Topics for Writing Content for Internship

Daily writing prompt
If you could un-invent something, what would it be?

General Categories

AGeneral Economics and Teaching
BHistory of Economic Thought, Methodology, and Heterodox Approaches
CMathematical and Quantitative Methods
DMicroeconomics
EMacroeconomics and Monetary Economics
FInternational Economics
GFinancial Economics
HPublic Economics
IHealth, Education, and Welfare
JLabor and Demographic Economics
KLaw and Economics
LIndustrial Organization
MBusiness Administration and Business Economics • Marketing • Accounting • Personnel Economics
NEconomic History
OEconomic Development, Innovation, Technological Change, and Growth
PPolitical Economy and Comparative Economic Systems
QAgricultural and Natural Resource Economics • Environmental and Ecological Economics
RUrban, Rural, Regional, Real Estate, and Transportation Economics
YMiscellaneous Categories
ZOther Special Topics

A. General Economics and Teaching

 
A1General Economics
A10General
A11Role of Economics • Role of Economists • Market for Economists
A12Relation of Economics to Other Disciplines
A13Relation of Economics to Social Values
A14Sociology of Economics
A19Other
 
A2Economic Education and Teaching of Economics
A20General
A21Pre-college
A22Undergraduate
A23Graduate
A29Other
 
A3Collective Works
A30General
A31Collected Writings of Individuals
A32Collective Volumes
A33Handbooks
A39Other

B. History of Economic Thought, Methodology, and Heterodox Approaches

 
B00General
 
B1History of Economic Thought through 1925
B10General
B11Preclassical (Ancient, Medieval, Mercantilist, Physiocratic)
B12Classical (includes Adam Smith)
B13Neoclassical through 1925 (Austrian, Marshallian, Walrasian, Wicksellian)
B14Socialist • Marxist
B15Historical • Institutional • Evolutionary
B16Quantitative and Mathematical
B17International Trade and Finance
B19Other
 
B2History of Economic Thought since 1925
B20General
B21Microeconomics
B22Macroeconomics
B23Econometrics • Quantitative and Mathematical Studies
B24Socialist • Marxist • Sraffian
B25Historical • Institutional • Evolutionary • Austrian • Stockholm School
B26Financial Economics
B27International Trade and Finance
B29Other
 
B3History of Economic Thought: Individuals
B30General
B31Individuals
B32Obituaries
 
B4Economic Methodology
B40General
B41Economic Methodology
B49Other
 
B5Current Heterodox Approaches
B50General
B51Socialist • Marxian • Sraffian
B52Historical • Institutional • Evolutionary • Modern Monetary Theory
B53Austrian
B54Feminist Economics
B55Social Economics
B59Other

C. Mathematical and Quantitative Methods

 
C00General
C01Econometrics
C02Mathematical Methods
 
C1Econometric and Statistical Methods and Methodology: General
C10General
C11Bayesian Analysis: General
C12Hypothesis Testing: General
C13Estimation: General
C14Semiparametric and Nonparametric Methods: General
C15Statistical Simulation Methods: General
C18Methodological Issues: General
C19Other
 
C2Single Equation Models • Single Variables
C20General
C21Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions
C22Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes
C23Panel Data Models • Spatio-temporal Models
C24Truncated and Censored Models • Switching Regression Models • Threshold Regression Models
C25Discrete Regression and Qualitative Choice Models • Discrete Regressors • Proportions • Probabilities
C26Instrumental Variables (IV) Estimation
C29Other
 
C3Multiple or Simultaneous Equation Models • Multiple Variables
C30General
C31Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions • Social Interaction Models
C32Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes • State Space Models
C33Panel Data Models • Spatio-temporal Models
C34Truncated and Censored Models • Switching Regression Models
C35Discrete Regression and Qualitative Choice Models • Discrete Regressors • Proportions
C36Instrumental Variables (IV) Estimation
C38Classification Methods • Cluster Analysis • Principal Components • Factor Models
C39Other
 
C4Econometric and Statistical Methods: Special Topics
C40General
C41Duration Analysis • Optimal Timing Strategies
C43Index Numbers and Aggregation
C44Operations Research • Statistical Decision Theory
C45Neural Networks and Related Topics
C46Specific Distributions • Specific Statistics
C49Other
 
C5Econometric Modeling
C50General
C51Model Construction and Estimation
C52Model Evaluation, Validation, and Selection
C53Forecasting and Prediction Methods • Simulation Methods
C54Quantitative Policy Modeling
C55Large Data Sets: Modeling and Analysis
C57Econometrics of Games and Auctions
C58Financial Econometrics
C59Other
 
C6Mathematical Methods • Programming Models • Mathematical and Simulation Modeling
C60General
C61Optimization Techniques • Programming Models • Dynamic Analysis
C62Existence and Stability Conditions of Equilibrium
C63Computational Techniques • Simulation Modeling
C65Miscellaneous Mathematical Tools
C67Input–Output Models
C68Computable General Equilibrium Models
C69Other
 
C7Game Theory and Bargaining Theory
C70General
C71Cooperative Games
C72Noncooperative Games
C73Stochastic and Dynamic Games • Evolutionary Games • Repeated Games
C78Bargaining Theory • Matching Theory
C79Other
 
C8Data Collection and Data Estimation Methodology • Computer Programs
C80General
C81Methodology for Collecting, Estimating, and Organizing Microeconomic Data • Data Access
C82Methodology for Collecting, Estimating, and Organizing Macroeconomic Data • Data Access
C83Survey Methods • Sampling Methods
C87Econometric Software
C88Other Computer Software
C89Other
 
C9Design of Experiments
C90General
C91Laboratory, Individual Behavior
C92Laboratory, Group Behavior
C93Field Experiments
C99Other

D. Microeconomics

 
D00General
D01Microeconomic Behavior: Underlying Principles
D02Institutions: Design, Formation, Operations, and Impact
D04Microeconomic Policy: Formulation, Implementation, and Evaluation
 
D1Household Behavior and Family Economics
D10General
D11Consumer Economics: Theory
D12Consumer Economics: Empirical Analysis
D13Household Production and Intrahousehold Allocation
D14Household Saving • Personal Finance
D15Intertemporal Household Choice • Life Cycle Models and Saving
D16Collaborative Consumption
D18Consumer Protection
D19Other
 
D2Production and Organizations
D20General
D21Firm Behavior: Theory
D22Firm Behavior: Empirical Analysis
D23Organizational Behavior • Transaction Costs • Property Rights
D24Production • Cost • Capital • Capital, Total Factor, and Multifactor Productivity • Capacity
D25Intertemporal Firm Choice: Investment, Capacity, and Financing
D26Crowd-Based Firms
D29Other
 
D3Distribution
D30General
D31Personal Income, Wealth, and Their Distributions
D33Factor Income Distribution
D39Other
 
D4Market Structure, Pricing, and Design
D40General
D41Perfect Competition
D42Monopoly
D43Oligopoly and Other Forms of Market Imperfection
D44Auctions
D45Rationing • Licensing
D46Value Theory
D47Market Design
D49Other
 
D5General Equilibrium and Disequilibrium
D50General
D51Exchange and Production Economies
D52Incomplete Markets
D53Financial Markets
D57Input–Output Tables and Analysis
D58Computable and Other Applied General Equilibrium Models
D59Other
 
D6Welfare Economics
D60General
D61Allocative Efficiency • Cost–Benefit Analysis
D62Externalities
D63Equity, Justice, Inequality, and Other Normative Criteria and Measurement
D64Altruism • Philanthropy • Intergenerational Transfers
D69Other
 
D7Analysis of Collective Decision-Making
D70General
D71Social Choice • Clubs • Committees • Associations
D72Political Processes: Rent-Seeking, Lobbying, Elections, Legislatures, and Voting Behavior
D73Bureaucracy • Administrative Processes in Public Organizations • Corruption
D74Conflict • Conflict Resolution • Alliances • Revolutions
D78Positive Analysis of Policy Formulation and Implementation
D79Other
 
D8Information, Knowledge, and Uncertainty
D80General
D81Criteria for Decision-Making under Risk and Uncertainty
D82Asymmetric and Private Information • Mechanism Design
D83Search • Learning • Information and Knowledge • Communication • Belief • Unawareness
D84Expectations • Speculations
D85Network Formation and Analysis: Theory
D86Economics of Contract: Theory
D87Neuroeconomics
D89Other
 
D9Micro-Based Behavioral Economics
D90General
D91Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

E. Macroeconomics and Monetary Economics

 
E00General
E01Measurement and Data on National Income and Product Accounts and Wealth • Environmental Accounts
E02Institutions and the Macroeconomy
 
E1General Aggregative Models
E10General
E11Marxian • Sraffian • Kaleckian
E12Keynes • Keynesian • Post-Keynesian • Modern Monetary Theory
E13Neoclassical
E14Austrian • Evolutionary • Institutional
E16Social Accounting Matrix
E17Forecasting and Simulation: Models and Applications
E19Other
 
E2Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy
E20General
E21Consumption • Saving • Wealth
E22Investment • Capital • Intangible Capital • Capacity
E23Production
E24Employment • Unemployment • Wages • Intergenerational Income Distribution • Aggregate Human Capital • Aggregate Labor Productivity
E25Aggregate Factor Income Distribution
E26Informal Economy • Underground Economy
E27Forecasting and Simulation: Models and Applications
E29Other
 
E3Prices, Business Fluctuations, and Cycles
E30General
E31Price Level • Inflation • Deflation
E32Business Fluctuations • Cycles
E37Forecasting and Simulation: Models and Applications
E39Other
 
