Masters in Business Analytics

Understanding the evolution of analytics in business

What is Business Analytics Definitions and relevance

Components of Business Analytics: People, Process, Technology

Difference between Data Science, Business Analytics, and Business Intelligence

Types of analytics: Descriptive, Predictive, Prescriptive

Overview of the Analytics Lifecycle

Business Analyst roles and responsibilities

Tools used in Business Analytics (Excel, SQL, Python, Power BI, Tableau)

Industry applications of Business Analytics

Case studies: Banking, Retail, Healthcare, Manufacturing

Excel interface and data navigation tips

Working with structured datasets in Excel

Data formatting, conditional formatting, and data validation

Formulas and Functions:

• Text Functions (LEFT, RIGHT, MID, CONCATENATE)
• Logical Functions (IF, AND, OR, NOT)
• Lookup Functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
• Statistical Functions (AVERAGE, MEDIAN, MODE, STDEV)

Data Cleaning Techniques

Sorting, Filtering, and Advanced Filters

Using Named Ranges for clarity and speed

Charts and Graphs: Column, Line, Bar, Pie, Combo, Sparklines

Creating PivotTables and PivotCharts

Using slicers and timeline filters

Introduction to Power Query for data transformation

Goal Seek, Scenario Manager, and Data Tabl

Creating interactive dashboards with Excel

Types of data: Structured vs Unstructured, Qualitative vs Quantitative

Levels of Measurement: Nominal, Ordinal, Interval, Ratio

Measures of Central Tendency: Mean, Median, Mode

Measures of Dispersion: Range, Variance, Standard Deviation

Skewness and Kurtosis

Basics of Probability and probability distributions

Normal Distribution, Binomial and Poisson Distributions

Sampling techniques and sample size determination

Central Limit Theorem

Confidence intervals and margin of error

Hypothesis Testing:

• Null and Alternate Hypotheses
• One-tailed vs Two-tailed tests
• Z-test, t-test (one-sample, two-sample)
• ANOVA
• Chi-square test

Correlation analysis and Pearson's coefficient

Linear Regression analysis

Interpreting statistical outputs in business context

Basics of databases and relational database design

Understanding ER models and data schemas

Introduction to SQL syntax and queries

SELECT statements and filtering with WHERE clause

Sorting results using ORDER BY

Aggregate functions: SUM, AVG, COUNT, MIN, MAX

GROUP BY and HAVING clauses

Using ALIAS for readability

Understanding JOINs: INNER, LEFT, RIGHT, FULL OUTER

Subqueries and Nested queries

Common Table Expressions (CTEs)

Window functions (ROW_NUMBER, RANK, LEAD, LAG)

Case expressions and IF conditions

Date and string functions

Creating and modifying views

Basic data manipulation (INSERT, UPDATE, DELETE)

Practice with large datasets

Introduction to BI tools: Differences between Power BI and Tableau

Connecting to multiple data sources

Data modeling and data relationships

Measures vs calculated columns

Data transformation and cleaning using Power Query

Designing basic and advanced charts:

• Line, Bar, Column, Pie, Scatter, Heat Maps, Tree Maps, Waterfall, KPI Cards

Using filters, slicers, and date hierarchies

Drill-downs and drill-through reports

Tooltips and interactions

Using DAX in Power BI:

• CALCULATE, FILTER, ALL, RELATED, Time Intelligence functions

Creating dashboards and layouts

Publishing and sharing dashboards

Real-world dashboarding use cases:

• Sales dashboards
• Marketing campaign performance
• Financial KPI dashboards

Introduction to Python and Anaconda environment

Data types, variables, control structures (if, for, while)

Functions and Lambda expressions

Working with lists, tuples, dictionaries

Reading and writing files

Introduction to NumPy:

• Arrays, array operations, indexing

Introduction to Pandas:

• Series and DataFrame objects
• Reading data from CSV, Excel, SQL
• Data selection, filtering, sorting
• Handling missing values and duplicates
• GroupBy operations
• Merging and joining datasets

Exploratory Data Analysis (EDA)

Data visualization with:

• Matplotlib: Line plots, bar charts, histograms
• Seaborn: Pairplots, heatmaps, boxplots

Automating Excel/CSV reports with Python

Overview of Machine Learning in Business

Supervised vs Unsupervised Learning

Linear Regression:

• Model creation, evaluation (R-squared, MAE, RMSE)

Logistic Regression:

• Probability interpretation, ROC Curve, AUC

Decision Trees and Random Forests

K-Means Clustering

Model evaluation metrics:

• Confusion Matrix, Accuracy, Precision, Recall, F1 Score

Overfitting and Underfitting

Train-test split and Cross-validation

Feature selection and engineering basics

Business case applications:

• Customer churn prediction
• Sales forecasting
• Market segmentation

Choosing a real-world capstone project (Retail, Finance, HR, Marketing, etc.)

Defining the business problem statement and KPIs

Data collection and preparation

Exploratory Data Analysis and visualization

Building dashboards and simple predictive models

Final project presentation with business insights

Peer and mentor reviews

Resume building tailored to Business Analyst roles

Interview preparation: