Description
Course Name: Certificate in Management (Business Analytics)
Course Id: CIMBAQ1001.
Eligibility: Graduate or Equivalent.
Objective: The Certificate in Management (Business Analytics) is a specialized short-term program aimed at equipping individuals with the skills and knowledge required to analyze business data and support decision-making processes. This program combines management principles with analytical tools and techniques, preparing learners for roles in data-driven industries.
Duration: Two Month.
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- 60 minutes.
No. of Questions- 30. (Multiple Choice Questions).
Maximum Marks- 100, Passing Marks- 40%.
There is no negative marking in this module.
| Marking System: | ||||||
| S.No. | No. of Questions | Marks Each Question | Total Marks | |||
| 1 | 10 | 5 | 50 | |||
| 2 | 5 | 4 | 20 | |||
| 3 | 5 | 3 | 15 | |||
| 4 | 5 | 2 | 10 | |||
| 5 | 5 | 1 | 5 | |||
| 30 | 100 | |||||
| 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 | 40-50 | 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 Business Analytics: Definition and scope of business analytics, Importance of analytics in decision making, Types of business analytics (descriptive, predictive, prescriptive), Business intelligence vs business analytics, Data-driven decision-making process, Role of big data in analytics, Key components of business analytics systems, Overview of business analytics tools and software, Business analytics in different industries, Case studies on the application of business analytics.
Data Collection and Management: Sources of business data (internal and external), Types of data: structured and unstructured, Data collection techniques and methods, Data cleaning and preprocessing, Data storage and retrieval systems, Data quality management, Data integration from various sources, Introduction to data warehousing, Data governance and security, Case studies on data management in business analytics.
Statistical Methods for Business Analytics: Basic statistical concepts for business analytics, Descriptive statistics: mean, median, mode, Variability and spread: range, variance, standard deviation, Probability distributions in analytics, Inferential statistics: hypothesis testing, Regression analysis and correlation, Time series analysis for business forecasting, Statistical tools and software for analysis, Case studies on applying statistics in business problems.
Predictive Analytics and Modeling: Introduction to predictive analytics, Techniques in predictive modeling (linear and logistic regression), Data mining and machine learning in business analytics, Model building and validation, Overfitting and underfitting in models, Cross-validation and performance metrics, Predictive analytics tools and software, Application of predictive analytics in business decision-making, Introduction to artificial intelligence in analytics, Case studies on predictive analytics in various industries
Data Visualization and Reporting: Importance of data visualization in business analytics, Principles of effective data visualization, Types of data visualization (charts, graphs, heat maps), Tools for data visualization (Tableau, Power BI, etc.), Creating interactive dashboards, Key performance indicators (KPIs) and metrics, Reporting business insights through visual representation, Storytelling with data, Communicating complex data effectively, Case studies on successful data visualization strategies.
Prescriptive Analytics and Optimization: Introduction to prescriptive analytics, Optimization techniques in business analytics, Linear programming and its applications, Decision analysis and making under uncertainty, Simulation modeling and Monte Carlo methods, Resource allocation and capacity planning, Cost-benefit analysis in decision-making, Application of prescriptive analytics in supply chain and operations, Use of prescriptive analytics in marketing and finance, Case studies on prescriptive analytics in business optimization.