E4Money and Interest Rates
E40General
E41Demand for Money
E42Monetary Systems • Standards • Regimes • Government and the Monetary System • Payment Systems
E43Interest Rates: Determination, Term Structure, and Effects
E44Financial Markets and the Macroeconomy
E47Forecasting and Simulation: Models and Applications
E49Other
 
E5Monetary Policy, Central Banking, and the Supply of Money and Credit
E50General
E51Money Supply • Credit • Money Multipliers
E52Monetary Policy
E58Central Banks and Their Policies
E59Other
 
E6Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
E60General
E61Policy Objectives • Policy Designs and Consistency • Policy Coordination
E62Fiscal Policy • Modern Monetary Theory
E63Comparative or Joint Analysis of Fiscal and Monetary Policy • Stabilization • Treasury Policy
E64Incomes Policy • Price Policy
E65Studies of Particular Policy Episodes
E66General Outlook and Conditions
E69Other
 
E7Macro-Based Behavioral Economics
E70General
E71Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on the Macro Economy

F. International Economics

 
F00General
F01Global Outlook
F02International Economic Order and Integration
 
F1Trade
F10General
F11Neoclassical Models of Trade
F12Models of Trade with Imperfect Competition and Scale Economies • Fragmentation
F13Trade Policy • International Trade Organizations
F14Empirical Studies of Trade
F15Economic Integration
F16Trade and Labor Market Interactions
F17Trade Forecasting and Simulation
F18Trade and Environment
F19Other
 
F2International Factor Movements and International Business
F20General
F21International Investment • Long-Term Capital Movements
F22International Migration
F23Multinational Firms • International Business
F24Remittances
F29Other
 
F3International Finance
F30General
F31Foreign Exchange
F32Current Account Adjustment • Short-Term Capital Movements
F33International Monetary Arrangements and Institutions
F34International Lending and Debt Problems
F35Foreign Aid
F36Financial Aspects of Economic Integration
F37International Finance Forecasting and Simulation: Models and Applications
F38International Financial Policy: Financial Transactions Tax; Capital Controls
F39Other
 
F4Macroeconomic Aspects of International Trade and Finance
F40General
F41Open Economy Macroeconomics
F42International Policy Coordination and Transmission
F43Economic Growth of Open Economies
F44International Business Cycles
F45Macroeconomic Issues of Monetary Unions
F47Forecasting and Simulation: Models and Applications
F49Other
 
F5International Relations, National Security, and International Political Economy
F50General
F51International Conflicts • Negotiations • Sanctions
F52National Security • Economic Nationalism
F53International Agreements and Observance • International Organizations
F54Colonialism • Imperialism • Postcolonialism
F55International Institutional Arrangements
F59Other
 
F6Economic Impacts of Globalization
F60General
F61Microeconomic Impacts
F62Macroeconomic Impacts
F63Economic Development
F64Environment
F65Finance
F66Labor
F68Policy
F69Other

G. Financial Economics

 
G00General
G01Financial Crises
 
G1General Financial Markets
G10General
G11Portfolio Choice • Investment Decisions
G12Asset Pricing • Trading Volume • Bond Interest Rates
G13Contingent Pricing • Futures Pricing
G14Information and Market Efficiency • Event Studies • Insider Trading
G15International Financial Markets
G17Financial Forecasting and Simulation
G18Government Policy and Regulation
G19Other
 
G2Financial Institutions and Services
G20General
G21Banks • Depository Institutions • Micro Finance Institutions • Mortgages
G22Insurance • Insurance Companies • Actuarial Studies
G23Non-bank Financial Institutions • Financial Instruments • Institutional Investors
G24Investment Banking • Venture Capital • Brokerage • Ratings and Ratings Agencies
G28Government Policy and Regulation
G29Other
 
G3Corporate Finance and Governance
G30General
G31Capital Budgeting • Fixed Investment and Inventory Studies • Capacity
G32Financing Policy • Financial Risk and Risk Management • Capital and Ownership Structure • Value of Firms • Goodwill
G33Bankruptcy • Liquidation
G34Mergers • Acquisitions • Restructuring • Corporate Governance
G35Payout Policy
G38Government Policy and Regulation
G39Other
 
G4Behavioral Finance
G40General
G41Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
 
G5Household Finance
G50General
G51Household Saving, Borrowing, Debt, and Wealth
G52Insurance
G53Financial Literacy
G59Other

H. Public Economics

 
H00General
 
H1Structure and Scope of Government
H10General
H11Structure, Scope, and Performance of Government
H12Crisis Management
H13Economics of Eminent Domain • Expropriation • Nationalization
H19Other
 
H2Taxation, Subsidies, and Revenue
H20General
H21Efficiency • Optimal Taxation
H22Incidence
H23Externalities • Redistributive Effects • Environmental Taxes and Subsidies
H24Personal Income and Other Nonbusiness Taxes and Subsidies
H25Business Taxes and Subsidies
H26Tax Evasion and Avoidance
H27Other Sources of Revenue
H29Other
 
H3Fiscal Policies and Behavior of Economic Agents
H30General
H31Household
H32Firm
H39Other
 
H4Publicly Provided Goods
H40General
H41Public Goods
H42Publicly Provided Private Goods
H43Project Evaluation • Social Discount Rate
H44Publicly Provided Goods: Mixed Markets
H49Other
 
H5National Government Expenditures and Related Policies
H50General
H51Government Expenditures and Health
H52Government Expenditures and Education
H53Government Expenditures and Welfare Programs
H54Infrastructures • Other Public Investment and Capital Stock
H55Social Security and Public Pensions
H56National Security and War
H57Procurement
H59Other
 
H6National Budget, Deficit, and Debt
H60General
H61Budget • Budget Systems
H62Deficit • Surplus
H63Debt • Debt Management • Sovereign Debt
H68Forecasts of Budgets, Deficits, and Debt
H69Other
 
H7State and Local Government • Intergovernmental Relations
H70General
H71State and Local Taxation, Subsidies, and Revenue
H72State and Local Budget and Expenditures
H73Interjurisdictional Differentials and Their Effects
H74State and Local Borrowing
H75State and Local Government: Health • Education • Welfare • Public Pensions
H76State and Local Government: Other Expenditure Categories
H77Intergovernmental Relations • Federalism • Secession
H79Other
 
H8Miscellaneous Issues
H80General
H81Governmental Loans • Loan Guarantees • Credits • Grants • Bailouts
H82Governmental Property
H83Public Administration • Public Sector Accounting and Audits
H84Disaster Aid
H87International Fiscal Issues • International Public Goods
H89Other

I. Health, Education, and Welfare

 
I00General
 
I1Health
I10General
I11Analysis of Health Care Markets
I12Health Behavior
I13Health Insurance, Public and Private
I14Health and Inequality
I15Health and Economic Development
I18Government Policy • Regulation • Public Health
I19Other
 
I2Education and Research Institutions
I20General
I21Analysis of Education
I22Educational Finance • Financial Aid
I23Higher Education • Research Institutions
I24Education and Inequality
I25Education and Economic Development
I26Returns to Education
I28Government Policy
I29Other
 
I3Welfare, Well-Being, and Poverty
I30General
I31General Welfare, Well-Being
I32Measurement and Analysis of Poverty
I38Government Policy • Provision and Effects of Welfare Programs
I39Other

J. Labor and Demographic Economics

 
J00General
J01Labor Economics: General
J08Labor Economics Policies
 
J1Demographic Economics
J10General
J11Demographic Trends, Macroeconomic Effects, and Forecasts
J12Marriage • Marital Dissolution • Family Structure • Domestic Abuse
J13Fertility • Family Planning • Child Care • Children • Youth
J14Economics of the Elderly • Economics of Disability • Non-Labor Market Discrimination
J15Economics of Minorities, Races, Indigenous Peoples, and Immigrants • Non-labor Discrimination
J16Economics of Gender • Non-labor Discrimination
J17Value of Life • Forgone Income
J18Public Policy
J19Other
 
J2Demand and Supply of Labor
J20General
J21Labor Force and Employment, Size, and Structure
J22Time Allocation and Labor Supply
J23Labor Demand
J24Human Capital • Skills • Occupational Choice • Labor Productivity
J26Retirement • Retirement Policies
J28Safety • Job Satisfaction • Related Public Policy
J29Other
 
J3Wages, Compensation, and Labor Costs
J30General
J31Wage Level and Structure • Wage Differentials
J32Nonwage Labor Costs and Benefits • Retirement Plans • Private Pensions
J33Compensation Packages • Payment Methods
J38Public Policy
J39Other
 
J4Particular Labor Markets
J40General
J41Labor Contracts
J42Monopsony • Segmented Labor Markets
J43Agricultural Labor Markets
J44Professional Labor Markets • Occupational Licensing
J45Public Sector Labor Markets
J46Informal Labor Markets
J47Coercive Labor Markets
J48Public Policy
J49Other
 
J5Labor–Management Relations, Trade Unions, and Collective Bargaining
J50General
J51Trade Unions: Objectives, Structure, and Effects
J52Dispute Resolution: Strikes, Arbitration, and Mediation • Collective Bargaining
J53Labor–Management Relations • Industrial Jurisprudence
J54Producer Cooperatives • Labor Managed Firms • Employee Ownership
J58Public Policy
J59Other
 
J6Mobility, Unemployment, Vacancies, and Immigrant Workers
J60General
J61Geographic Labor Mobility • Immigrant Workers
J62Job, Occupational, and Intergenerational Mobility
J63Turnover • Vacancies • Layoffs
J64Unemployment: Models, Duration, Incidence, and Job Search
J65Unemployment Insurance • Severance Pay • Plant Closings
J68Public Policy
J69Other
 
