Description
Course Name: Diploma in Data Analytics in Economics
Course Id: DDAE/Q1001.
Eligibility: A higher secondary (12th grade) or equivalent is required.
Duration: Six Months.
Objective:The Diploma in Data Analytics in Economics is designed to provide learners with a strong foundation in economic theory, quantitative methods, and data analysis techniques. The course covers statistical tools, econometric modeling, data visualization, and the interpretation of economic data to support policy-making and business decisions.
How to Enroll and Get Certified in Your Chosen Course:
Step 1: Choose the course you wish to get certified in.
Step 2: Click on the “Enroll Now” button.
Step 3: Proceed with the enrollment process.
Step 4: Enter your billing details and continue to course fee payment.
Step 5: You will be redirected to the payment gateway. Pay the course and exam fee using one of the following methods:
Debit/Credit Card, Wallet, Paytm, Net Banking, UPI, or Google Pay.
Step 6: After successful payment, you will receive your study material login ID and password via email within 48 hours of fee payment.
Step 7: Once you complete the course, take the online examination.
Step 8: Upon passing the examination, you will receive:
• A soft copy (scanned) of your certificate via email within 7 days of examination.
• A hard copy (original with official seal and signature) sent to your address within 45 day of declaration of result.
Step 9: After certification, you will be offered job opportunities aligned with your area of interest.
Online Examination Detail:
Duration- 120 minutes.
No. of Questions- 60. (Multiple Choice Questions).
10 Questions from each module, each carry 10 marks.
Maximum Marks- 600, Passing Marks- 40%.
There is no negative marking in this module.
| How Students will be Graded: | ||
| S.No. | Marks | Grade |
| 1 | 91-100 | O (Outstanding) |
| 2 | 81-90 | A+ (Excellent) |
| 3 | 71-80 | A (Very Good) |
| 4 | 61-70 | B (Good) |
| 5 | 51-60 | C (Average) |
| 6 | 41-50 | P (Pass) |
| 7 | 0-40 | F (Fail) |
Key Benefits of Certification- Earning a professional certification not only validates your skills but also enhances your employability. Here are the major benefits you gain:
Practical, Job-Ready Skills – Our certifications are designed to equip you with real-world, hands-on skills that match current industry demands — helping you become employment-ready from day one.
Lifetime Validity – Your certification is valid for a lifetime — no renewals or expirations. It serves as a permanent proof of your skills and training.
Lifetime Certificate Verification – Employers and institutions can verify your certification anytime through a secure and reliable verification system — adding credibility to your qualifications.
Industry-Aligned Certification –All certifications are developed in consultation with industry experts to ensure that what you learn is current, relevant, and aligned with market needs.
Preferred by Employers – Candidates from ISO-certified institutes are often prioritized by recruiters due to their exposure to standardized, high-quality training.
Free Job Assistance Based on Your Career Interests – Receive personalized job assistance and career guidance in your preferred domain, helping you land the right role faster.
Syllabus:-
Introduction to Data Analytics in Economics: Definition and importance of data analytics in economics, Role of data in economic decision-making, Types of economic data (macro, micro, financial, behavioral), Sources of economic data (government, private, open data), Introduction to statistical tools for economic analysis, Key economic indicators and their measurement, Role of big data in economic forecasting, Overview of data-driven economic policies, Ethical considerations in economic data analytics, Case studies on data-driven economic insights.
Statistical Foundations for Economic Data Analysis: Descriptive statistics and economic data, Probability distributions in economic modeling, Inferential statistics for economic research, Hypothesis testing in economic analysis, Correlation and regression in economics, Time series analysis fundamentals, Index numbers and inflation measurement, Variance and standard deviation in economic datasets, Sampling techniques in economic research, Statistical software applications for economic data.
Data Collection and Cleaning in Economic Analysis: Primary vs. secondary data sources in economics, Data extraction from surveys and reports, Handling missing data in economic datasets, Data cleaning techniques for accuracy, Identifying outliers in economic data, Standardizing economic variables for analysis, Data integration from multiple sources, Handling categorical and numerical economic data, Ethical data handling in economic research, Tools for automating data cleaning processes.
Data Visualization and Economic Interpretation: Fundamentals of data visualization in economics, Types of economic data visualizations (graphs, charts, maps), Using Python/R for economic data visualization, Creating dashboards for economic insights, Interpreting economic trends through visualization, Data storytelling for economic policy decisions, Geographic Information Systems (GIS) in economics, Best practices for clear and accurate visual representation, Comparing economic indicators using visuals, Case studies on real-world economic data visualization.
Econometric Modeling and Forecasting: Introduction to econometrics, Linear regression models for economic analysis, Multivariate regression and economic applications, Time series econometrics, Panel data analysis in economics, Autoregressive models in economic forecasting, Cointegration and error correction models, Machine learning in econometric forecasting, Limitations of econometric models, Real-world applications of econometrics in economic policy
Machine Learning in Economic Data Analytics: Introduction to machine learning for economic data, Supervised vs. unsupervised learning in economic applications, Decision trees and random forests in economic modeling, Neural networks for macroeconomic forecasting, Clustering techniques in consumer economics, Sentiment analysis in economic research, Predictive analytics for financial markets, Reinforcement learning in economic simulations, Bias and limitations in machine learning models, Case studies on AI applications in economic analysis.
