when we make a decision; Whether it will affect us in the short or long term, a recurring daily decision or a fateful decision, it is based on thinking about what we went through before, and what results we will get based on the implementation of this decision in the future.
This process is an analysis of our past and an attempt to predict our future, and making decisions based on this analysis and prediction, and this is the essence of the process of analyzing data in a simple way.
Successful companies in the world depend on the analysis of the data of their customers and users continuously for several years, by sorting their consumption data by scientists and data analysts to arrive at patterns of their behavior, through which they can improve products and services and increase sales.
In this article, we discuss the field of data analysis with an overview of its most important basic elements.
The most important terms in the field of data analysis
The field of data analysis is one of the fields that diverge greatly, and contains within it more than one term. These terms generally express the components of this science and its components; Such as the term data analysis, and the term big data, which although closely related to each other, are different in their definition.
In order to clearly understand the field of data analysis, we must address the definition of each term individually, and know the role of individuals in each of them, so that we can form a complete picture of this field and its parts related to each other.
The following order of terms is defined to begin with the more general and comprehensive terms and then terms that are part of or dependent on them.
1. Big Data
The term big data is used to refer to ordinary data whose size is huge, and contains a large amount of information, which also has its characteristics that its size increases at a rapid and high rate with the passage of time, in addition to that it is characterized by complexity and overlap in a way that makes dealing with it or even storing it through tools and programs Ordinary data management is very difficult.
The main objective of big data analysis is to obtain clear information about the current situation and specific expectations about the future, by studying the behavior of users whose data has been collected in different ways.
There are other goals of big data analysis; Such as access to real values that reflect the conditions of companies in global markets compared to their competitors, and that big data is relied upon to extract information that helps in studying the motives of crime, or preventing natural disasters, or at the most, preparing for them in an appropriate manner that reduces their risk to the public. Humanity.
The reason the data volume is increasing at a rapid rate is due to the proliferation of devices that users interact with, which depend on the Internet even if only slightly.
At first, it was limited to desktop and laptop computers, and then expanded to include a large number of mobile devices, especially with the release of the Android system.
Which was the basic nucleus for the emergence of tablets, then smart TV, down to devices that depend on the Internet of things such as smart refrigerators and smart cars, or any electronic device that connects to the Internet and has a simple user interface that contains private data that the user interacts with.
As for companies or commercial and economic dealings with big data… All the devices we mentioned previously send their data in one way or another (sometimes with the permission of the user and sometimes without permission at other times) to their manufacturers.
Which in turn analyzes this data in order to obtain information about the user's interaction with the product, his satisfaction with it, dissatisfaction with one of its characteristics, more details about his willingness to buy this product again or improved versions of it, and any information that can be used to predict the user's future behavior towards this the product or its brand.
Examples that fall under the definition of big data:
- Stock exchange trading data.
- NASA data.
- Social media data.
- Behavioral data of users of online store websites.
- Data of users of smart devices such as smart watches.
- The above examples generate data daily, ranging in size from 1 terabyte to 500 terabytes.
2. Data Science
Data science is a science that uses several fields such as programming, mathematics, statistics, and machine learning to access useful information from Big Data.
The science of data analysis is usually used to make decisions and reach expectations by analyzing this data from different trends and factors, some of these factors may be unclear at the present time, which is necessary because this science is relied upon in making future decisions and plans based on the long term.
3. The Data Scientist and his role
By applying machine-learning algorithms, the data scientist analyzes the available data, including images, words, and videos, to create an artificial intelligence capable of performing analytical tasks on data that usually needs a human element to analyze it.
Ultimately, this artificial intelligence will reach predictions and predictions that can be used to provide clear reports, which help companies access reliable information in making decisions regarding the future of these companies’ activity and increase their success rate.
What is Data Analysis
We can define data analysis or data analysis as the process of arranging and refining data to discover useful information for making decisions in specific areas such as money, business, health, etc., and the primary purpose of data analysis is to extract useful information from it and make effective decisions based on it.
That is, the data analyst uses the science of data analysis in dealing with big data to extract useful information from it, at the request of large companies and institutions that have this amount of data and want to analyze it.
Transforming data into a visual image
Although it is not mentioned enough when talking about the field of data analysis, the section of data visualization (or converting data into simple visual forms) is one of the important sections in the field of data analysis.
Its importance lies in the fact that the process of data analysis must have an output that non-specialists can understand; This output may be a graph, a chart, or any other visual form that can be understood by decision makers such as CEOs and stockholders.
The importance of this field is not limited to the fact that it refines and arranges the information contained in big data and presents it in images and graphs to decision makers, but it is considered the final product for everyone, meaning that both the data analyst and the data scientist are working to make their end product is the set of images Or charts and graphs, which show the meaning of the data they have analyzed in a neat and tidy manner.
Data Analysis Role
like a data scientist; The data analyst performs the same analytical role with a fundamental difference, which is that the analyst does not provide predictions for what might happen in the future, he only analyzes the available data to analyze the current situation of giant companies and institutions.
The most important entities that rely on data analysis
- communications companies.
- pharmaceutical companies.
- top manufacturers.
- science laboratories.
- Social media platforms.
- e-commerce sites.
