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:

Introduction

● 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

● Estimation

● Interval Estimates

● Confidence Intervals

● Prediction Intervals

Statistical Inference

● Hypothesis Testing

● One-Sample Hypothesis Tests

● Two-Sample Hypothesis Tests

● ANOVA

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

● Classification

● 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

● Matrices

● Attaching to objects

● Statistical Computation and Simulation

● Graphics