An MBA in Business Analytics equips professionals with the skills to harness data-driven insights and make informed decisions. The program typically covers a wide range of subjects, from foundational statistics and programming to advanced analytics techniques and industry applications. Students delve into topics such as data mining, machine learning, predictive modeling, and data visualization to develop a comprehensive understanding of how to extract meaningful information from complex datasets.
Table of contant
- Fundamentals of Data Analytics MBA Business Analytics Syllabus
- Quantitative Methods for Business MBA Business Analytics Syllabus
- Machine Learning for Business MBA Business Analytics Syllabus
- Marketing Analytics MBA Business Analytics Syllabus
- HR Analytics MBA Business Analytics Syllabus
- Financial Analytics MBA Business Analytics Syllabus
- Industry Internship MBA Business Analytics Syllabus
- FAQ’s: MBA Business Analytics Syllabus
Fundamentals of Data Analytics MBA Business Analytics Syllabus
Fundamentals of Data Analytics
Basic Statistics and Probability
Descriptive Statistics:
Measures of Central Tendency: Mean, median, and mode represent the primary price of a dataset.
Mean: The common value of a dataset.
Median: The middle cost while statistics is looked after in ascending order.
Mode: The most often happening value in a dataset.
Measures of Dispersion: Variance and widespread deviation degree the unfold of facts.
Variance: The common squared deviation from the mean.
Standard Deviation: The rectangular root of variance, imparting a greater interpretable degree of unfold.
Measures of Shape: Skewness and kurtosis describe the form of a distribution.
Skewness: Measures the asymmetry of the distribution.
Kurtosis: Measures the tail behavior of the distribution.
Probability Theory:
Probability: A numerical degree of the likelihood of an event going on.
Conditional Probability: The possibility of 1 occasion occurring given that every other event has already took place.
Bayes’ Theorem: A formula that relates conditional possibilities.
Probability Distributions: Functions that describe the chance of different effects. Common distributions include binomial, regular, and Poisson.
Data Types and Sources
Data Types:
Categorical: Data that may be labeled into classes.
Nominal: Categories haven’t any inherent order (e.G., colors, manufacturers).
Ordinal: Categories have a herbal order (e.G., ratings, schooling tiers).
Numerical: Data that may be measured quantitatively.
Discrete: Data that can tackle best precise values (e.G., quantity of objects sold).
Continuous: Data that may take on any price inside a selection (e.G., top, weight).
Data Sources:
Primary Data: Data accumulated without delay for a selected purpose (e.G., surveys, experiments).
Quantitative Methods for Business MBA Business Analytics Syllabus
Quantitative Methods for Business
Linear Programming
Linear programming is a mathematical optimization approach used to allocate constrained assets to maximize or reduce a linear objective function subject to linear constraints. It involves finding the superior solution to a problem with more than one variables and constraints.
Components of a Linear Programming Problem:
Objective feature: The quantity to be maximized or minimized.
Decision variables: The variables that can be adjusted to reap the top of the line solution.
Constraints: Limitations or restrictions on the decision variables.
Applications of Linear Programming:
Production planning: Determining ideal production degrees to maximise profit.
Resource allocation: Allocating scarce resources to diverse sports.
Transportation troubles: Finding the most green routes for transporting items.
Portfolio optimization: Selecting the most efficient mix of investments to maximize returns even as minimizing danger.
Decision Theory
Decision theory presents a framework for making rational decisions under uncertainty. It entails comparing different alternatives and choosing the one this is anticipated to yield the excellent final results.
Decision Making Process:
Identify alternatives: Consider the viable options to be had.
Assess probabilities: Determine the chance of various results.
Evaluate results: Assess the ability effects of each alternative.
Make a selection: Choose the alternative this is anticipated to yield the quality outcome.
Decision Criteria:
Expected price: The weighted average of the viable outcomes.
Utility principle: Incorporates the subjective cost of various outcomes.
Risk aversion: A choice for averting risk.
Machine Learning for Business MBA Business Analytics Syllabus
Machine Learning for Business
Supervised Learning Techniques
Supervised mastering includes education a version on a classified dataset, wherein each records point has a corresponding goal variable. The version learns to are expecting the target variable for new, unseen records.
- Regression: Predicting a continuous numerical cost (e.G., predicting residence costs).
- Linear regression
- Logistic regression
- Decision timber
- Random forests
- Support vector machines
- Classification: Predicting a specific value (e.G., classifying emails as spam or no longer spam).
- Logistic regression
- Decision trees
- Random forests
- Support vector machines
- Naive Bayes
- K-nearest neighbors
- Unsupervised Learning
Unsupervised mastering involves education a version on an unlabeled dataset, wherein the version have to find out patterns or structures within the records without explicit steering.
- Clustering: Grouping comparable facts factors together.
- K-method clustering
- Hierarchical clustering
DBSCAN
Dimensionality discount: Reducing the wide variety of functions even as preserving essential information.
Principal component analysis (PCA)
t-SNE
Neural Networks and Deep Learning
Neural networks are stimulated via the human brain and are composed of interconnected layers of synthetic neurons. Deep getting to know refers to neural networks with a couple of hidden layers, permitting them to study complicated patterns and functions.
Types of Neural Networks:
Feedforward neural networks: Information flows in one course from input to output.
Recurrent neural networks: Can system sequential information by way of keeping inner kingdom.
Convolutional neural networks: Specialized for processing image facts.
Applications:
Image popularity: Identifying objects in pictures.
Natural language processing: Understanding and generating human language.