Demand for a Data Analysis job
Data analyst job is one of the most demanded jobs in the world at the moment, this demand has increased at a high rate in recent years in particular, due to the huge rise in the presence of people on the Internet for shopping, work or entertainment, which has led to an increase in the volume of data on the Internet.
Platforms such as Facebook, Amazon and Netflix have increased the number of their users in the past year to reach unprecedented rates in terms of new subscribers or presence on the platforms themselves.
The major crisis in this field is that this high demand for the data analyst job does not match the available number of specialists in this field globally (this is, of course, an advantage for anyone who wants to learn this field).
Although wages in this field may reach 100,000 dollars annually, the number of analysts worldwide is still not equal to the required number. Some believe that the reason for this deficit is; Is that specializing in this field requires extensive study in areas such as programming, analysis, statistics and others.
How to get started in the field of data analysis
The field of data analysis is a very interesting field, and whether you plan to work as a data analyst or data scientist, there are several information that you must know before entering this field, in this paragraph we will introduce you to the steps that experts advise to follow to get a job in this field:
1. Obtain an academic degree in one of these fields (IT, computer science, or any certificate related to computers in general).
Read also: What are the fields of Computer Science
(Having a scientific degree in the field of mathematics or statistics alongside the previous fields will facilitate the process of your entry in this field, and we will explain this point in detail in the next paragraph).
2. Gain experience specific to the field in which you will specialize in data analysis, such as business or health.
3. Join a job related to data analysis, even if it is a simple initial job, to gain experience in the field in general.
4. Start developing your abilities in this field, whether academically such as enrolling in an accredited study program or research and study individually.
We know that some of these steps may be a bit difficult, but they ensure that you excel in the field of data analysis and take it as a profession, and as usual any profession you need to study before starting it, which makes sense in the field of work.
Note that one of the reasons that a career as an analyst or data scientist is profitable is that the number of specialists in it is scarce, and this is not necessarily related to the field being difficult to learn, but rather because it is a field that requires a lot of serious study and patience.
Do not worry about how to obtain certificates in this field, because there are many online learning platforms such as Coursera and Udemy, which provide intensive courses for beginners to help them know the areas and skills needed to enter officially in this field.
Coursera, in particular, also offers full data analytics study paths that are equivalent to major certificates.
So if you are about to start changing your career path, the field of data analysis is worth thinking and studying a lot, and in the next paragraph we will explain in a simple way the most important areas and skills that a data analyst needs in his daily work.
Work requirements in the field of Data Analysis
As we mentioned earlier, the data analyst needs to be familiar with some areas in order to be able to analyze the data as required of him.
Here are the most important areas to be familiar with:
Programming is an important part of the field of data analysis, and if you want to start in this field, you need to be familiar with at least one programming language, so experts in the field advise learning the Python and R programming languages as a start to be able to deal with data analysis libraries such as reshape2 and , scipy, because Python is a relatively easy programming language to write, unlike other programming languages such as Java.
Although programming is a basic requirement to enter this field, knowing it without having a basic knowledge of statistics is a waste of time, because statistics is one of the first steps in the process of data analysis.
Be sure to study both descriptive and inferential statistics, as the former refers to quantitative measures that describe the characteristics of the sample, while the latter is intended to be predictive measures that deduce the characteristics of the larger population through the interpretation of the sample.
In general, you will need to know the basics in statistics, but do not worry, statistics is a fun science as some of the concepts may be familiar to you, and you may remember them easily since you often studied them in high school.
The end product of the data analysis process is numbers, so a knowledge of mathematics is essential to being a data analyst.
Initially, you need to be familiar with algebra, and how to formulate problems on the ground into mathematical equations that can be understood and solved.
4. Machine learning
Machine learning, or machine learning, uses algebra and statistics to make accurately calculated predictions based on the data being processed
As a data analyst you only need to know a few examples in the field of machine learning algorithms like Principal Component Analysis, Neural Networks.
It is important to know that the data analyst does not need to know the theory of these algorithms or even the details of their work, but he should know the pros and cons of these examples, as well as when he should or should not use them in data analysis.
5. Data processing
The term data processing or data wrangling means collecting data in its unprocessed form, arranging and organizing it into data that can be read and understood, and this field requires familiarity with the basics of dealing with database programs MySQL, oracle.
You will also need to learn how to format the data into csv and xml files.
6. Problem solving
Big data contains a huge number of information, and despite the technical progress, this field is still considered one of the complex fields, because analyzing this data may take a long time and effort, which are two factors that are not sufficiently available.
For example, when you go deeper as an analyst in this field, you will encounter many problems, (don't forget that you analyze the data of human users whose behavior changes according to many factors, and sometimes these factors may not be clear).
So your task always revolves around solving any problems that hinder your understanding of this behavior, and analyzing the data in a way that achieves the maximum benefit for the party you work for in the least possible time and with the least effort, to the extent that you are able to do the analysis process itself, giving you enough time to use this data in forecasting. If you are a data scientist, or know and measure current conditions and conditions on the ground if you are an analyst.
In this article, we have provided a simple introduction to the field of data science and the most important terms related to it, to help you learn about this field without going into deep technical details, especially since this field contains many and complex details, but it is interesting and desirable for those interested in it, whether from the general knowledge or from the point of view of specialization in the future.
Have you ever thought of reading about this field? Share your information and experience with us in the comments.