Artificial Intelligence & Data Analytics.

University Lectures

Meet your lecturers in Business Management

Lecture: ( 9 )

9- Artificial Intelligence

Data Analytics.

Mr. / Girgis

Go to my Blog

https://mrgirgis.blogspot.com/

Click here to go to : Abouna Fanous Site.

موقع عمى أبونا فانوس الأنبا بولا

E-mail 1 : girgishannaharoun@yahoo.co.uk

E-mail 2 : girgishanna027@gmail.com

اضغط هنا لتصل الى فيديوهات موقع ابونا فانوس و تنال بركته

VK8GFP9HFt9BbBrZe58JpDvB9NEdhFIgtrOB-I8YcSjs9DNu9yWv_6L9Qb-bnK0v.jpg

Translate This Page

“This poor man cried out, and the

LORD heard him, and saved him

out of all his troubles

.”Psalm 34:6

free-clipart-important-notice-9 | Chippewas of the Thames

Humor:

Girl Laughing Hysterically Stock Photo ...

Teacher

I wish you’d pay a little attention,

David.

David:

I'm paying as little as I can,

teacher.

Dear visitor,

Use the language selector

above to go through my whole

site using any native language

you speak,

then you can enjoy my

YouTube channel.

اختاراي لغة من لغات العالم / اللغة التى تريد

تصفح موقعى باستخدامها . استخدم المؤشر

الذى فى الاعلى

I register a video presentation

in my YouTube channel for

each page of my site.

Next,use the other world site

selector above to go to the

search engine site or the social

media site you like.

My YouTube Channel:

منهج الانجليزى ثانوية عامة / و شكل و طريقة امتحا ن

نظام التعليم الجديد

والتصحيح الكترونيا

Business Courses - Masterclass – House of the Reader

Artificial Intelligence

Data Analytics.

Introduction

Artificial Intelligence (AI) and Data Analytics are two

transformative fields that are revolutionizing industries

by enabling smarter decision-making, automation,

and insights extraction from vast amounts of data.

While AI focuses on creating systems that can

perform tasks requiring human intelligence,

Data Analytics involves analyzing data to

uncover meaningful patterns and insights.

Artificial Intelligence (AI) and data analytics,

when combined, transform raw data into actionable,

proactive insights by automating data cleaning,

analysis, and interpretation. This partnership

allows organizations to move from reactive reporting

to predictive modeling, enhancing decision-making

across sectors. Key techniques include machine

learning for forecasting and NLP for analyzing

unstructured data, such as.

Artificial intelligence (AI) uses programming

techniques inspired by natural learning. AI

enables computer systems to solve problems

with accurate data, context, and environment

interpretation. Examples: reading text, driving cars,

image recognition, and industrial machine control.

Summary

AI automates data preparation, enables natural

language querying, and delivers real-time insights

that replace static reporting across analytics

workflows.

AI accelerates analysis, detects patterns humans

miss, and democratizes access to insights, but

organizations must address risks like bias, data

quality issues, and over-reliance on automation.

AI handles routine tasks like data cleaning and report

generation, freeing analysts to focus on judgment-

intensive work, business context, and model oversight.

What AI for Data Analytics Means

As AI becomes increasingly integral to how

organizations work with data, more teams are

adopting AI-based tools to move faster and make

better decisions. Instead of relying solely on manual

queries, dashboards and human‑driven interpretation,

modern analytics can now incorporate AI/ML,

natural language processing (NLP) interfaces and

automated workflows that augment human workflows.

For example, generative AI makes analytics more

accessible by allowing people to ask questions in

everyday language instead of writing SQL queries

or using complex BI tools. Automation reduces the

manual effort required to clean data, generate

features and run models, freeing analysts to focus

on higher‑value tasks.

Compared with traditional analytics, where teams

manually prepare data and build reports, AI can now

perform many of the more routine and repetitive tasks

.. Analysts still guide the process, but by incorporating

AI, analytics teams can prepare data more reliably,

generate insights faster and make predictions part

of everyday decision‑making.

