle and cycloid. Motion under resisting medium.
9. MATHEMATICAL ANALYSIS:
   (i) Structure of point Sets: Open sets, Closed sets, Derived sets and Countable sets of real numbers.
   (ii) Convergence of real sequences: Limit points of a sequence, Convergent sequences, Cauchy’s general principle of convergence. Uniform convergence of the sequence of functions, M௡-test.
   (iii) Infinite Series: Infinite Series with different tests of convergence. Uniform convergence of the series of functions. Weierstrass M-test.
   (iv) Riemann Integral: Definition, Refinements of partitions, Riemann sums, Conditions of integrability, Some integrable functions. Fundamental theorems of calculus.
10. LINEAR PROGRAMMING PROBLEMS:
    Convex sets and convex functions. Graphical solution of Linear Programming Problems. The simplex algorithm-method, Big-M and Two-phase methods, Degeneracy, Alternative optima, Unbounded and Infeasible solutions. Duality and Dual simplex method, Relationship between the primal and dual solutions. Transportation and Assignment problems.

(02) STATISTICS/MATHEMATICAL STATISTICS / APPLIED STATISTICS

(Written Nature -Objective Type)

Total Questions- 200 || Max. Marks – 200 || Time – 03 Hours

1. Descriptive Statistics
   Primary and secondary data, classification and presentation of data, diagrammatic and graphical representation of data; Frequency distribution, Measures of location and dispersion, skewness and kurtosis. Bivariate data. Karl Pearson Correlation and Rank correlation coefficient. Partial and multiple correlation. Principle of least squares and curve fitting. Linear regression. Fitting of regression lines, Regression coefficients and their properties. Probability and Distribution Theory Classical, statistical and axiomatic definitions of probability. Additive and Multiplicative laws of probability, Conditional probability, Bayes’ theorem and its applications. Random variable, discrete and continuous random variables; Probability density and Distribution functions and their properties. Joint and marginal distributions, conditional distributions, Distributions of functions of random variables. Mathematical expectation and conditional expectation. Additive and multiplicative theorems of expectation. Characteristic function and moment generating function, Tchebycheff inequality. Weak and strong laws of large numbers and central limit theorem. Standard probability distributions–Discrete Uniform, Bernoulli, Binomial, Poisson, Geometric, Negative binomial, Rectangular, Exponential, Normal, Beta, Gamma; Fitting of Binomial, Poisson and Normal distributions.
2. Inference Estimation:
   Concept of estimation, Properties of good estimator; Estimation by methods of maximum likelihood and moments, Optimal properties of maximum likelihood estimators; Minimum variance unbiased estimators. Minimum variance bound; Cramer-Rao inequality. Sufficient estimator and Factorization theorem. Rao- Blackwell theorem. Interval estimation. Null, Alternative, Simple and composite hypotheses. Two types of errors. Critical region. Power function. Most powerful and uniformly most powerful tests. Neyman- Pearson fundamental lemma. Unbiased and Randomized tests. Likelihood ratio test. Non Parametric Tests: Advantages and disadvantages of non parametric tests, run test for randomness, one sample tests: sign test, Wilcoxon signed rank test, Kolmogarov-Smirnov’s test, Median test and Mann-Whitney U-test.
3. Analysis of Variance and Design of Experiments:
