In this article, we will learn about data analysis in general terms. This article will continue to be prepared as a helpful resource for data visualization tutorials.
It is recommended that you read the basics of data visualization after this lesson. So you will have a better understanding of the course.
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What Is Data Analysis?
The process of finding useful information, cleaning, processing, and drawing conclusions is called data analysis. In below, you can find techniques and operation processes.
Data Analysis Technics
Data analysis in statistics is generally divided into descriptive statistics, descriptive data analysis (EDA), and confirmatory data analysis (CDA).
Data Analysis Model
Data analysis consists of 5 main stages, each project may have its own stages, but these stages are generally accepted.
- Define Need = First step, determining why you need data analysis.
- Data Collecting = Start collecting your data from the secure sources you designate.
- Clean Through Data = Clean unnecessary data that do not meet your specified criteria.
- Begin Analyzing = stage at which data is started to analyze
- Interpret Result = The final step is interpreting the results from the data analysis.
Step 1 – Define Need
Data analysis is a field that may be needed in many business areas. Before you start collecting and analyzing data, it is important to focus on the main need.
In the section below, we have prepared concrete examples that can be asked to you.
- How can increasing profit without sacrificing quality?
- What products do people usually buy?
- How can we increase sales?
Step 2 – Data Collecting
After a purpose has been defined, one of the most important stages that will affect the all stages after this stage is data collecting.
Data collection starts from the main source (also called internal source). This is usually structured data collected from CRM software, ERP systems, marketing automation tools, etc. These sources contain information about customers, finances, sales gaps, etc.
Then there are secondary sources, also called external sources. This is structured and unstructured data that can be collected from many places.
Step 3 – Data Cleaning
After collecting data from all necessary sources, we will clear the data. In the process of data analysis, data cleaning is very important, just because not all data is useful data.
In order to produce accurate results, data scientists must identify and eliminate duplicate data, abnormal data, and other inconsistencies that may bias the analysis.
Step 4 – Data Analysis
Our aim in this section is to analyze and manipulate all the obtain data. This can be done in a variety of ways.
One method is through data mining, which is defined as “knowledge discovery in a database”. Data mining techniques such as cluster analysis, anomaly detection, and association rule mining can reveal previously invisible hidden patterns in the data.
Machine learning algorithms, creating understandable reports with data visualization are included in the data analysis section
Step 5 – Interpret Result
At the end of the data analysis, data, in the form of reports, and graphics are interpreted. All previous work is integrated here and the main results are prepared.