J7Labor Discrimination
J70General
J71Discrimination
J78Public Policy
J79Other
 
J8Labor Standards: National and International
J80General
J81Working Conditions
J82Labor Force Composition
J83Workers’ Rights
J88Public Policy
J89Other

K. Law and Economics

 
K00General
 
K1Basic Areas of Law
K10General
K11Property Law
K12Contract Law
K13Tort Law and Product Liability • Forensic Economics
K14Criminal Law
K15Civil Law • Common Law
K16Election Law
K19Other
 
K2Regulation and Business Law
K20General
K21Antitrust Law
K22Business and Securities Law
K23Regulated Industries and Administrative Law
K24Cyber Law
K25Real Estate Law
K29Other
 
K3Other Substantive Areas of Law
K30General
K31Labor Law
K32Energy, Environmental, Health, and Safety Law
K33International Law
K34Tax Law
K35Personal Bankruptcy Law
K36Family and Personal Law
K37Immigration Law
K38Human Rights Law • Gender Law • Animal Rights Law
K39Other
 
K4Legal Procedure, the Legal System, and Illegal Behavior
K40General
K41Litigation Process
K42Illegal Behavior and the Enforcement of Law
K49Other

L. Industrial Organization

 
L00General
 
L1Market Structure, Firm Strategy, and Market Performance
L10General
L11Production, Pricing, and Market Structure • Size Distribution of Firms
L12Monopoly • Monopolization Strategies
L13Oligopoly and Other Imperfect Markets
L14Transactional Relationships • Contracts and Reputation • Networks
L15Information and Product Quality • Standardization and Compatibility
L16Industrial Organization and Macroeconomics: Industrial Structure and Structural Change • Industrial Price Indices
L17Open Source Products and Markets
L19Other
 
L2Firm Objectives, Organization, and Behavior
L20General
L21Business Objectives of the Firm
L22Firm Organization and Market Structure
L23Organization of Production
L24Contracting Out • Joint Ventures • Technology Licensing
L25Firm Performance: Size, Diversification, and Scope
L26Entrepreneurship
L29Other
 
L3Nonprofit Organizations and Public Enterprise
L30General
L31Nonprofit Institutions • NGOs • Social Entrepreneurship
L32Public Enterprises • Public-Private Enterprises
L33Comparison of Public and Private Enterprises and Nonprofit Institutions • Privatization • Contracting Out
L38Public Policy
L39Other
 
L4Antitrust Issues and Policies
L40General
L41Monopolization • Horizontal Anticompetitive Practices
L42Vertical Restraints • Resale Price Maintenance • Quantity Discounts
L43Legal Monopolies and Regulation or Deregulation
L44Antitrust Policy and Public Enterprises, Nonprofit Institutions, and Professional Organizations
L49Other
 
L5Regulation and Industrial Policy
L50General
L51Economics of Regulation
L52Industrial Policy • Sectoral Planning Methods
L53Enterprise Policy
L59Other
 
L6Industry Studies: Manufacturing
L60General
L61Metals and Metal Products • Cement • Glass • Ceramics
L62Automobiles • Other Transportation Equipment • Related Parts and Equipment
L63Microelectronics • Computers • Communications Equipment
L64Other Machinery • Business Equipment • Armaments
L65Chemicals • Rubber • Drugs • Biotechnology • Plastics
L66Food • Beverages • Cosmetics • Tobacco • Wine and Spirits
L67Other Consumer Nondurables: Clothing, Textiles, Shoes, and Leather Goods; Household Goods; Sports Equipment
L68Appliances • Furniture • Other Consumer Durables
L69Other
 
L7Industry Studies: Primary Products and Construction
L70General
L71Mining, Extraction, and Refining: Hydrocarbon Fuels
L72Mining, Extraction, and Refining: Other Nonrenewable Resources
L73Forest Products
L74Construction
L78Government Policy
L79Other
 
L8Industry Studies: Services
L80General
L81Retail and Wholesale Trade • e-Commerce
L82Entertainment • Media
L83Sports • Gambling • Restaurants • Recreation • Tourism
L84Personal, Professional, and Business Services
L85Real Estate Services
L86Information and Internet Services • Computer Software
L87Postal and Delivery Services
L88Government Policy
L89Other
 
L9Industry Studies: Transportation and Utilities
L90General
L91Transportation: General
L92Railroads and Other Surface Transportation
L93Air Transportation
L94Electric Utilities
L95Gas Utilities • Pipelines • Water Utilities
L96Telecommunications
L97Utilities: General
L98Government Policy
L99Other

M. Business Administration and Business Economics • Marketing • Accounting • Personnel Economics

 
M00General
 
M1Business Administration
M10General
M11Production Management
M12Personnel Management • Executives; Executive Compensation
M13New Firms • Startups
M14Corporate Culture • Diversity • Social Responsibility
M15IT Management
M16International Business Administration
M19Other
 
M2Business Economics
M20General
M21Business Economics
M29Other
 
M3Marketing and Advertising
M30General
M31Marketing
M37Advertising
M38Government Policy and Regulation
M39Other
 
M4Accounting and Auditing
M40General
M41Accounting
M42Auditing
M48Government Policy and Regulation
M49Other
 
M5Personnel Economics
M50General
M51Firm Employment Decisions • Promotions
M52Compensation and Compensation Methods and Their Effects
M53Training
M54Labor Management
M55Labor Contracting Devices
M59Other

N. Economic History

 
N00General
N01Development of the Discipline: Historiographical; Sources and Methods
 
N1Macroeconomics and Monetary Economics • Industrial Structure • Growth • Fluctuations
N10General, International, or Comparative
N11U.S. • Canada: Pre-1913
N12U.S. • Canada: 1913–
N13Europe: Pre-1913
N14Europe: 1913–
N15Asia including Middle East
N16Latin America • Caribbean
N17Africa • Oceania
 
N2Financial Markets and Institutions
N20General, International, or Comparative
N21U.S. • Canada: Pre-1913
N22U.S. • Canada: 1913–
N23Europe: Pre-1913
N24Europe: 1913–
N25Asia including Middle East
N26Latin America • Caribbean
N27Africa • Oceania
 
N3Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy
N30General, International, or Comparative
N31U.S. • Canada: Pre-1913
N32U.S. • Canada: 1913-
N33Europe: Pre-1913
N34Europe: 1913-
N35Asia including Middle East
N36Latin America • Caribbean
N37Africa • Oceania
 
N4Government, War, Law, International Relations, and Regulation
N40General, International, or Comparative
N41U.S. • Canada: Pre-1913
N42U.S. • Canada: 1913–
N43Europe: Pre-1913
N44Europe: 1913–
N45Asia including Middle East
N46Latin America • Caribbean
N47Africa • Oceania
 
N5Agriculture, Natural Resources, Environment, and Extractive Industries
N50General, International, or Comparative
N51U.S. • Canada: Pre-1913
N52U.S. • Canada: 1913–
N53Europe: Pre-1913
N54Europe: 1913–
N55Asia including Middle East
N56Latin America • Caribbean
N57Africa • Oceania
 
N6Manufacturing and Construction
N60General, International, or Comparative
N61U.S. • Canada: Pre-1913
N62U.S. • Canada: 1913–
N63Europe: Pre-1913
N64Europe: 1913–
N65Asia including Middle East
N66Latin America • Caribbean
N67Africa • Oceania
 
N7Transport, Trade, Energy, Technology, and Other Services
N70General, International, or Comparative
N71U.S. • Canada: Pre-1913
N72U.S. • Canada: 1913–
N73Europe: Pre-1913
N74Europe: 1913–
N75Asia including Middle East
N76Latin America • Caribbean
N77Africa • Oceania
 
N8Micro-Business History
N80General, International, or Comparative
N81U.S. • Canada: Pre-1913
N82U.S. • Canada: 1913–
N83Europe: Pre-1913
N84Europe: 1913–
N85Asia including Middle East
N86Latin America • Caribbean
N87Africa • Oceania
 
N9Regional and Urban History
N90General, International, or Comparative
N91U.S. • Canada: Pre-1913
N92U.S. • Canada: 1913–
N93Europe: Pre-1913
N94Europe: 1913–
N95Asia including Middle East
N96Latin America • Caribbean
N97Africa • Oceania

O. Economic Development, Innovation, Technological Change, and Growth

 
O1Economic Development
O10General
O11Macroeconomic Analyses of Economic Development
O12Microeconomic Analyses of Economic Development
O13Agriculture • Natural Resources • Energy • Environment • Other Primary Products
O14Industrialization • Manufacturing and Service Industries • Choice of Technology
O15Human Resources • Human Development • Income Distribution • Migration
O16Financial Markets • Saving and Capital Investment • Corporate Finance and Governance
O17Formal and Informal Sectors • Shadow Economy • Institutional Arrangements
O18Urban, Rural, Regional, and Transportation Analysis • Housing • Infrastructure
O19International Linkages to Development • Role of International Organizations
 
O2Development Planning and Policy
O20General
O21Planning Models • Planning Policy
O22Project Analysis
O23Fiscal and Monetary Policy in Development
O24Trade Policy • Factor Movement Policy • Foreign Exchange Policy
O25Industrial Policy
O29Other
 
O3Innovation • Research and Development • Technological Change • Intellectual Property Rights
O30General
O31Innovation and Invention: Processes and Incentives
O32Management of Technological Innovation and R&D
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O35Social Innovation
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O40General
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O49Other
 