   Analysis of variance for one way and two way classifications; Fixed, mixed and random effect models; Basic principles of experimental design-randomization, replication and local control; Analysis and layout of completely randomized design, randomized block design and Latin square design, Simple and factorial experiments 2² and 2³ experiments.
4. Sampling Theory and Official Statistics
   Concept of population and sample, Complete enumeration versus sampling; sampling and non-sampling errors, Types of sampling: Purposive and random sampling, simple random sampling with and without replacement, estimation of population mean, population proportions and their standard errors. Stratified random sampling, proportional and optimum allocation, comparison with simple random sampling; Systematic sampling (Linear case only), Estimation of population mean and standard error of this estimate, comparison with simple random sampling. Sampling with probability proportional to size (with and without replacement method), Concept of multistage sampling and its application, Introduction to Indian statistical system, national and state statistical systems and their activities, role of CSO & NSSO.
5. Applied statistics Quality Control:
   Process control and product control: control limits, charts for attributes p chart, np chart, c-chart,charts for variables: (Xത,R), (Xത,σ) charts. Acceptance sampling plans for attributes inspection; single and double sampling plans and their properties.
6. Demography and Vital Statistics:
   Complete life table and its main features, Uses of life table. Abridged life tables. Stable and stationary populations. Measurement of Fertility: Crude birth rate, General fertility rate, Age specific birth rate, Total fertility rate, Gross reproduction rate and Net reproduction rate. Measurement of Mortality: Crude death rate, Standardized death rates, Age-specific death rates, Infant Mortality rate and Death rate by cause.
7. Index Numbers: Definition and Construction of Index Numbers, Problems faced in the construction of Index Numbers, Weighted and unweighted Index Numbers. Price relatives and quantity or volume relatives, Laspeyre’s, Paasches’, Marshal Edgeworth and Fisher index numbers; chain base index number, tests for index number, Construction of index numbers of wholesale and consumer prices.
8. Time Series Analysis:
   Introduction to time series data and its applications in various fields; Economic time series, Components of a time series; Additive and multiplicative models, determination of trend, method of moving averages, seasonal and cyclical fluctuations, Estimation of seasonal component by method of simple averages.
9. Multivariate Analysis and Operations Research:
   • Multivariate Analysis
     Random Vector, Bivariate, Trivariate and Multivariate data, Introduction to Multivariate normal distribution and its properties. Estimates of parameters of Multivariate normal distribution (without derivations).
   • Operations Research
     Introduction to Operations Research, Types of OR problems, Linear Programming Problem (LPP), Mathematical formulation of LPP, Solution of LPP by Graphical and Simplex Methods. Transportation problem, Determination of Initial Basic Feasible Solution by North West Corner rule, Least Cost methods and Vogel Approximation method. Assignement problem and its solution. Hungarian method to find optimal solution. Game Theory: Two Person Zero Sum game, Minimax- Maximin Principle. Solution to Game Theory problems using Graphical method. Networking; Shortest route and Minimal Spanning Tree problem, PERT and CPM: Inventory Management, Inventory System and its Characteristics, EOQ Model with and without shortages.