At Databricks, we don't consider AI to be a separate

add‑on, but rather an integrated capability that

enhances every step of the data lifecycle when

built on a unified, well‑governed foundation.

Getting Started with AI for Data

Analytics

AI supports common analytics tasks such as classifying

data, identifying trends, answering natural‑language

questions and recommending next-best actions,

although organizations must still manage risks like

biased outputs, poor data quality and governance

issues. To get started, analysts need foundational

data literacy, comfort with basic ML concepts and

the ability to validate results.

Most teams begin by establishing a unified data

foundation and piloting small, high‑value use cases,

but whether it’s forecasting demand or helping

business users explore data conversationally, AI

extends what analytics teams can accomplish and

makes insights more widely available.

How AI Enhances Each Stage of the

Data Analytics Workflow

In data analytics, workflows typically move through

the following stages:

Data collection

Preparation

Analysis

Visualization

Decision‑making

Each step has its own challenges, but AI can play

a meaningful role that is specific to each.

Data Collection

In this stage, AI helps teams gather information from

a variety of sources without the need to build custom

pipelines for each one. Automated systems are able

to pull data from applications, documents, sensors

and APIs, then classify it for analysis. AI also deals

with large datasets more efficiently than traditional

tools, which is especially valuable when organizations

collect data from multiple business units or real‑time

streams.

Preparation

During the data cleaning and preparation stage,

AI can identify anomalies, missing values and

inconsistencies that would take an analyst much

longer to find. It can also automate repetitive tasks

like formatting fields, standardizing labels and

joining datasets. This reduces the time employees

have to spend on manual prep work and improves

the quality of downstream analysis by basing it on

higher‑quality data.

Analysis

This stage is where AI can help recognize patterns,

predict outcomes and detect unusual behavior. AI-

driven models are able to run continuously, which

makes real‑time analysis and forecasting possible.

Instead of waiting for scheduled reports, teams

can see changes as they happen and respond

immediately.

Visualization

AI tools can easily create charts, dashboards and summaries

based on the underlying data. NLP technologies also

enable users to ask questions in a conversational

way and receive clear explanations in return. This

makes complex analysis easier to understand and

helps nontechnical users explore data without

needing advanced skills.

Decision-making

AI elevates this stage by moving teams from

decisions based on historical reports to

forward‑looking strategies. Modern AI solutions

can surface anomalies, forecast emerging risks

and opportunities and distill unstructured signals

such as customer sentiment into patterns that

leaders can act on. Combining this with Natural

Language Querying reduces data preparation time

while providing analysts with insights based on real-

time, “what-if” scenarios that drive timely action.

Practical Examples of AI-powered

Analytics in Action

As organizations mature in their use of AI for data

analytics, and while there is certainly plenty of room

for further development, it makes sense to look at

some of the ways AI is currently being used

successfully in the following categories:

Scenario-based workflows

Real-time analytics

Natural language querying

Accelerating time-to-insight

Scenario-based Workflows

One common scenario-based example is sentiment

analysis, where AI analyzes customer feedback,

social posts or support tickets to determine whether

customers feel positive, neutral or negative about a

product or service. This helps teams understand

trends in customer experience without having to

read thousands of individual comments.

Predictive analytics is another widely used workflow,

where AI models provide forward‑looking insights,

such as forecasting demand, estimating churn or

predicting which sales leads are most likely to

convert.

Anomaly detection can flag unusual patterns in

transactions, sensor readings or system logs so

teams can investigate issues before they escalate.

For organizations with large datasets, AI can also

generate quick summaries that highlight key

themes or changes, saving hours of manual review.

Real-time Analytics

By conducting AI‑powered real-time analysis,

retailers can forecast sales for specific days and

adjust staffing or inventory levels. Manufacturers

can identify operational issues as they happen by

monitoring equipment data. Logistics teams can

track delivery performance and anticipate delays.

Real‑time insights like these help organizations

reduce the lag between data collection and action.

Natural Language Querying

Natural language querying makes analytics more

accessible. Instead of writing SQL queries or

navigating dashboards, users can ask questions

like “What were our top‑selling products last

quarter” or “Show me regions with rising support

volume.” AI interprets the question, runs the

analysis and returns a clear answer, lowering the

barrier for nontechnical users of business data.