O5Economywide Country Studies
O50General
O51U.S. • Canada
O52Europe
O53Asia including Middle East
O54Latin America • Caribbean
O55Africa
O56Oceania
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P. Political Economy and Comparative Economic Systems

 
P00General
 
P1Capitalist Economies
P10General
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P12Capitalist Enterprises
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P14Property Rights
P16Capitalist Institutions • Welfare State
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P2Socialist and Transition Economies
P20General
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P25Urban, Rural, and Regional Economics
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P3Socialist Institutions and Their Transitions
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P32Collectives • Communes • Agriculture
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P39Other
 
P4Other Economic Systems
P40General
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P47Performance and Prospects
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P49Other
 
P5Comparative Economic Systems
P50General
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P52Comparative Studies of Particular Economies
P59Other

Q. Agricultural and Natural Resource Economics • Environmental and Ecological Economics

 
Q00General
Q01Sustainable Development
Q02Commodity Markets
 
Q1Agriculture
Q10General
Q11Aggregate Supply and Demand Analysis • Prices
Q12Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
Q13Agricultural Markets and Marketing • Cooperatives • Agribusiness
Q14Agricultural Finance
Q15Land Ownership and Tenure • Land Reform • Land Use • Irrigation • Agriculture and Environment
Q16R&D • Agricultural Technology • Biofuels • Agricultural Extension Services
Q17Agriculture in International Trade
Q18Agricultural Policy • Food Policy • Animal Welfare Policy
Q19Other
 
Q2Renewable Resources and Conservation
Q20General
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Q22Fishery • Aquaculture
Q23Forestry
Q24Land
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Q4Energy
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Q42Alternative Energy Sources
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Q47Energy Forecasting
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Q5Environmental Economics
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Q52Pollution Control Adoption and Costs • Distributional Effects • Employment Effects
Q53Air Pollution • Water Pollution • Noise • Hazardous Waste • Solid Waste • Recycling
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Q55Technological Innovation
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Q57Ecological Economics: Ecosystem Services • Biodiversity Conservation • Bioeconomics • Industrial Ecology
Q58Government Policy
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R. Urban, Rural, Regional, Real Estate, and Transportation Economics

 
R00General
 
R1General Regional Economics
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R19Other
 
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R28Government Policy
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R5Regional Government Analysis
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R58Regional Development Planning and Policy
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Y. Miscellaneous Categories

 
Y1Data: Tables and Charts
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Y2Introductory Material
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Y3Book Reviews (unclassified)
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Numeraire (NMR) and Machine Learning: Revolutionizing Financial Prediction

 In today’s rapidly evolving financial landscape, maintaining a competitive edge is paramount for achieving success. The ongoing advancements in technology have ushered in a transformative era, with the integration of machine learning into financial prediction standing out as a significant game-changer. Numeraire (NMR), a cryptocurrency, leads this financial revolution by pioneering innovative approaches to predictive analytics. This article aims to provide an in-depth exploration of how Numeraire and the power of machine learning are reshaping the financial industry, offering traders and investors invaluable data-driven insights to enhance their decision-making processes and achieve more informed financial outcomes. For those seeking a platform to navigate the online trading landscape, consider exploring immediate-growth.com. Their resources and insights can provide a deeper understanding of topics like Numeraire (NMR) and the role of machine learning in financial predictions.

Photo by Worldspectrum on Pexels.com

The Rise of Numeraire (NMR)

What is Numeraire (NMR)?

Numeraire (NMR) is a unique and groundbreaking cryptocurrency introduced in 2017. Created by Numerai, a hedge fund based in San Francisco, NMR serves as a utility token that incentivizes data scientists to participate in their machine learning competition. The competition allows data scientists from around the world to develop predictive models on financial data.

How does the Numeraire Competition Work?

Numerai releases encrypted financial data to data scientists who compete to create the most accurate predictive models. Unlike traditional data competitions, Numerai does not know the real identities of its participants, fostering a trustless and decentralized environment. Participants use NMR to stake their predictions, and if their models perform well, they are rewarded with additional NMR tokens. This unique structure aligns the interests of data scientists with those of the hedge fund, creating a symbiotic relationship between the two.

Machine Learning and Its Role in Financial Prediction

Machine learning has transformed numerous industries, and the financial sector is no exception. Its ability to analyze vast amounts of data and identify patterns enables more accurate predictions. Financial institutions are increasingly integrating machine learning algorithms into their decision-making processes, and the results are promising.

Data-Driven Insights

Machine learning models can analyze historical market data, economic indicators, and even social sentiment to generate insights and predictions. These data-driven insights provide a significant advantage to traders and investors, allowing them to make well-informed decisions.

Risk Management

Managing risk is a critical aspect of financial trading and investment. Machine learning algorithms can assess risk more effectively than traditional methods, identifying potential pitfalls and mitigating losses.

Trading Algorithms

Automated trading algorithms driven by machine learning are gaining popularity. These algorithms can execute trades at lightning speed, reacting to market changes and opportunities instantly. They eliminate human emotions from the trading equation, leading to more rational and disciplined decision-making.

The Synergy of Numeraire and Machine Learning

Empowering Data Scientists

Numeraire’s unique approach empowers data scientists to build better predictive models. By providing them with encrypted financial data and rewarding successful predictions with NMR tokens, Numerai attracts top talent from around the world. The competition cultivates a community of data-driven enthusiasts who collaborate and push the boundaries of financial prediction.

Enhanced Accuracy and Performance

Combining machine learning with the Numeraire competition creates a dynamic environment where participants continuously improve their models. This leads to enhanced prediction accuracy over time. As the pool of talent and data grows, the predictions become more robust, enabling better financial decision-making.

Democratizing Financial Prediction

Numeraire and machine learning have the potential to democratize financial prediction. Traditionally, sophisticated financial forecasting tools were limited to large institutions with substantial resources. However, Numeraire’s decentralized model opens the door for anyone with data science expertise to contribute and be rewarded for their skills.

Real-World Applications

Asset Management

The integration of Numeraire and machine learning has significant implications for asset management firms. Hedge funds, mutual funds, and other investment institutions can leverage these technologies to generate alpha and improve portfolio performance.

Quantitative Trading

Quantitative trading, or algorithmic trading, relies heavily on data and mathematical models to identify trading opportunities. Numeraire’s competition and machine learning algorithms can enhance quantitative trading strategies, making them more effective and profitable.

Risk Assessment and Fraud Detection

The financial industry faces various risks, including credit risk, market risk, and fraud. Machine learning models can analyze historical data and patterns to assess risks accurately and detect fraudulent activities in real-time.

Conclusion

Numeraire (NMR) and machine learning are a formidable duo that is reshaping the financial landscape. The integration of these technologies empowers data scientists, improves prediction accuracy, and democratizes financial forecasting. As the financial industry continues to evolve, embracing innovation will be crucial for staying competitive. Numeraire and machine learning offer a glimpse into the future of finance, where data-driven insights drive smart decision-making, and the boundaries of possibility are continually pushed.

WOMEN EMPOWERMENT

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Empowerment stands for giving authority and power to women. Thus, Women’s empowerment refers to empowering women to make their own decisions. It means women should have full equality across all fields, regardless of stereotypes. With higher literacy rates and equal pay for equal work, women can thrive economically and rise out of poverty. Protecting women and girls from violence and abuse while challenging the stigmas against reporting crimes would overall create a much safer society.

The Current State of Gender Equality:

On the World Economic Forum’s Global Gender Gap Index of 2021, India ranks 140th among 153 nations, “becoming the third-worst performer in South Asia.” India fell 28 places from its 2020 rank of 112th. The report cites several reasons for this fall. In terms of political empowerment, the number of female ministers declined from about 23% in 2019 to just 9% in 2021. The female workforce participation rate also decreased “from 24.8% to 22.3%.”
Additionally, the “share of women in senior and managerial positions also remains low.” The report also indicates that women in India earn just one-fifth of what men earn.

Furthermore, “one in four women” endure “intimate violence” at least once in their lifetime. Although India has achieved gender parity in educational attainment, illiteracy rates among women remain high. The report indicates that just 65.8% of women in India are literate in 2021 in comparison to 82.4% of men.


Women also endure inequality concerning land and property rights. A 2016 UNICEF report noted that only 12.7% of properties in India “are in the names of women” despite 77% of women in India depending on agricultural work as a core source of income.

Benefits of Empowering Women in India:

As the majority of India’s population, women represent a significant portion of the nation’s untapped economic potential. As such, empowering women in India through equal opportunities would allow them to contribute to the economy as productive citizens. With higher literacy rates and equal pay for equal work, women can thrive economically and rise out of poverty.

Protecting women and girls from violence and abuse while challenging the stigmas against reporting crimes would overall create a much safer society. Improving the female political representation rate would enable more women to serve as role models for young girls and allow a platform to bring awareness to the issues affecting women in India. Overall, gender equality allows for women to live a better quality of life, allowing them to determine their futures beyond traditional expectations.

Women Of Worth (WOW):

According to its website, “Women Of Worth exists for the growth, empowerment, and safety of girls and women” standing “for justice, equality and change.” WOW began in 2008, created by a group of women who longed for change in a society rife with gender discriminatory practices. Its ultimate vision is “to see women and girls live up to their fullest potential.” With a mission of empowering women in India.

The organization has three focal areas:

1. Advocacy Work: WOW utilizes social media platforms to raise awareness of gender inequality and “change attitudes and behavior.”