(3) ECONOMICS/APPLIED ECONOMICS/BUSINESS ECONOMICS/ ECONOMETRICS

(Written Nature -Objective Type)

Total Questions- 200 || Max. Marks – 200 || Time – 03 Hours

1. Micro Economics:
   Concept of Micro and Macro economics; Economic Static and Economic Dynamics; Nature of Economics: Normative and Positive; Consumer’s Behaviour and Equilibrium, Meaning of Demand, Law of Demand and Utility Analysis; Indifference Curve Approach; MRS, Budget Line; Revealed Preference Approach; Elasticity of Demand: Types of Elasticity, Factors affecting Elasticity; Price Effect: Income Effect, Substitution Effect; Giffin Goods, Inferior goods; Consumer’s Surplus; Production Function: Law of Variable Proportion and the Laws of Returns to Scale; Economies of Scale; Rate of Technical Substitution (RTS), Iso- quant, MRTS, Iso-cost Line, Cost and Revenue Function; Relationship between MR and Price; Deriving AR and MR from TR; Relationship between AC, MC and TC; Pricing under various forms of market: Perfect Competition, Monopoly, Monopolistic Competition, Duopoly, Oligopoly; Pricing of factors of production; Distribution theories: Rent, Wages, Interest, Profit; Paretian Welfare Economics.
2. Macro Economics:
   National Income: Meaning, Concepts, Measurement and Limitations; Circular Flow of National Income; Methods of National Income Accounting; National Income and Employment: Classical, Keynesian and Neo- Classical theories; Consumption Function – Propensity to Consume and Propensity to Save; Investment Function-Determinants, Marginal Efficiency of Capital and Theory of Multiplier; Trade Cycle: Kaldor and Hicks; Macro Theories of Distribution: Classical and Kaldor; General Equilibrium Analysis; Philips Curve and Rational Expectations Theory.
3. Economic Growth and Development:
   Meaning; Difference between Growth and Development; Measurement of Development: Human Development Index (HDI), Gender Development Index (GDI), Gender Inequality Index (GII), Multi-Dimensional Poverty Index (MPI); Happiness Index; Growth Models: Harrod and Domar, Rostow; Unlimited Supply of Labour; Input-Output Analysis; Globalisation and LDCs; Sustainable Development Goals; Goals of COP26.
4. Money, Banking and Public Finance: Meaning, Concepts, and Functions of Money; Measures of Money Supply; Theories of Money: Fisher, Cambridge, Keynesian, Milton Friedman; Functions of Central Bank; Credit Control; Meaning and Functions of Commercial Banks; Credit Creation; Inflation- Types, Causes, Effects and Controls; Demand for Money and Liquidity Preference Theory; Monetary Policy: Meaning and Objectives.
   Public Finance: Public Expenditure: Theories, Classification and Effects; Taxation: Principles of Taxation, Classification of Taxes, Incidence, Impact and Shifting of Taxes, Theories of Tax Shifting, Taxable Capacity; Theory of Maximum Social Advantage, Sources of Public Revenue; Public Debt- Sources, Effects, Burden and its Management. Fiscal Policy: Meaning and Objectives.
5. Demography: Theory of Demographic Transition; Malthusian Theory; Fertility: Concepts, Measurement and Factors; Mortality; Population Pyramid; Demographic Dividend; Population Growth rate; Sources of Demographic data; Population and Economic Development: Migration and Urbanization.
6. International Economics: Theories of International Trade: Absolute Advantage, Comparative Advantage, Opportunity Cost, Heckscher-Ohlin; Offer Curve, Terms of Trade; Gains from Trade and their Distribution; Tariffs; Custom Union and Free Trade Area; Foreign Trade Multiplier; Working of Foreign Trade Multiplier; Balance of Trade; Balance of Payments and Adjustment Mechanism; Exchange
   Rate: Fixed and Flexible; Foreign Exchange Market: Theories of Exchange rate determination, Objectives of WTO, IMF, World Bank, ADB, SAARC, BRICS and G-20.
7. Econometrics: Meaning and Scope: Single linear equation regression model (by OLS Method); General Linear Regression Model (GLM).
8. Indian Economy: Sectoral Distribution of National Income; Trends in Population Growth and Demographic Changes; Poverty in India:- Measurement, Nature and Poverty Alleviation Programmes; Unemployment : Nature, Problems and Remedies; Inequality in India; Recent Social Security measures; Economic Planning in India; NITI Aayog: Constitution, Role and Functions; Centre-State Financial Relations:- Recommendations of 15th Finance Commission; GST; EXIM Policy. Role and Problems of Agriculture:- Share of agriculture in National Income and Workforce; Green Revolution; Land Reforms; Rural Credit, Indebtedness and NABARD; Agricultural Marketing; Agricultural Price Policy; Problems of Agricultural Labourers; Food Security and Climate Change. Industrialization: MSMEs and Large Scale Industries; Role of Public and Private Sector; Foreign Capital, Foreign Direct Investment and MNCs; Industrial Policy; New Economic Policy; Recent schemes for the promotion of MSMEs.
9. Uttarakhand Economy: -Demographic Characteristics; Role of Agriculture, Industry and Service Sectors in State Economy; Natural Resources; Human Resources; Infrastructure; Economic Problems: Natural Disaster Management, Environmental Degradation; Migration: Causes, Impact and Remedies; Tourism: Problems and Prospects.