Tools and Platforms Enabling AI for

Data Analytics

AI‑driven BI tools increasingly feature core capabilities

to support key data analytics workflows. Predictive

features support trend forecasting and risk

identification. Generative AI can summarize datasets

or translate technical findings into plain language.

Natural language querying makes exploration more

intuitive, while AI‑assisted visualization and

workflow automation reduce the manual effort

behind dashboards, data prep and routine reporting.

The right tool still depends on the problem you’re

solving. Forecasting requires strong predictive

models, dashboard automation benefits from

AI‑driven visualization and spreadsheet augmentation

is far easier with natural‑language features that cut

down on complex formulas. At the moment, some

tools are better at some capabilities than others,

although the trend is clear. The modern BI stack is

converging toward a unified suite that includes all of

them. Databricks AI/BI brings these capabilities together

in one platform, pairing governed data with

AI‑assisted analytics for faster, more reliable insights.

Benefits of Using AI in Data Analytics

The benefits of using AI for data analytics generally center

around productivity, efficiency, accuracy, accessibility

and scalability. Specific benefits include:

Faster real‑time analysis:

AI processes large datasets quickly and updates

insights continuously, enabling a shift from static

reports to always‑current intelligence.

Improved accuracy and pattern

detection:

ML models uncover subtle trends, anomalies and

relationships that humans often miss, reducing

error and strengthening analytical rigor.

Democratized analytics across

technical and business users:

Natural language interfaces let anyone explore

data in everyday language, lowering barriers and

broadening participation.

Scalable insight generation:

Automated workflows, shared models and unified

platforms deliver consistent insights across teams,

freeing analysts for higher‑value strategic work.

Limitations, Risks and Ethical

Considerations

While incorporating AI into analytics can significantly

improve data intelligence, it also introduces risks.

These risks shouldn’t prevent adoption, but they do

highlight the need for a strong data foundation and

responsible practices. The following are some key

areas to consider.

Bias, interpretability and Data Quality

AI outputs depend heavily on the data they learn from.

If the data is incomplete or biased, the results may be

as well. Interpretability is another challenge. Some

models act like black boxes, making it hard to

understand how they reach conclusions. When

internal reasoning isn’t visible, it is even more

important to maintain trust in AI outputs by ensuring

the data is clean, accurate and well‑documented.

Human Oversight and Governance

AI can generate insights quickly and confidently,

which may lead users to over‑rely on automated

results without validating them. AI is powerful, but

it’s not infallible. Analysts remain essential for

reviewing outputs, validating assumptions and

ensuring insights align with real‑world context.

That’s partly why governance is also important.

Organizations must manage version control,

maintain reproducible workflows and support

audit trails to track how models were built and

how results were generated. Without these

controls, troubleshooting becomes difficult and

compliance risks increase.

Privacy and other Challenges to

Consider

AI systems often work with sensitive data, which may

raise privacy and ethical concerns. Organizations

must ensure that data is collected and used

responsibly, with proper safeguards and access

controls in place.

One question to consider is the ethical impact of

using AI for analytics. Companies must handle

data responsibly and help customers understand

how their information will be used. Transparency

is also essential. Organizations should be able to

explain how AI models work, what data they rely

on and how they inform decisions. Ethical use

also requires human oversight to ensure AI

supports decision‑making rather than replacing

judgment or accountability.

Another common question is whether it’s okay to

rely solely only on AI for data analysis. It is not. AI

can speed up analysis and generate insights, but it

cannot replace human expertise, domain knowledge

or ethical judgment. The strongest analytics

workflows combine AI‑driven automation with

thoughtful human oversight to ensure accuracy

and accountability.

How AI is Changing the Role of

Data Analysts

AI is already reshaping the day‑to‑day work of data

analysts by shifting the balance of responsibilities

away from manual tasks and toward more complex,

judgment-oriented activities. Analysts can now rely

on AI to automate things like cleaning data, building

routine reports or writing repetitive queries, as well

as preparing datasets, generating summaries,

creating visualizations and identifying patterns

much faster than they could do manually.