2. Training and Health Services: WOW provides training to both men and women in schools, tertiary institutions, and companies on women’s safety and rights. It also presents lectures and “keynote addresses” on the topic. Furthermore, WOW provides counseling sessions to improve mental health.
Rehabilitation and Restoration: WOW offers “counseling, life skills training, and therapy” to children and women who are victims of abuse, neglect, and trafficking.


WOW’s efforts have seen success. The organization helped to rescue 200 girls from abusive backgrounds, providing them with rehabilitation services. WOW also gave 11 girls scholarships to continue their education. WOW provided training on gender equality to about 800 working people and “1500 students” along with “200 parents” and 300 educators.


3. Gender equality is a crucial cornerstone in the advancement of any society or nation as it affects all areas of society from economic growth to education, health, and quality of life. Gender inequality in India is a deep-rooted, complex, and multi-layered issue but it is also an essential battle to overcome to see the fullest potential of the nation.

How are women empowered in India?

The Constitution of India has certain provisions that specifically focus on women’s empowerment and prevents discrimination against women in society. Article 14 talks about equality before the law. Article 15 enables the state to make special provisions for women.

Beti Bachao Beti Padhao Andolan has been launched for creating awareness among the people to educate all girl children in the country. The government successfully promotes this scheme by forming District Task Force and Block Task Force. The scheme was launched in the Panipat district of Haryana on 22 January 2015 with initial funding of Rs. 100 crore. Before the launching of this scheme, the Child Sex Ratio of Panipat was 808 in 2001 and 837 in 2011.
Massive publicity is made about the program in print and electronic media, and the logo of this scheme is very common in government buildings such as pillars of National Highway 44, Panipat District Court, bus stand, and railway station of Panipat district, etc.

Financial independence is important for women’s empowerment. Women, who are educated and earning, are in a much better position in our society as compared to uneducated women workers. Therefore, a scheme called working women hostels has been launched so that safe and convenient accommodation should be provided to working women. The benefit of this scheme is given to every working woman without any distinction of caste, religion, marital status, etc. To take benefit from this scheme, the gross total income of women should not exceed Rs. 50,000 per month in the case of metropolitan cities whereas, in the case of small cities, the gross total income should not exceed Rs. 35,000 per month.

The focus of the government has shifted from women’s development to women-led development. To achieve this goal, the government is working around the clock to maximize women’s access to education, skill training, and institutional credit. MUDRA Yojana ( Micro Units Development and Refinance Agency Ltd ) is one such scheme that was launched on 8 April 2015 in which loans up to Rs. 10 lakh are provided to women entrepreneurs, without any collateral. For instance: A woman namely Kamla daily wage laborer from Panipat has taken a loan of Rs. 45,000 from the State Bank of India to start work in a beauty parlor and she is engaged in gainful employment with dignity now.

Conclusion:

Women must have an equal voice, rights, and opportunities, throughout their lives. Gender equality can make a difference to individual lives and whole communities. Economic and Social Empowerment places women and girls in a stronger position. Women’s and girls Economic Empowerment gives a voice in decison making processes. women also should be given equal rights like men to actually empower them. They need to be strong, aware, and alert every time for their growth and development. The most common challenges are related to the education, poverty, health, and safety of women.

Radio In India

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Radio broadcasting began in India in 1922. The Government owned radio station All India Radio dominated broadcasting since 1936.

Broadcasting in India actually began about 13 years before AIR came into existence. In June 1923 the Radio Club of Bombay made the first ever broadcast in the country. This was followed by the setting up of the Calcutta Radio Club five months later. The Indian Broadcasting Company (IBC) came into being on July 23, 1927, only to face liquidation in less than three years.

In April 1930, the Indian Broadcasting Service, under the Department of Industries and Labour, commenced its operations on an experimental basis. Lionel Fielden was appointed the first Controller of Broadcasting in August 1935. In the following month Akashvani Mysore, a private radio station was set up. On June 8, 1936, the Indian State Broadcasting Service became All India Radio.

The Central News Organisation (CNO) came into existence in August, 1937. In the same year, AIR came under the Department of Communications and four years later came under the Department of Information and Broadcasting. When India attained independence, there were six radio stations in India, at Delhi, Bombay, Calcutta, Madras, Tiruchirapalli and Lucknow. The following year, CNO was split up into two divisions, the News Services Division (NSD) and the External Services Division (ESD). In 1956 the name AKASHVANI was adopted for the National Broadcaster. The Vividh Bharati Service was launched in 1957 with popular film music as its main component

The phenomenal growth achieved by All India Radio has made it one of the largest media organisations in the world. With a network of 262 radio stations, AIR today is accessible to almost the entire population of the country and nearly 92% of the total area. A broadcasting giant, AIR today broadcasts in 23 languages and 146 dialects catering to a vast spectrum of socio-economically and culturally diverse populace.

Programmes of the External Services Division are broadcast in 11 Indian and 16 foreign languages reaching out to more than 100 countries. These external broadcasts aim to keep the overseas listeners informed about developments in the country and provide a rich fare of entertainment as well.

The News Services Division, of All India Radio broadcasts 647 bulletins daily for a total duration of nearly 56 hours in about 90 Languages/Dialects in Home, Regional, External and DTH Services. 314 news headlines on hourly basis are also being mounted on FM mode from 41 AIR Stations. 44 Regional News Units originate 469 daily news bulletins in 75 languages. In addition to the daily news bulletins, the News Services Division also mounts number of news-based programmes on topical subjects from Delhi and its Regional News Units

AIR operates at present 18 FM stereo channels, called AIR FM Rainbow, targeting the urban audience in a refreshing style of presentation. Four more FM channels called, AIR FM Gold, broadcast composite news and entertainment programmes from Delhi, Kolkata, Chennai and Mumbai. With the FM wave sweeping the country, AIR is augmenting its Medium Wave transmission with additional FM transmitters at Regional stations.

In April 2020, as per a survey by AZ Research PPL, commissioned by the Association of Radio Operators for India (AORI) Radio listenership in India touched a peak of 51 million.

Does radio have a future?

The consoles, connected watches and TV’s that we use every day will be just another way in which radio stations can broadcast and increase their audience numbers. Since its creation, radio has continually evolved with the times

Why Radio is still popular?

Portable and Inexpensive: Radio is portable among many modes of communication. They can be used in cars, stores, and other places, which helps to reach the targeted audience. According to researchers, broadcast radio reaches 99% of the Indian population today.

The Government decision for transition to the digital mode of transmission, AIR is switching from analog to digital in a phased manner. The technology adopted is the Digital Radio Mondiale or DRM. With the target of complete digitization by 2017, the listeners can look forward to highly enhanced transmission quality in the near future.

MICROPHONES

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A microphone is a device that translates sound vibrations in the air into electronic signals and scribes them to a recording medium or over a loudspeaker. Microphones enable many types of audio recording devices for purposes including communications of many kinds, as well as music vocals, speech and sound recording.

Types Of Microphone

There are three main types microphones based on construction -:

1. Dynamic/Moving coil. 2. Ribbon. 3. Condenser/ capacitor

1. Dynamic / Moving coil

A microphone in which the sound waves cause a movable wire or coil to vibrate in a magnetic field and thus induce a current.

Key Advantages -:

1. Rugged and able to handle high sound pressure levels, like those delivered by a kick drum.
2. Provide good sound quality in all areas of microphone performance.
3. They do not require a power source to run
4. They are relatively cheap

Key disadvantages -:

Heavy microphone diaphragm and wire coil limits the movement of the assembly, which in turn restricts the frequency and transient response of the microphone
Generally not as suitable as condenser microphones for recording instruments with higher frequencies and harmonics, such as a violin.

Dynamic microphones can be used for many applications, produce an excellent sound and are suitably rugged – great for traveling on the road. They are best avoided when recording high-frequency content on an important recording.

For reliable, everyday tasks you will not find a more multifaceted, trustworthy device than a good quality dynamic microphone.

Ribbon -:

A ribbon microphone, also known as a ribbon velocity microphone, is a type of microphone that uses a thin aluminum, duraluminum or nanofilm of electrically conductive ribbon placed between the poles of a magnet to produce a voltage by electromagnetic induction. Ribbon microphones are typically bidirectional, meaning that they pick up sounds equally well from either side of the microphone

Key Adavantages -:

1. Ribbon Microphones are very sensitive and accurate
2. Ribbon microphones have a very low noise
3. Ribbon microphones tend not to pick up lots of background noise
4. Ribbon microphones can be very expensive
5. Ribbon microphones are good to produce a thin and tinny sound

Key disadvantages -:

1. Ribbon microphones can be very large and heavy
2. Ribbon microphones are very sensitive to air movements
3. It is very difficult to achieve a tight polar pattern
4. The ribbon is fragile and susceptible to damage
5. Ribbon microphones are not as popular as dynamic microphones
Ribbon microphones require more maintenance

Ribbon microphones are often described as the most natural-sounding microphones available, and for good reason: they condenser microphones that use a thin ribbon of aluminum foil to pick up sound (instead of a solid diaphragm).

Condenser/ Capacitor Microphones -:

A Condenser capsule is constructed similarly. It consists of a thin membrane in close proximity to a solid metal plate. The membrane or diaphragm, as it is often called, must be electrically conductive, at least on its surface. The most common material is gold-sputtered mylar, but some (mostly older) models employ an extremely thin metal foil.

When sound waves hit the diaphragm, it moves back and forth relative to the solid backplate. In other words, the distance between the two capacitor plates changes. As a result, the capacitance changes to the rhythm of the sound waves. Thus, converted sound into an electrical signal.

