Introduction to data analysis

BY :CHAIMAE ILLI

INTRODUCTION

DATA

ANALYSIS

a Data Science and

Business Intelligence

student at the Cadi Ayyad

university and the Event

Manager of the SDAD club

This course is done by:

ILLI

Chaimae

https://www.linkedin.com/in/chaimae-illi-265215209

chaimae.rema@gmail.com

Overview:

Introduction

What is data analysis?

Uses of Data analysis

Data analysis tools

Data analysis process

Data analysis types

Why is data analysis important?

Today we collect data at various

points of processes and

transactions, which has huge

potential to change the way we

work for the better. However, this

data analysis can add value to the

business only when it’s analyzed to

gain insights into how to improve

your products and services.

Data analysis allows you to know

and interpret information to identify

points of value. But what is data

analysis in simple words?

What is data analysis?

Data analysis is the science of examining a

set of data to draw conclusions about the

information to be able to make decisions or

simply to expand the knowledge on various

subjects.

Data Analysis is one of the most important

processes that businesses can leverage to make

the right decisions.

Effective data analysis is a skill that can be applied

to finance, retail business, medicine, and

healthcare, and even in the world of sports

Why is data analysis important ?

Uses of data analysis:

It is used in many industries regardless of the

branch. It gives us the basis to make decisions or

confirm if a hypothesis is true:

Mainly, researchers perform

data analysis to predict

consumer behavior and help

companies place their

products and services in the

market accordingly.

Universities and academic

institutions can perform

data analysis to measure

student performance and

gain insights into ways

education can be further

improved.

Marketing : Academics:

Organizations can use data

analysis to offer a great

experience to their

employees and ensure an

excellent work environment.

They can also utilize the

data at hand to find out the

best resources – the ones

whose skill set matches with

the organizational goals.

Human Ressources:

Data analysis tools:

In order to perform high-quality data analysis,

it is fundamental to use tools and softwares that will ensure the best results. As the analysis industry grows, so

does the offer for services and features that you can exploit

Data analysis process:

As the data companies have available to them continues to

grow in both amount and complexity, so does the need for

an effective and efficient process by which to harness the

value of that data. The analysis method typically moves

through several iterative phases. Let’s take a closer look at

each.

Data Requirement Gathering

Ask yourself why you’re doing this

analysis, what type of data analysis you

want to use, and what data you are

planning on analyzing.

Data Collection :

Guided by the requirements you’ve

identified, it’s time to collect the data from

your sources. Sources include case

studies, surveys, interviews,

questionnaires, direct observation, and

focus groups. Make sure to organize the

collected data for analysis.

Data Cleaning :

Not all of the data you collect will be

useful, so it’s time to clean it up. This

process is where you remove white

spaces, duplicate records, and basic

errors. Data cleaning is mandatory before

sending the information on for analysis.

Data Analysis:

Here is where you use data analysis

software and other tools to help you

interpret and understand the data and

arrive at conclusions. Data analysis tools

include Excel, Python, R, Looker, Rapid

Miner, Chartio, Metabase, Redash, and

Microsoft Power BI.

Data Interpretation :

Now that you have your results, you need

to interpret them and come up with the

best courses of action, based on your

findings.

Data Visualization :

Data visualization is a fancy way of

saying, “graphically show your information

in a way that people can read and

understand it.” You can use charts, graphs,

maps, bullet points, or a host of other

methods. Visualization helps you derive

valuable insights by helping you compare

datasets and observe relationships.

Types of Data Analysis:

Descriptive Analysis : works with either

complete or selections of summarized

numerical data. It illustrates means and

deviations in continuous data and

percentages and frequencies in categorical

data.

• Diagnostic Analysis: Diagnostic analysis

answers the question, “Why did this

happen?” Using insights gained from

descriptive analysis . analysts use

diagnostic analysis to identify patterns in

data. Ideally, the analysts find similar

patterns that existed in the past, and

consequently, use those solutions to

resolve the present challenges hopefully.

• Predictive Analysis: Predictive analysis

answers the question, “What is most

likely to happen?” By using patterns

found in older data as well as current

events, analysts predict future events.

While there’s no such thing as 100

percent accurate forecasting, the odds

improve if the analysts have plenty of

detailed information and the discipline to

research it thoroughly.

• Prescriptive Analysis: Mix all the

insights gained from the other data

analysis types, and you have prescriptive

analysis. Sometimes, an issue can’t be

solved solely with one analysis type, and

instead requires multiple insights.

Conclusion:

Data analysis has multiple facets and approaches,

encompassing diverse techniques under a variety of

names, and is used in different business, science, and

social science domains. In today's business world,

data analysis plays a role in making decisions more

scientific and helping businesses operate more

effectively

Thank you!

BY: ILLI CHAIMAE

Club SDAD

sdad.club22@gmail.com

club_sdad

Club SDAD