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Accelerating Artificial Intelligence With Business Analysis

Anyone defining requirements for digital solutions must keep up with new trends in technology such as Artificial Intelligence (AI) and Big Data to remain relevant in the workforce. The requirements […]

Instructor AlBrza Global (Droub Programs)

Updated February 3, 2023

About Course

Anyone defining requirements for digital solutions must keep up with new trends in technology such as Artificial Intelligence (AI) and Big Data to remain relevant in the workforce.

The requirements elicitation and analysis process is just as crucial for AI initiatives as it is for any other project. Business stakeholders and technical experts like data scientists, chatbot developers, or machine learning engineers involved in an AI project need to collaborate to produce a successful product.

Business Analysts Make Sure Technological Developments Drive Business Value

The role of the business analyst is to help organizations with their decision-making process by asking questions and analyzing data to better understand what they need. With the emergence of AI, data scientists need input from business analysts about what type of information is needed to generate AI models.

The Business Analyst Role in Implementing AI-based Business Solutions
Requirements Elicitation and Analysis for Conversational User Experiences
Comparing Natural Language Understanding (NLU) Bots and Rule-based Bots
Integrate common data science techniques
What are the most common data science programming languages
Use specific data science tools
Overview of Conversation Flow Analysis and Design for the Business Analyst
Understand the data sources involved
Interpret the results of an analysis and extract insights
Check the adequacy and fitness of data involved
Make decisions based on the insights
How to improve business processes and gain new revenue streams
Improve your decision-making strategy
Use data to provide intelligent services and products
Distinguish between strategic data needs
Understand customers and markets
Data quality, ethics, privacy, ownership, access and security
Understand Excel best practices
Navigate spreadsheets professionally
How to use Excel functions
Work with pivot tables
Distinguish between the diverse types of data and levels of measurement
Become familiar with the p-value
Perform hypothesis testing
Create basic and advanced charts
Create stunning visualization
Manage expectations of different stakeholders
Define business intelligence
Improve your understanding of various analysis methodologies

Course Content

MODULE ( 1 )

Introduction to AI

00:00
History of Artificial Intelligence

00:00
Artificial Intelligence (AI) vs. Machine Learning (ML) vs. Deep Learning (DL)

00:00
AI Applications

00:00
AI Basics

00:00
The benefits of Artificial Intelligence

00:00
Future of Artificial Intelligence

00:00
How Do Big Data and Data Analytics Change IT Analysis

00:00
Machine Learning, Weak AI (ANI), and Strong AI (AGI)

00:00
The Business Analyst Role in Implementing AI-Based Business Solutions

00:00
The Impact of AL in Three Level of Business Analysis

00:00
Targeted IT Business Analysis: Analyze Features and User Stories

00:00
Tactical Business Analysis: Elicit and Prioritize User Requirements

00:00
Strategic Business Analysis: Prepare for Future Changes

00:00

MODULE ( 2 )

MODULE ( 3 )

MODULE ( 4 )

MODULE ( 5 )

MODULE ( 6 )

MODULE ( 7 )

MODULE ( 8 )

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Accelerating Artificial Intelligence With Business Analysis

Instructor

AlBrza Global (Droub Programs)

Level

Intermediate

Enrolled

Duration 30 hours

Lectures

100 Lessons

Accelerating Artificial Intelligence With Business Analysis

About Course

What Will You Learn?

Course Content

MODULE ( 1 )

Introduction to AI

History of Artificial Intelligence

Artificial Intelligence (AI) vs. Machine Learning (ML) vs. Deep Learning (DL)

AI Applications

AI Basics

The benefits of Artificial Intelligence

Future of Artificial Intelligence

How Do Big Data and Data Analytics Change IT Analysis

Machine Learning, Weak AI (ANI), and Strong AI (AGI)

The Business Analyst Role in Implementing AI-Based Business Solutions

The Impact of AL in Three Level of Business Analysis

Targeted IT Business Analysis: Analyze Features and User Stories

Tactical Business Analysis: Elicit and Prioritize User Requirements

Strategic Business Analysis: Prepare for Future Changes

MODULE ( 2 )

Introduction to Data and Data Science

The Different Data Science Fields

Analysis vs Analytics

Intro to Business Analytics, Data Analytics, and Data Science

The Relationship between Different Data Science Fields

What is the Purpose of each Data Science field

Common Data Science Techniques

Traditional Data: Techniques

Big Data: Techniques with Real-life Examples

Business Intelligence (BI): Techniques & Real-life Examples

Traditional Methods: Techniques, Real-life Examples

Machine Learning (ML): Techniques, Types of Machine Learning, Real-life Examples

Programming Languages & Software Employed in Data Science

Dispelling Common Misconceptions

MODULE ( 3 )

Data Literacy

Qualitative vs. Quantitative Data

Structured vs. Unstructured Data

Data at Rest vs. Data in Motion

Transactional vs. Master Data

Big Data, Storing Data, Database, Data Warehouse, Data Marts, The ETL Process

Apache Hadoop, Data Lake, Cloud Systems, Edge Computing

Batch vs. Stream Processing, Graph Database

Analysis vs. Analytics, Descriptive Statistics, Inferential Statistics, Business Intelligence (BI)

Supervised Learning, Regression Analysis, Time Series Forecasting, Classification

Unsupervised Learning, Clustering Analysis, Association Rules, Reinforcement Learning, Deep Learning

Reading Data, Data Quality Assessment, Data Description, Measures of Central Tendency, Measures of Spread

Interpreting Data, Correlation Аnalysis, Correlation Coefficient, Correlation and Causation

Simple Linear Regression, R-squared, Forecasting, Hypothesis Testing

P-Value, Statistical Significance, Classification Models, Accuracy, Recall and Precision

MODULE ( 4 )

Data Strategy

Deciding Your Strategic Data Needs

Using Data to Improve Your Decisions

Using Data to Understand Your Customers and Markets

Using Data to Provide More Intelligent Services

Using Data to Make More Intelligent Products

Using Data to Improve Your Business Processes

Monetising Your Data

Defining Your Data Use Cases

Sourcing and Collecting the Data

Data Governance

Turning Data into Insights

Creating the Technology and Data Infrastructure

Building the Data Competencies in Your Organization

Executing and Revisiting Your Strategy

MODULE ( 5 )

Microsoft Excel

Data Entry Techniques in Excel

How to Make Your Spreadsheets Look Professional

Inserting, Deleting, and Modifying Rows & Columns

Excel Formulas & Functions, Formatting

Useful Tools & Tips

Key Excel Functions

Practical Exercise

Excel Charts

Building Professional Charts in Excel

Pivot tables

Building a world-class report with GETPIVOTDATA (practical exercise)

Descriptive Statistics Fundamentals