Key Adavantages -:

1. They have a Greater Dynamic Range than Ribbon or Dynamic Mics.
2. They Have a Better Frequency Response than Dynamic Mics.
3.They Have a Better Noise Floor than Dynamic or Ribbon Mics.
4. When Hit with Loud Transients, They Generally Sound Snappier than Dynamic or Ribbon Mics.

Key Disadvantages -:

1. The limited number of operating microphones at the same time and place.
2. The limited number of radio channels.
Sound files can use up a lot of computer memory in a device.
3. Voice recognition system software is not as accurate as typing manually.

Condenser microphones are best used to capture vocals and high frequencies. They are also the preferred type of microphone for most studio applications.

Conclusion -:

Microphones are used everywhere, from stage performances, broadcasting, and even talking on the phone. The microphone is a transducer, a machine that changes one form energy to another form of energy. Microphones are an essential part of any audio recording system.

Bitcoin The Future?

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Bitcoin is a type of digital currency that enables instant payments to anyone. Bitcoin was introduced in 2009. Bitcoin is based on an open-source protocol and is not issued by any central authority. It is an electronic currency created back in January 2009. It is known to be decentralized electronic cash that does not rely on banks. It is possible to send from one user to another on the bitcoin blockchain network without the necessity for mediators. It is primarily used for sending or receiving cash through the internet even to strangers. Bitcoin is also known to be a new type of cash. It is predicted to grow at a rapid pace over the years, along with its value. It is typically purchased as an investment by numerous industries and people.


The central government typically handles bitcoins without specific rules, unlike dollars and euros. It is not owned by a country, individual, or group. Therefore, it reduces the chances of corruption and inflation.

History -:

The origin of Bitcoin is unclear, as is who founded it. A person, or a group of people, who went by the identity of Satoshi Nakamoto are said to have conceptualized an accounting system in the aftermath of the 2008 financial crisis.

Uses -:

1. Originally, Bitcoin was intended to provide an alternative to fiat money and become a universally accepted medium of exchange directly between two involved parties.
2. Fiat money is a government-issued currency that is not backed by a commodity such as gold.
3. It gives central banks greater control over the economy because they can control how much money is printed.
4. Most modern paper currencies, such as the US dollar and Indian Rupee are fiat currencies

Acquiring Bitcoins -:

1. One can either mine a new Bitcoin if they have the computing capacity, purchase them via exchanges, or acquire them in over-the-counter, person-to-person transactions.
2. Miners are the people who validate a Bitcoin transaction and secure the network with their hardware.
3. The Bitcoin protocol is designed in such a way that new Bitcoins are created at a fixed rate.
4. No developer has the power to manipulate the system to increase its profits.
5. One unique aspect of Bitcoin is that only 21 million units will ever be created.
6. A Bitcoin exchange functions like a bank where a person buys and sells Bitcoins with traditional currency. Depending on the demand and supply, the price of a Bitcoin keeps fluctuating.

Bitcoin Regulation -:

The supply of bitcoins is regulated by software and the agreement of users of the system and cannot be manipulated by any government, bank, organization, or individual.Bitcoin was intended to come across as a global decentralised currency, any central authority regulating it would effectively defeat that purpose.It needs to be noted that multiple governments across the world are investing in developing Central Bank Digital Currencies (CBDCs), which are digital versions of national currencies.
The legitimacy of Bitcoins (or cryptocurrencies)

In India -:
In the 2018-19 budget speech, the Finance Minister announced that the government does not consider cryptocurrencies as legal tender and will take all measures to eliminate their use in financing illegitimate activities or as a part of the payment system.
In April 2018, the Reserve Bank of India (RBI) notified that entities regulated by it should not deal in virtual currencies or provide services for facilitating any person or entity in dealing with or settling virtual currencies.
However, the Supreme Court struck down the ban on the trading of virtual currencies (VC) in India, which was imposed by the RBI.
The Supreme Court has held that cryptocurrencies are like commodities and hence they can not be banned.

Possible Reasons for the Rise in the Value of the Bitcoin -:

1. Increased acceptance during the pandemic.
2. Global legitimacy from large players like payments firm PayPal, and Indian lenders like State Bank of India, ICICI Bank, HDFC Bank, and Yes Bank.
3. Some pension funds and insurance funds are investing in Bitcoins.

Bitcoin Transaction -:

Bitcoin address is built from the public key. It is very similar as compared to an email address, anyone can check up and provide bitcoins. The private key is known to be identical to that of an email password since it is possible to send bitcoins with the help of remote access only. That’s why it is essential to keep the private key confidential or hidden. To send bitcoins, it is required to verify to the network that you acquire the private key of that particular address without the private key being revealed. It can be done with a specific mathematics branch referred to as public-key cryptography. The identification of the user possessing bitcoins is known as a public key. The public access and the ID number are very alike. For an individual to send you bitcoins, they require your bitcoin address. It is known to be another version of the public key that can be typed and read effortlessly.

However, the security concern of bitcoin is increasing day by day across the world. Since digital wallets are used to store bitcoins, they might be targeted by hackers as their value increases.

YOUTUBE MARKETING

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YouTube Marketing is the practice of promoting businesses and products on YouTube’s platform, by uploading valuable videos on a company’s YouTube channel or using YouTube ads. more and more companies are including YouTube as part of their digital marketing strategy.

That’s partly because YouTube as a platform is growing insanely fast. But it’s also because the video is an extremely powerful medium.

The truth about youtube marketing -:

YouTube is an opportunity to get more traffic and customers. YouTube can be a very competitive place. This means you can’t just start uploading videos and expect to see results. Countless “big brands” have jumped into YouTube marketing head-first… with only a handful of views and subscribers to show for it.

The truth is, to succeed on YouTube, you need to have a winning strategy, the ability to create great videos, and the SEO know-how to optimize those videos around keywords and topics that people on YouTube care about.

Why youtube is considered a major market for advertising – :

1. YouTube is the 2nd most-visited website in the world.
2. 2 billion people log in to YouTube every month.
3. 68% of YouTube users state that videos help them make a “purchasing decision”.
4. The number of SMBs advertising on YouTube has doubled over the last 2 years.

What is the main goal of YouTube marketing?

One of your objectives for your YouTube marketing should be to help your customer find you. A catchy slogan or prominent company name throughout the video can keep you on people’s minds long after they’ve seen your message online. They can then do an online search and find you.

Objectives of Youtube marketing –:

YouTube videos should have clear objectives that align with your company goals. focus all of your efforts on gimmicks that will get the attention of viewers and help your video go viral, you may overlook the reason you market on YouTube to get more business. Make sure your attention-getting videos help you move toward your company objectives.

1. Reaching Your Target Customer -:

If your target demographic is women between the ages of 35 and 45, and your video catches on with teenagers, you may be popular, but you won’t be effective. Think about the kinds of images and messages that would appeal to your customer, and make it one of your objectives to use as many of those images as possible.

2. Making It Easy to Find You -:

One of your objectives for your YouTube marketing should be to help your customer find you. A catchy slogan or prominent company name throughout the video can keep you on people’s minds long after they’ve seen your message online. They can then do an online search and find you. You should include a link to your website, along with any other contact information, such as an email address, business address, or phone number. Don’t lose sight of your objective of helping customers contact you.

3. Establishing a Relationship -:

You should evaluate the relationship you want with your customers, and create a video that helps them feel you are one of them. You can convey a sense of trust, lightheartedness, sophistication, down-to-earth values, or even anger, to name a few relationship starters.

4. Keeping Your Product in Mind -:

Don’t get so involved with making an interesting video that you lose sight of your number-one objective: letting people know about your product or service. Feature your product prominently and clearly, so that viewers won’t have to wonder what you are marketing.

Importance of YouTube to Business -:

1. Advertising -: the largest video-sharing website on the Internet, according to NBC, YouTube also doubles as one of the largest video search engines in the world
2. Customer Communication -: YouTube provides an array of channels for businesses to communicate with customers and prospects.
3. Internal Communication -: YouTube provides a convenient and easy-to-use video hosting service, it can serve as an inexpensive way to post instructional videos, announcements, and other internal communications.
4. Complaints –: As a business owner, you should carefully monitor YouTube for customer feedback and complaints.
5. Considerations -: YouTube can offer numerous important benefits to businesses, but you should keep some considerations in mind when using this resource.

Advantages of YouTube Marketing –:

1. Heavy Traffic
2. Higher Visibility on Google
3. Build Your Email List on YouTube
4. Higher Conversion Rates
5. Multiple Video Types
6. Massive Media Library

Disadvantages of Youtube Marketing –:

1. Control
2. Targeting
3. Ad Bypass
4. Auctions
5. Sales Conversion

YouTube provides every business with an insane opportunity to get more traffic and customers. However, it is also a very competitive place as well. This means that you can’t just start uploading videos and expect to see results overnight. Many big businesses jump into YouTube marketing with no strategy – their lack of views and subscribers show for it. The truth is that to succeed on YouTube is not just about creating great videos. It’s knowing how to optimize those videos around keywords that people on YouTube are searching for.

DEPRESSION

N kavya

Depression is a mood disorder that causes a persistent feeling of sadness and loss of interest, also called major depressive disorder or clinical depression. It affects how you feel, think, and behave and can lead to a variety of emotional and physical problems. Depression is not a weakness; you cannot simply “snap out “of it. Depression may require long-term treatment. But we should not feel discouraged because most people with depression feel better with medication, psychotherapy, or both.

