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Artificial Intelligence And Data Analytics

As the volume of available business data expands, the winners in tomorrow’s marketplace will be those who can generate insight from information. Yet, many leaders feel daunted by the sheer […]

Instructor AlBrza Global (Droub Programs)

Updated February 4, 2023

About Course

As the volume of available business data expands, the winners in tomorrow’s marketplace will be those who can generate insight from information. Yet, many leaders feel daunted by the sheer amount of data out there. Many others make the critical mistake of looking for patterns in the data they have, instead of framing productive questions to shape the data they need. Competency in this area is so lacking, a recent Gartner study predicted that by 2020, 80% of organizations will initiate deliberate development programs in data literacy.

Many of the ideas, methods and principles that describe the best business data and analytics practices were explained in this Bootcamp. You will learn how to “think data” the Booth way. They develop the critical and creative reasoning skills needed to frame a data analytics project, collaborate with data specialists, and ultimately make evidenced-based decisions that drive results — without sacrificing speed and agility.

Master the ins and outs of data analytics from foundational topics in data management with Excel and SQL to data cleansing and preprocessing using Python’s highly sought-after functionalities – Pandas and Numpy. Complete your analytics toolkit with the full range of data visualization tools you’ll need to become a compelling data storyteller. Featuring insights into the SQL interview process and a tutorial on date and time value manipulation in Python to give you a strong leg up over the competition.

Develop the key ingredients of a powerful data analytics strategy: a specific business objective, well-developed theories, and a model that points the way to critical data and deep insight.
Frame questions to generate data-based insight: Identify specific objectives and related hypotheses to drive data analysis. Avoid biases in interpreting data: Sidestep the common pitfall of unconsciously bending data to support false assumptions and preconceptions.
Tell the story of the data: Translate data-driven insights into actionable decisions and drive buy-in by delivering a compelling narrative.
Distinguish between various data science related fields
Integrate common data science techniques
What are the most common data science programming languages
Understand the terms traditional and big data
Use specific data science tools
Become familiar with data science job positions and alternatives
Understand Excel best practices
Insert and format different types of charts
How to use Excel functions
Distinguish between the diverse types of data and levels of measurement
Calculate confidence intervals
Become familiar with the p-value
Understand what a distribution is
Perform hypothesis testing
Basic Python syntax
Understand Object-Oriented Programming (OOP)
Work with variables, operators, and conditional statements
Study Python sequences and iterations
Import modules in Python
Create basic and advanced charts
Create stunning visualization
Develop a basic understanding of the pandas library
Practice with fundamental programming tools
Work with pandas Series and DataFrames
Study collecting, cleaning, and preprocessing data

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
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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Artificial Intelligence And Data Analytics

Instructor

AlBrza Global (Droub Programs)

Level

Expert

Enrolled

Duration 30 hours

Lectures

118 Lessons

Artificial Intelligence And Data Analytics

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

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 )

The Different Data Science Fields

Analysis vs Analytics

Intro to Business Analytics, Data Analytics, and Data Science

Adding Business Intelligence (BI), Machine Learning (ML), and Artificial Intelligence (AI) to the picture

Correlation vs. Causation

When are Traditional data, Big Data, BI, Traditional Data Science and ML applied?

Why do we Need each of these Disciplines?

Common Data Science Techniques

Traditional Data: Techniques

Traditional Data: Techniques

Traditional Data: Real-life Examples

Big Data: Techniques & Real-life Examples

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

Traditional Methods: Techniques & Real-life Examples

Machine Learning (ML): Techniques

Machine Learning (ML): Types of Machine Learning

Machine Learning (ML): Real-life Examples

MODULE ( 3 )

Common Data Science Tools

Programming Languages & Software Employed in Data Science – All the Tools You Need

Data Science Job Positions: What do they Involve and What to Look out for?

Dispelling Common Misconceptions

Introduction to MS-Excel

Data Entry Techniques in Excel

How to Make Your Spreadsheets Look Professional

Excel Formulas for Beginners

Useful Tools & Tips

Beginner, Intermediate & Advanced Functions

Practical Exercise “Build a P&L From Scratch”

Introduction to Excel Charts

Building Professional Charts in Excel

MODULE ( 4 )

Introduction to Statistics

Population vs sample

Descriptive Statistics Fundamentals

Types of data and levels of measurement

Levels of measurement

Categorical Variables. Visualization techniques

Numerical Variables. Frequency distribution table

The histogram

Cross table and scatter plot

Inferential Statistics Fundamentals

What is a distribution

The Normal Distribution

The Standard Normal Distribution

Central limit theorem

Standard error

Estimators and estimates

Confidence Intervals

Population variance known, z-score

Confidence Interval Clarifications

Student’s T Distribution

Population variance unknown, t-score

Confidence intervals. Two means. Dependent samples

Hypothesis testing

Null vs Alternative

Rejection region and significance level

p-value

Practical Example – Hypothesis Testing

MODULE ( 5 )

Introduction to Python