Marketing Analytics MBA Business Analytics Syllabus
| Topic | Subtopics | Description |
|---|---|---|
| Customer Segmentation | ||
| – Demographic Segmentation | Age, Gender, Income, Education | Grouping customers based on personal traits. |
| – Behavioral Segmentation | Purchase History, Usage Patterns | Grouping customers based on how they interact with products. |
| – Geographic Segmentation | Location, Region, City Size | Grouping customers based on where they live or work. |
| Social Media Analytics | ||
| – Engagement Metrics | Likes, Shares, Comments | Measuring how users interact with social media posts. |
| – Sentiment Analysis | Positive, Negative, Neutral | Analyzing customer feelings and opinions on social platforms. |
| – Influencer Analysis | Identifying Key Influencers | Finding individuals who have a large impact on customer opinions. |
| Pricing and Promotion Analysis | ||
| – Price Elasticity | Changes in Demand with Price | Understanding how price changes affect customer buying behavior. |
| – Discount Effectiveness | Sales Before and After Discounts | Evaluating how promotions and discounts impact sales. |
| – Competitive Pricing | Comparing Prices with Competitors | Analyzing how your pricing compares to competitors’ pricing |
HR Analytics MBA Business Analytics Syllabus
| Topic | Subtopics | Description |
|---|---|---|
| Employee Retention Models | Analyzing factors to retain employees and reduce turnover. | |
| – Attrition Prediction | Predictive Models (Logistic Regression, Decision Trees) | Forecasting employee turnover based on historical data. |
| – Engagement Surveys | Employee Satisfaction, Pulse Surveys | Measuring employee satisfaction to understand reasons for leaving or staying. |
| – Exit Interview Analysis | Voluntary/Non-voluntary Termination Reasons | Analyzing feedback from employees who leave the company. |
| Workforce Planning | Strategic planning for future workforce needs. | |
| – Demand Forecasting | Headcount Planning, Resource Allocation | Predicting the number of employees required to meet future business demands. |
| – Skill Gap Analysis | Current vs. Required Skills | Identifying gaps in employee skills for training or recruitment. |
| – Succession Planning | Key Role Identification, Leadership Pipelines | Preparing employees for future leadership or critical positions. |
| Performance Analytics | Measuring and improving employee performance. | |
| – Key Performance Indicators | Goal Achievement, Targets | Setting and tracking individual and team performance metrics. |
| – 360-Degree Feedback | Peer, Manager, and Self-Evaluation | Collecting comprehensive feedback from multiple sources for performance review. |
| – Productivity Metrics | Output per Employee, Efficiency | Analyzing employee productivity and identifying areas for improvement. |
Financial Analytics MBA Business Analytics Syllabus
| Topic | Subtopics | Description |
|---|---|---|
| Risk Analytics and Portfolio Management | Managing risks and optimizing investment portfolios. | |
| – Risk Assessment | Market Risk, Credit Risk, Operational Risk | Identifying and evaluating potential financial risks faced by a business. |
| – Diversification | Asset Allocation, Risk-Return Tradeoff | Spreading investments across different assets to reduce risk. |
| – Portfolio Optimization | Sharpe Ratio, Modern Portfolio Theory | Techniques to maximize returns for a given level of risk. |
| Predictive Models for Financial Planning | Forecasting financial outcomes and planning for the future. | |
| – Budget Forecasting | Time Series, Regression Analysis | Predicting future revenues and expenses to aid budgeting. |
| – Cash Flow Analysis | Cash Flow Projections, Sensitivity Analysis | Analyzing and forecasting the flow of cash in and out of a business. |
| – Scenario Planning | Best/Worst Case Scenarios | Simulating different financial outcomes based on varying assumptions. |
| Fraud Detection and Prevention | Identifying and mitigating financial fraud in business. | |
| – Anomaly Detection | Data Mining, Machine Learning Models | Using algorithms to detect unusual financial transactions that may indicate fraud. |
| – Fraud Risk Assessment | Internal Controls, Risk Indicators | Evaluating processes to identify areas vulnerable to fraud. |
| – Transaction Monitoring | Red Flags, Suspicious Activity Reports (SAR) | Continuously monitoring transactions for signs of fraud or irregularities. |
Industry Internship MBA Business Analytics Syllabus
Practical Exposure to Business Analytics
Real-world problem-fixing: Apply analytical techniques to deal with challenges confronted through organizations.
Data-pushed decision-making: Contribute to strategic selections based totally on information-pushed insights.
Collaboration with go-practical teams: Work with stakeholders from various departments.
Working with Industry Tools and Techniques
Proficiency in enterprise-general software program: Gain arms-on revel in with equipment like Python, R, SQL, Tableau, and Power BI.
Application of superior analytics strategies: Utilize strategies inclusive of gadget learning, deep gaining knowledge of, and statistical modeling.
Exposure to huge statistics technology: Work with large datasets and cloud-primarily based structures.
Report and Presentation of Learnings
Effective conversation: Develop talents in presenting findings and guidelines to stakeholders.
Data visualization: Create compelling visualizations to bring complicated information.
Storytelling with statistics: Craft narratives that correctly communicate the value of analytical insights.
FAQ's: MBA Business Analytics Syllabus
Q1. What is the MBA in business analytics syllabus?
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Q2. Is Business Analytics MBA hard?
Ans.
Q3. Is there maths in MBA Business Analytics?
Ans:
Math is crucial in business analytics because the close relationship between business analytics and mathematics is what helps an analyst to excel in this realm. A robust understanding of mathematical foundations like probability, statistics, and linear algebra is important to understand and analyze large data sets