Let us see know about the symptoms of depression –:

• Feelings of sadness, tearfulness, emptiness, or hopelessness
• Angry outbursts, irritability or frustration, even over small matters
• Loss of interest or pleasure in most or all normal activities, in their hobbies or sports
• Sleep disturbances, including insomnia or sleeping too much
• Tiredness and lack of energy, so even small tasks take extra effort
• Reduced appetite and weight loss or increased cravings for food and weight gain
• Anxiety, agitation, or restlessness
• Slowed thinking, speaking, or body movements
• Feelings of worthlessness or guilt fixating on past failures or self-blame
• Trouble thinking, concentrating, making decisions, and remembering things
• Frequent or recurrent thoughts of death, suicidal thoughts, suicide attempts, or suicide
• Unexplained physical problems, such as back pain or headaches.

People dealing with depression may occur only once during their life, people typically have multiple episodes, and during these episodes, symptoms occur most of the day, nearly every day which also affects their day-to-day activities, such as work, school, social activities, or relationships with others. Some people might even feel generally miserable without really knowing the exact reason.

• Depression in children and teens may include sadness, irritability, clinginess, worry, aches, pains, being extremely sensitive, feeling misunderstood, anger, and poor performance.
• Depression in symptoms in older adults may include memory, difficulties or personality changes, fatigue, and often wanting to stay at home, rather than go out to socialize or do new things.

Causes of depression –:

• Biological differences – People with depression appear to have physical changes in their brains. The significance of these changes is still uncertain.
• Brain chemistry – Neurotransmitters are naturally occurring brain chemicals that likely play a role in depression.
• Hormones – Changes in the body’s balance of hormones may be involved in causing or triggering depression.
• Inherited traits – Depression is more common in people whose blood relatives also have this condition. Research shows genes may be involved in causing depression.

Risk factors of depression –:

• Certain personality traits, such as low self-esteem and being too dependent, self-critical, or pessimistic
• Traumatic or stressful events, such as physical or sexual abuse, the death or loss of a loved one, a difficult relationship, or financial problems.
• History of other mental health disorders, such as anxiety disorder, eating disorders, or post-traumatic stress disorder. Abuse of alcohol or recreational drugs.
• Serious or chronic illness, including cancer, stroke, chronic pain, or heart disease. Certain medications may also trigger depression such as some high blood pressure medications or sleeping pills.

Complications in depression – :

• Excess weight or obesity, which can lead to heart disease and diabetes
• Pain or physical illness
• Alcohol or drug misuse
• Anxiety, panic disorder, or social phobia
• Family conflicts, relationship difficulties, and work or school problems
• Social isolation
• Suicidal feelings, suicide attempts, or suicide
• Self-mutation, such as cutting
• Premature death from medical conditions

Prevention of depression -:

There is no fixed way to prevent depression but these strategies may play a major role –
• Take steps to control stress
• Reach out to family and friends
• Get treatment at the earliest sign of a problem
• Consider getting long–term treatment because it helps to prevent a relapse of symptoms.

Types of depressive disorders -:

• Major depressive disorder
• Anxious distress, Melancholy, Agitated (Major depression looks different in different people. So they are characterized into three types.)
• Persistent depressive disorder
• Bipolar disorder
• Seasonal affective disorder (SAD)
• Psychotic disorder
• Peripartum (Postpartum) Depression
• Premenstrual Dysphoric Disorder
• ‘Situational ’Depression
• Atypical depression
• Clinical depression

General issues on Environmental ecology

The environment plays a significant role to support life on earth. But there are some issues that are causing damages to life and the ecosystem of the earth. It is related to the not only environment but with everyone that lives on the planet. Besides, its main source is pollution, global warming, greenhouse gas, and many others. The everyday activities of human are constantly degrading the quality of the environment which ultimately results in the loss of survival condition from the earth.There are hundreds of issue that causing damage to the environment. But in this, we are going to discuss the main causes of environmental issues because they are very dangerous to life and the ecosystem.

Pollution – It is one of the main causes of an environmental issue because it poisons the air, water, soil, and noise. As we know that in the past few decades the numbers of industries have rapidly increased. Moreover, these industries discharge their untreated waste into the water bodies, on soil, and in air. Most of these wastes contain harmful and poisonous materials that spread very easily because of the movement of water bodies and wind. Greenhouse Gases – These are the gases which are responsible for the increase in the temperature of the earth surface. This gases directly relates to air pollution because of the pollution produced by the vehicle and factories which contains a toxic chemical that harms the life and environment of earth. Climate Changes – Due to environmental issue the climate is changing rapidly and things like smog, acid rains are getting common. Also, the number of natural calamities is also increasing and almost every year there is flood, famine, drought, landslides, earthquakes, and many more calamities are increasing.

Development recognises that social, economic and environmental issues are interconnected, and that decisions must incorporate each of these aspects if there are to be good decisions in the longer term.For sustainable development, accurate environment forecasts and warnings with effective information on pollution which are essential for planning and for ensuring safe and environmentally sound socio-economic activities should be made known.


THE EARTH IS WHAT WE
ALL HAVE IN COMMAN

Basic charts for Data Analysis – Data Visualization in R

Data visualization helps to understand large chunks of data with pictorial representation. R is an amazing tool for data visualization. R provides a wide range of charts for data visualizing data. R is a language that is designed for computing statistical problems, graphical data analysis, and scientific research.

Basic charts for visualization we shall discuss :

  • Bar Plot
  • Histogram
  • Scatter Plot
  • Box Plot
  • Line Chart

Bar plot

Barplot is used to visualize the relative or absolute frequencies of observed values of a variable. The frequency of the observation is the count of that observation in the data. They are used for continuous and categorical variable plotting.

#Lets suppose we have a monthly sales data
sales <- c(120,123,117,130,110,80,130,112,120,111,125,140)
barplot(sales,main = "Monthly sales",xlab = "Month",ylab="sales",
  names.arg = c("Jan","Feb","Mar","Apr","May","Jun","July",
                "Aug","Sept","Oct","Nov","Dec"),col="green")
barplot(sales,main = "Monthly sales",xlab = "Month",ylab="sales",
  names.arg = c("Jan","Feb","Mar","Apr","May","Jun","July",
                "Aug","Sept","Oct","Nov","Dec"),col="green",horiz = TRUE)

Histogram

The histogram is used for data that is classified into different groups. The histogram has a similar appearance to the vertical bar chart but there are no gaps between the bars.

Generally, it is used to display the distribution of numerical variables.

salary <- c(120,123,117,130,110,98,80,130,112,120,89,111,130,125,140)
hist(salary,col="light blue",border = "blue",main ="Histogram of Salary")

Scatter Plot

A scatter plot is used to identify relationships between two numerical variables. Each dot represents a pair of observations (x,y). They are commonly used to find correlational relationships between variables.

x = c(6,13,9,17,12,8,11,18,5,12,7,9,7,17,14,9,8,10,3,12)
y = c(15,8,11,12,10,15,11,10,8,14,11,13,10,9,10,12,13,12,10,10)
plot(x,y,main = "Scatter plot",col="blue",pch=19,frame=F)

Another way of making a scatterplot using the “‘car” package:

library(car)
x = c(6,13,9,17,12,8,11,18,5,12,7,9,7,17,14,9,8,10,3,12)
y = c(15,8,11,12,10,15,11,10,8,14,11,13,10,9,10,12,13,12,10,10)
scatterplot(x~y)
The graph shows marginal box plots, a regression line (solid blue line), the smoothed conditional spread (blue region), the non-parametric regression smooth (blue dashed line)

Boxplot

Boxplot is very useful in finding outliers in the data. Boxplot gives you a good representation of quartiles, mean, median, skewness, and spread of the data. Underlying distribution can be identified using a boxplot.

Different parts of a boxplot.
# boxplot() to create a boxplot for ozone in airquality dataframe.
# Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
df = airquality
boxplot(df$Ozone)
Points at the extreme are outliers.
# Notch can be added by setting the notch parameter "notch = TRUE"
# Alignment can be change by setting horizantal parameter
boxplot(df$Ozone,main="Mean ozone",xlab="Parts per billion",ylab="ozone",col="yellow",
        border="brown",notch = TRUE,horizontal = TRUE)

Line Chart

A line chart is a type of chart that represents information in a series of data points connected by a straight line segment. It is used to show a change in continuous variables over a time span. It is widely used for sales, share price analysis, weather recordings, etc.

#Simple Line chart
a = c(119,119,110,112,114,113,118,109,130,136)
plot(a,col="red",type="l",main = "Simple Line chart")
# Multiple Lines Chart
a = c(119,119,110,112,114,113,118,109,130,136)
b = c(132,126,113,115,120,111,136,121,122,116)
plot(a,col="red",type="l",main = "Multiple Line chart")
lines(b,col="blue")
legend(2,135,legend=c("a","b"),col=c("red","blue"),lty = 1,cex=0.8)

These are few of the many charts used in data analysis. R provides more than 400 different charts for data visualization.To explore more such blogs about data visualization follow us at EDUINDEX.

New NASA Earth System Observatory to Help Address, Mitigate Climate Change

May 24, 2021

NASA will design a new set of Earth-focused missions to provide key information to guide efforts related to climate change, disaster mitigation, fighting forest fires, and improving real-time agricultural processes. With the Earth System Observatory, each satellite will be uniquely designed to complement the others, working in tandem to create a 3D, holistic view of Earth, from bedrock to atmosphere.



