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Business Analysis Practice Exam

Business Analysis Practice Exam

Business analysts analyze data to uncover difficulties, opportunities, and solutions for businesses, which they subsequently communicate to the company's stakeholders. They are also important in fixing outmoded processes, supporting new technology adoption, and driving innovation, making this a sought-after position in every business, from consulting to product management. A Business Analyst's professional path can follow a multitude of directions depending on their experience and future career goals.

Roles and Responsibilities

Business analysts collect and analyze data on behalf of companies. They frequently offer this information to stakeholders in order to discover patterns and areas where they may improve.

Business analysts assist companies in achieving their goals by recommending strategies to increase structure, efficiency, and profits. Individuals that are multi-talented and have specialized expertise to enhance the firm's economy are required in today's organizations.

Course Outline

The Business Analyst certification covers the following topics:


Evolution and Scope

Data for Business Analytics

Decision Models

Problem Solving and Decision Making

Analytics and Spreadsheets

Excel Formulas

Excel Functions

Spreadsheet Add-Ins

Spreadsheet Modeling

Visualizing Data

Data Visualization

Data Queries

Summarizing Data

Frequency Distributions

PivotTables and Pivot Charts

Descriptive Statistical Measures

Statistical Notation

Measures of Location

Measures of Dispersion

Measures of Shape

Measures of Association

Excel Descriptive Statistics Tool

Probability Distributions

Probability Basics

Discrete Probability Distributions

Continuous Probability Distributions

Distribution Fitting

Sampling and Estimation

Sampling Methods

Statistical Sampling

Sampling Distributions


Interval Estimates

Confidence Intervals

Prediction Intervals

Statistical Inference

Hypothesis Testing

One-Sample Hypothesis Tests

Two-Sample Hypothesis Tests


Predictive Modeling

Logic-Driven Model

Data-Driven Modeling

Analyzing Uncertainty and Model Assumptions

Risk Solver Platform

Regression Analysis

Simple Linear Regression

Residual Analysis and Regression Assumptions

Multiple Linear Regression

Regression with Categorical Independent Variables

Regression Models with Nonlinear Terms

Forecasting Techniques

Qualitative and Judgmental Forecasting

Statistical Forecasting Models

Time Series Forecasting Models

Simulation and Risk Analysis

Monte Carlo Simulation

New-Product Development Model

Newsvendor Model

Overbooking Model

Cash Budget Model

Data Mining

Data Mining Basics

Cluster Analysis


Association Rule Mining

Cause-and-Effect Modeling

Linear Optimization

Building Linear Optimization Models

Excel Solver

Optimization Models for Prediction and Insight

Applications of Linear Optimization

Integer Optimization

Solving with General Integer Variables

Integer Optimization Models with Binary Variables

Mixed-Integer Optimization Models

Nonlinear and Non-Smooth Optimization

Modeling and Solving Nonlinear Optimization Problems

Quadratic Optimization

Spreadsheet Models

Optimization Models with Uncertainty

Stochastic Programming

Risk Analysis in Optimization

Decision Analysis

Decisions with Uncertain Information

Decision Trees

The Value of Information

R Programming Language

Objects and Arithmetic

Summaries and Subscripting


Attaching to objects

Statistical Computation and Simulation


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