Data analysis is a future system through which companies and institutions can square on top of investment, which means the study of data and information, analysis, arrangement and organization in the form of charts, to reach the proper conclusion in a direct and simplified way, and is in the use of Python language, which is always the best for easy handling, and can use other means such as Microsoft Excel, SQL, MATLAB...
What are the types of data analysis ?
There are different types of data analysis, and each type has a purpose and a domain that is used for the benefit of that domain, and to perform the process properly it is necessary to recognize each type and when to use it.Types of Data Analysis
1- Predictive Analysis
The goal is to predict future events, and depends on linking events to each other and predict certain events that can happen in the future from events that already exist in the present or in the past, and we find this type of analysis, for example in statistical institutions that predict the outcome of elections by studying and analyzing the behavior of Statistics in previous elections.2- Diagnostic Analysis
This type is effective in identifying patterns of data behavior and is based on the principle of " why did it happen? ". When you get a problem in any type of business we use diagnostic analysis, its role is limited to encountering similar patterns of the problem and uses similar solutions to solve it.3- Descriptive Analysis
Descriptive analysis analyzes complete data or a sample of summarized numerical data and shows us the average and deviation of continuous data as well as the percentage and frequency of categorical data.4- Statistical Analysis
As its name shows, it is an analysis based on the idea of performing a set of statistical operations such as data collection, analysis, interpretation, presentation and modeling of data, and statistical analysis often answers the question "what happened?" Using previous data.
Stages of Data Analysis
Select needs
Before starting to analyze data or go deeper into any analysis techniques, it is very important to identify the needs and determine the things that should be reached, as well as the needs and requirements of the company and its own perspective, and based on this can determine the general needs and the purpose of the company to analyze the data.Ask questions
Once you set the goals and basic needs, you must think about the questions that need to be answered because it helps in achieving the task, this is considered one of the most important stages of analysis, because putting wrong questions will lead to incorrect results.Data collection
The second step is the preparation and collection of data, where a data analyst collects data no relationship to the subject of the analysis, which will help in answer to the questions you ask, as to the collection of this data be in various forms such as Excel files or surveys, questionnaires, and store the collected data in a spreadsheet or SQL database, or pull data from the database such as Oracle and Microsoft.Data cleaning
After collecting data from different sources, the role of data cleaning came, clean data means data without spelling errors and without repetition and inappropriate or incomplete data, clean data depends largely on the integration of data, it is possible to collect repeated data or inconsistent data, and therefore unnecessary data must be removed and cleaned, this step is necessary in the science of data analysis because clean and coordinated data helps us to find better solutions and results are accurate.Data analysis
After cleaning, we will move on to the fun and important part of the analysis, at this stage the role of the data analyst is to search for relationships, identify trends, sort and filter the data, the type of data analysis performed by the analyst largely depends on the objectives and the quality of the questions asked and the answers required.Data visualisation
The main reason in the work of the perceptions of the data is that it is possible to deal with non-technical and you need to deliver information and results of them, so photographing the data transform complex data into drawings and forms simple and easy to understand, visualize the data, different methods and programs like Tableau, Power BI, Looker, and some of the components provided by the language (Python / Matplotlib,Seaborn) and some of the components provided by the language (R / ggplot2).
Some Data Analysis Tools
Programming language Python
is the fastest growing and widespread among other programming languages because of its flexibility and because it contains a number of important libraries that serve different scientific fields such as applications and games industry, artificial intelligence, business intelligence and data analysis...
It is very easy to exploit in the field of (data analysis) because it has libraries built specifically for the science of data analysis, for example, the most famous of them is a library.which helps us in the process of Data Processing and extraction of certain from Big data and of course the use of data frames, The most popular libraries Python for Data Analysis : Pandas - Numpy - Seaborn - Matplotlib
For example, you can use Python language to send TCP packets to devices, perform malware analysis, create intrusion detection systems, and other means that help with protection and security.
Microsoft Power BI
Microsoftr Power BI is a business analysis service provided by Microsoft that aims to create user-friendly reports and models, which is the gateway to business intelligence, and through the program Power BI reports and statistics resulting from data from different sources can be easily shared with more than one person to study the improvements needed to facilitate and develop the work of the organization or company.From the data sources that are connected to the Power BI :
- Files: Excel, PDF, SharePoint Folder.
- Databases: SQL Server Database, Oracle Database, and Microsoft Access.
- Other sources: Dynamic 365, Salesforce, Google Analytics, Adobe Analytics, Github, MailChimp.
MATLAB
Matlab is a program specialized in all mathematical equations (and has other uses), very strong in the analysis and representation of data, has its own programming language, Matlab, and recognized in many classifications, and its programming language is characterized by its dependence on matrices mainly, everything is recognized as a matrix from the singular number to photos or videos "Matlab language is relatively easy".The program contains three basic things : Programming - Graphical interfaces - Simulation.
It is characterized by the following topics :
- Artificial intelligence branches : Neural Networks - Machine training - Deep Learning.
- Image processing.
- Simulation of different systems : mechanical - electric - electronic - robots - Communication systems.
Some features offered by the program :
This program does calculus, algebraic and differential equations of high level, which can be very difficult, and does many of the difficult differential and mathematical problems that engineers, physicists and others know about as difficult as we forget to analyze the data.
Skills required to be a Data Analyst
Creative and analytical thinking:
Curiosity and creativity one of the main features of a good data analyst, it is better to have a strong foundation in statistical methods, but what is more important is to think about problems from a creative and analytical perspective.Strong and effective communication:
Strong communication is the key to success, a data analyst sends his findings clearly and uncomplicated, whether it's to a group of readers or to a team of executives who make business decisions.Programming languages (R / SAS):
One language must be mastered with a working knowledge of a few others. Analysts adopt programming languages such as R and SAS for data collection, data cleaning, statistical analysis and data visualization.SQL Databases:
SQL databases are relational (relational) databases of structured data, which store data in tables where information is pulled from different tables for analysis by analysts.Query languages in the database:
Most query languages use our data analysts are SQL, and there are many variations of this language, including PostgreSQL and T-SQL (Procedural Language / SQL).Microsoft Excel and Machine learning:
A data analytics professional improves handling excellence as well as understanding advanced modeling and analytics techniques, and also has machine learning skills, although machine learning is not a skill expected for typical data analyst jobs.Data storage:
Some data analysts work in the background, their important role is to link databases from multiple sources to create a data warehouse and use query languages to find and manage data.At the end if you like the topic do not forget to share it with your friends with the comment