“I’ve seen firsthand the impact of hurricanes made more intense and destructive by climate change, like Maria and Irma. The Biden-Harris Administration’s response to climate change matches the magnitude of the threat: a whole of government, all hands-on-deck approach to meet this moment,” said NASA Administrator Sen. Bill Nelson. “Over the past three decades, much of what we’ve learned about the Earth’s changing climate is built on NASA satellite observations and research. NASA’s new Earth System Observatory will expand that work, providing the world with an unprecedented understanding of our Earth’s climate system, arming us with next-generation data critical to mitigating climate change, and protecting our communities in the face of natural disasters

DATA SCIENCE

Introduction:-

Data scientists combine mathematics, statistics and the use of computer science to extract,analyze data from thousands of data sources in order to build creative and innovative business solutions.Data Scientist’s job involves solving the problems of his or her client by providing solutions using real time data and tools and algorithms.

Industries and Departments in which Data Scientist are hired:-

Data scientists and analysts are largely employed by IT companies, marketing, finance and retail sectors.
Companies use Data Scientists to give them a report on what their clients demands and needs and give them innovative solutions on how to cater to them. Oil, gas and telecommunication companies also have started employing data scientists to better cater to their clients.
Other sectors and departments that employ data scientists are
● NHS
● Government offices
● Research institutions and universities.

The roles and responsibilities of a data scientist:-

● To handle vast amounts of data and choose reliable sources.

● Developing prediction models and advanced machine learning algorithms

● Verifying data using data investigation and data analysis.

● Using data visualization techniques to present findings.

● Finding solutions to business problems by working with data engineers and data analysts.

Educational qualification For data scientist:-

● Should have a BSc/BA degree in the field of Computer Science/ Software Engineering/Information Science/Mathematics.


● Should have a postgraduate degree/diploma certification in Data Science/Machine Learning.

Career growth of a Data Scientist:-

The life of a Data Scientist starts from an associate data analyst and can go up to the role of Chief Data Scientist.Promotion can take two to five years it is based on the performance.After some experience they get into some higher position.

CONCLUSION:-

Data Scientists are one of the most in demand people in the world. They can skyrocket companies’ shares and make them reach new heights.Data Science is a very high paying industry thus finding a job with a seven-figure salary won’t be a problem. Data Science as an industry has a very bright future.Data Scientists have the ability to change the world’s future.

Data Scientist Vs Data Analyst

What is the contrast between a Data Scientist and a Data Analyst? Are these two the equivalent? Such inquiries have been a wellspring of extraordinary disarray among youths, who wish to make an effective career in data science. I’m here to settle these inquiries and explain the whole matter for you.

How about we initially comprehend the center distinction between two occupation jobs, Data Scientists are master experts, outfitted with a blend of Coding, Mathematical, Statistical, Analytical and ML abilities. 

Data Analysts are principally associated with the everyday information assortment and investigation undertakings. They should filter through information to distinguish significant experiences from information.

Data Scientist and Data Analyst Comparison

1. Responsibilities

Data Scientist

! Create & define programs for data collection. modelling, analysis, and reporting.

! Perform information cleansing and preparing tasks to mine important bits of knowledge from information. 

! Foster custom information models and ML algorithms to suit organization/client needs.

! To mine and analyse data from company databases to foster optimisation and improvement of business operations. 

! To use the right data visualization and predictive modelling tools to boost revenue generation, marketing strategies, enhance customer experiences, etc.

! To foster new ML techniques and scientific models. 

! To associate diverse datasets, decide the legitimacy of new information sources and information assortment techniques. 

! To facilitate and speak with both IT and business supervisory crews to execute information models and screen the results. 

! To distinguish new business openings and decide how the discoveries can be utilized to upgrade business systems and results.

Data Analyst

! To analyse and mine business data to identify correlations and discover valuable patterns from disparate data points

! To work with customer-centric algorithm models and personalize them to fit individual customer requirements.

! To make and send custom models to reveal answers to business matters, for example, advertising techniques and their presentation client taste, and inclination designs, and so forth.

! To guide and follow information from various frameworks to tackle explicit business issues. 

! To compose SQL inquiries to remove information from the information stockroom and to recognize the responses to complex business issues. 

! To apply statistical analysis to lead purchaser information research and examination.

! To coordinate with Data Scientists and Data Engineers to gather new data from multiple sources.

! To design and develop data visualization, reports, dashboards, to help the business management team to make better business decisions.

! To perform routine analysis tasks as well as quantitative analysis as and when required to support day-to-day business functioning and decision making.

2. Skills

Data Scientist

The role of a Data Scientist is highly specialized and versatile. Henceforth, Data Scientists generally have Advanced Degrees like a Masters or PhD. 

! A least of a Master’s certification in Statistics/Mathematics/Computer Science. 

! Proficiency in programming languages like R, Python, Java, SQL

! In-depth knowledge of ML techniques, including clustering, decision trees, artificial neural networks.

! In-depth knowledge of advanced statistical techniques and concepts.

! Experience in working with statistical and data mining techniques (linear regression, random forest. trees, text mining, social network analysis) 

! Experience in working with as well as creating data architectures.

! Experience in manipulating data sets and developing statistical models.

! Experience in using web services such as $3. Spark, Redshift, Digital Ocean.

Data Analyst

For the work job of a Data Analyst, the base prerequisite is to have an undergraduate stamp. Science, Technology, Engineering or Math Degree, having advanced Degrees is excellent, but it is not a necessity. 

3. Salary

As indicated by a PWC study report, by 2020, there will be around 2.7 million employment opportunities for Data Scientists and Data Analysts. Glassdoor keeps up with that the normal yearly compensation of Data Scientist is Rs.10 lakh, while that of a Data Analyst is Rs.4,82,041. 

With everything taken into account, both the alternatives are arising and exceptionally rewarding profession decisions. So you have a promising profession in Data Science, regardless you pick.

What is Data Science?

As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building a framework and solutions to store data. Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. But how is this different from what statisticians have been doing for years? The answer lies in the difference between explaining and predicting. 

From the above image, it is clear that a Data Analyst usually explains what is going on by processing history of the data. On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.

So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.

  • Predictive causal analytics – If you want a model that can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model that can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
  • Prescriptive analytics: If you want a model that has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
  • Machine learning for making predictions — If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
  • Machine learning for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.

Why Data Science?

Traditionally, the data that we had was mostly structured and small in size, which could be analyzed by using simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. One can understand the precise requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data, you can train models more effectively and recommend the product to your customers with more precision. The self-driving cars collect live data from sensors, including radars, cameras, and lasers to create a map of its surroundings. Based on this data, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn – making use of advanced machine learning algorithms. Data from ships, aircraft, radars, satellites can be collected and analyzed to build models. These models will not only forecast the weather but also help in predicting the occurrence of any natural calamities. It will help you to take appropriate measures beforehand and save many precious lives.

The following infographic shows the various domains in which Data Science is creating its impression:

Role of a Data Scientist

Data scientists are those who crack complex data problems with their strong expertise in certain scientific disciplines. They work with several elements related to mathematics, statistics, computer science, etc (though they may not be an expert in all these fields). They make a lot of use of the latest technologies in finding solutions and reaching conclusions that are crucial for an organization’s growth and development. Data Scientists present the data in a much more useful form as compared to the raw data available to them from structured as well as unstructured forms.

How Data Science is taking over the world

Data Science is nothing but finding patterns within the data. It uses various techniques to draw insights from the data. The goal of a data scientist is to derive useful information from huge amounts of data. In this era of Artificial Intelligence and Big Data, almost 2.5 exabytes of data is transferred daily. The need for data has begun to rise tremendously since the last decade. Many companies have started to do business on data. New sectors have been created in the IT due to data.

What is data?

In computing language, data is nothing but useful information which has been translated into a for which is efficient for movement or processing. Data which is in its beginning stage is referred to as raw data. That is the basic form of any kind of data. In the modern era, almost everything is stored digitally in computers or smart phones in the form of data. Data in computers is represented as binary values, either 0 or 1. Data is usually measured in Bytes, Kilo bytes, mega bytes, etc.

What makes data so precious?

Nowadays, data is stored digitally rather than in physical format. This usually takes less space but requires a bit of money to store. Data stored digitally is considered safe from any kind of damage and attacks except for hackers. The only way data can be stolen is through hacking. As the population is increasing, the amount of data transferred and stored is also increasing.

There needs to be a way to extract necessary, important data from this huge ocean. Normally it will be a very tedious and time consuming task to search the data we need from such huge amounts of data. But data science provides us with a solution to search, extract and organize our data. Data science churns raw data into meaningful insights.

Why is Data Science Important?

Data is the new magic. A data scientist is a wizard who is able to create magic from the data. A professional and skilled data scientist can extract meaningful data from any chunk of raw data he is given. He helps the company in the right direction. The Data Scientist is an expert in various underlying fields of Statistics and Computer Science. He uses his analytical aptitude to solve business problems.

Data Scientist is well versed with problem-solving and is assigned to find patterns in data. His goal is to recognize redundant samples and draw insights from it. Data Science requires a variety of tools to extract information from the data. A Data Scientist is responsible for collecting, storing and maintaining any form of data.

Companies are using Data to analyze their marketing strategies and create better advertisements. Many times, businesses spend an astronomical amount on marketing their products but at times this may also not get them a solution. Therefore, by studying and analyzing customer feedback, companies are able to create better advertisements. The companies do so by carefully analyzing customer behavior online. Also, monitoring customer trends helps the company to get better market insights.

Therefore, businesses need Data Scientists to assist them in making strong decisions with regards to marketing campaigns and advertisements. Thus, data science has taken over the world and is ruling it currently.