(04) COMMERCE

(Written Nature -Objective Type)

Total Questions- 200 || Max. Marks – 200 || Time – 03 Hours

1. Financial and Management Accounting
   Accounting Concepts and Conventions, Accounting Standards, Capital & Revenue, Methods of Charging Depreciation, Financial Statements-Trading Account, Manufacturing Account, Profit and Loss Account, Balance Sheet. Corporate Accounts- Issue of shares, forfeiture of shares and reissue of forfeited shares. Issue and Redemption of Debentures, Liquidation of Companies, Valuation of Goodwill and Shares, Accounting for Amalgamation of Companies (as per AS 14), Bonus Shares vs. Right Issues.
   Management Accounting- Ratio Analysis, Marginal Costing, Break-even Analysis, Standard Costing, Budgetary Control, Zero-base Budgeting and Performance Budgeting, Funds Flow Statement, Cash Flow Statement, Responsibility Accounting.
2. Banking and Financial Institutions
   Central banking in India, Methods of Credit Control by RBI, Commercial Banks, Banking Sector Reforms in India, NPA, Capital Adequacy Norms, Latest trends in E-Banking, Digital Currency, Payment Banks, and UPI Payments. De-monetization, and Banking Industry in India, Development Banks in India, SEBI and its role as regulator, NBFCs in India, Capital and Money Market and their instruments, Stock Exchanges in India.
3. Business Management and Communication:
   Functions of Management- Planning, Organising, Staffing, Directing and Controlling, Decision Making Process, Theories of Motivation, Leadership Styles, Theories of Leadership, Management by Objectives, Management by Exception. Communication- Concept, Body Language, Verbal & Non-verbal Communication, e-mail, Presentation, Interview, Meetings, Seminars, Parts of a Letter, Essentials of an Ideal Letter, Circulars, Office Memorandum, and Report Writing. Corporate Social Responsibility.
4. Marketing Management:
   Marketing- Concept, Direct Marketing, Ps of Marketing, Market Segmentation, Product Life Cycle, Branding and Advertisement, SWOT Analysis and Pricing Strategies, E-Marketing and Green Marketing.
5. Auditing:
   Standards of Auditing, Internal Control, Audit Sampling and Test Checking, Routine Checking, Vouchers and Vouching, Internal Check and Internal Audit, Verification and Valuation of Assets & Liabilities, Auditor’s Report on Profit & Loss Account and Balance Sheet, Qualifications and Appointment of Company Auditors, their powers, duties and liabilities as per Companies Act 2013, Managerial Remuneration. Cost Audit and Management Audit.
6. Managerial Economics:
   Fundamental Economic Concepts, Law of Diminishing Marginal Utility, Demand Forecasting, Market Forces: Demand and Supply, Price Determination in Various Competition- Perfect Competition, Monopoly, Monopolistic and Oligopoly, Price Discrimination, Phases of Business Cycle, Inflation, Monetary and Fiscal Policy.
7. International Business:
   Growing Relevance of International Business, Balance of Trade and Balance of Payment, International Advertisement, Bi-lateral Trade, Ethical Issues in International Business, Liberalisation, Privatisation and Globalisation (LPG), Information Technology and International Business, Export Promotion Techniques and Incentives, Foreign Direct Investment (FDI), International Economic
   • Institutions- IMF, World Bank, WTO, NAFTA, EU. FEMA, EXIM Policy,
   • International Financial Instruments.
8. Business Statistics and Data Processing:
   
   • Types of Data- Primary and Secondary, Questionnaire and Scales-Types, Preparation and Standardization, Data Collection and Analysis, Interpretation of Data. Measurement of Central Tendency, Dispersion and Skewness, Correlation and Regression, Index Number, Analysis of Time Series, Interpolation and Extrapolation, Theoretical Distribution- Normal Distribution. Sampling- Design and Methods of Sampling. Elementary Theory of testing a Hypothesis, t-test, F-test (ANOVA), Z-test and Chi-square test.
   • Data Processing- Its need, Elements, Data Entry, Data Processing and Computer Applications.