However, there are things analysts can do that AI

can't, or not as well, such as evaluate tradeoffs or

decide which insights matter most to their team.

Analysts provide the judgment, domain knowledge

and critical thinking needed to interpret results and

guide decisions. They also validate AI‑generated

outputs to ensure the logic is sound and the

conclusions are verifiable.

One other change is that many analysts now spend

more time on crafting effective prompts for AI

responses, or choosing the right combination of

models, queries and workflows to answer a business

question. Oversight is another growing responsibility.

Analysts may find they spend more time monitoring

data quality, checking for bias and ensuring that

automated insights are accurate and trustworthy.

These changes connect directly to a common

question: Will AI replace data analysts? The answer

is that while AI can automate tasks, it can't replace

the strategic thinking, contextual understanding and

ethical judgment that analysts bring. AI elevates the

analyst role, allowing analysts to focus on discovering

deeper insights and more impactful decision support.

Skills and Getting Started with AI

in Analytics

While AI is creating new opportunities and changing

responsibilities for analysts, people in those roles

should still strive to stay competitive by developing

relevant skills. =Emerging skills like prompt design

will help you get better results from AI-powered BI

tools.

Many teams begin with low‑barrier experiments that

use sample projects, accessible tools and sandbox

datasets. Many platforms offer guided notebooks or

built‑in examples that walk users through common

workflows. These small use cases help analysts

build confidence while they learn how AI fits into

their existing processes.

At the team level, a simple workflow is a great way

to learn. Analysts can build a basic predictive model

that forecasts a single metric, such as weekly demand

or customer churn. Or they might try running

sentiment analysis on customer reviews to see

how AI classifies positive and negative feedback.

By developing these skills and experimenting with

entry-level tools, analysts can begin using AI in

meaningful ways and prepare for more advanced

applications.

The Future of Data Analytics with AI

The future of data analytics is almost certain to be

influenced significantly by the trajectory of advances

in generative AI and automation. As generative AI is

expanding what teams can automate, it is also making

data more accessible. As predictive modeling matures

we should expect it to become more accurate and

more adaptive as the models learn from new data.

Autonomous data exploration is also likely to increase,

thanks to systems that can scan datasets, detect

patterns and surface insights without being prompted.

Another major shift to keep an eye on is the rise of

AI agents that support or augment analysts. Acting as

intelligent partners, these agents will be able to help

run queries, monitor data quality, recommend models

and flag anomalies, thus extending an analyst’s reach

and accelerating their decision‑making.

Conclusion

AI is reshaping data analytics in meaningful ways

by speeding up routine tasks, improving accuracy

and making insights easier for more people to

access. From data preparation to visualization,

AI is opening the door to new levels of automation

and exploration.

If you'd like your company to start using AI with

data analytics, the best way to begin is choosing

one workflow area to pilot an AI‑driven

improvement. This could be automating a recurring

report, summarizing a dataset with NLP tools or

testing a simple predictive model. Small, focused

experiments help teams learn what works and

build confidence before they take on more

complex initiatives.

Whether you're just starting out or well on your

way, the message is simple: AI expands what is

possible with data analytics, but human judgment

remains essential. When AI and human expertise

work together, organizations can use AI to unlock

faster insights and make better decisions based

on their data.

To learn more about how AI-powered data

intelligence powers business intelligence,

compound AI systems or the new Databricks

offering AI/BI Genie that helps you converse with

your data through natural language, get your copy

of our eBook.

More resources:

1-Pre-Shool Education.

2-Primary Education..

3-Middle School Education

4--High schools Education.

5-USA Education System.

6-UK Education System

7-Egyptian Education System.

8-Classroom Language Journal.

9-storytelling Classroom.

10- Twenty Testing mistakes to avoid.

Teaching Forum 2014, Volume

52, Number 3

1-Assessment Literacy

2-Using Locally Relevant Authentic

1- Critical thinking ( 1 ).

2- Critical thinking ( 2 ).

3-Teaching Referencing

4-Blogs and Networks .

5-Communication practice