(05) DATA SCIENCE

(Written Nature -Objective Type)

Total Questions- 200 || Max. Marks – 200 || Time – 03 Hours

Introduction to Data Science
Data Science, types of data, skill required for data science, personnel involved with data science, Data Science Process (DSP), Big data vs. Data Science, Big Data Analytics in Vertical Industry, Big Data Frameworks, Big Data Applications, Tools and Technologies.

Exploratory Data Analysis:

Primary and secondary data, classification and presentation of data, diagrammatic
and graphical representation of data, Frequency distribution. Measures of location and dispersion, skewness and kurtosis. Bivariate and multivariate data. Principle of least squares and curve fitting. Linear regression and fitting of regression lines by method of least squares, Regression coefficients and their properties; Concept of correlation, Karl Pearson and Rank correlation coefficients, partial and multiple correlation. Rank correlation. Association in contingency tables. Sampling, sampling from a population, estimation of parameters and sampling distributions of statistics, confidence intervals, testing of hypothesis.
Z, t, Chi-Square & F-test, interval estimation. Basic principles of designs of experiments and ANOVA; One and two factor ANOVA, factorials experiments.

Data Mining

Data pre-processing: Introduction to data pre-processing, data cleaning, data integration, data reduction, data transformation and data discretionary. Data warehouse: Basic concepts, data warehouse modelling: data cube and OLAP, data warehouse design and usage, data warehouse implementation. Mining frequent
patterns, associations, and correlations: Concepts, frequent item set mining methods, interesting patterns, pattern evaluation methods.

Data Visualization

Purpose, scope, communication style, quantitative relationships, differing roles of tables and graphs. Exploring the visual data spectrum: charting primitives (data points, line charts, bar charts, pie charts, area charts), exploring advanced visualizations (candlestick charts, bubble charts, surface charts, map charts,
infographics).

Big Data Analytics:

Characteristics of big data and dimensions of scalability, getting value out of big data, foundations for big data systems and programming. Near-neighbour search, shingling, similarity preserving summary, locality sensitive functions, distance measures, locality sensitive hashing and its applications to different distance measures, applications of locality sensitive hashing. Big Data Analytics Tools: Hadoop, Spark and Google Colaboratory.

Generative models for discrete data:

The Dirichlet multinomial model – likelihood, prior, posterior, posterior predictive; Naive Bayes classifiers – model fitting, using the model for prediction, the log-sum-exp trick, feature selection using mutual information, classifying documents using bag of words.

Clustering
Introduction to clustering, mixture densities, k-means clustering, supervised learning after clustering, hierarchical clustering, choosing the number of clusters.

Machine Learning
Introduction to machine learning, types of machine learning, Nearest neighbour methods, Validation: Nearest neighbour prediction, k-nearest neighbour methods, weighted neighbour methods, Curse of dimensionality, Linear Classification, characterizing a linear classifier, training a linear classifier, logistic regression. Decision trees: predictor form, training decision trees, decision tree classifiers, univariate trees – classification trees, regression trees, pruning, ruleextraction from trees, learning rules from data.

Design and Analysis of Machine Learning Experiments:
Factors, response, and strategy of experimentation, randomization, replication, and blocking, guidelines for machine learning experiments, cross-validation and re- sampling methods, k-fold cross-validation, 5*2 cross-validation, Measuring classifier performance, Assessing a classification algorithm’s performance –
binomial test, approximate normal test, comparison over multiple datasets– comparing two algorithms, multiple algorithms.

Deep Learning
The neural network – building intelligent machines, the limits of traditional computer programs, the mechanics of machine learning, the neuron, expressing linear perceptrons as neurons, feed-forward neural networks, linear neurons and their limitations. Training feed-forward neural networks – gradient descent, the back propagation algorithm. Neurons in human vision, the shortcomings of feature selection, filters and feature maps, Deep reinforcement learning, Markov Decision Processes (MDP), explore versus exploit, policy versus value learning.