Guide to Data Analysis
- RICHA RAMBHIA
- Aug 3, 2022
- 3 min read
Data Analysis refers to as the process of collecting, preparing, cleaning, and processing raw data to extract relevant and insightful information from the dataset which helps businesses and organization make informed decisions. Data Analysis is important as it helps in various forms such as customer targeting, reduced costs, better problem-solving methods and knowing your target customers which helps any businesses in a way that they could be profitable from the decisions made.
Speaking of data analysis, let's dive into the analysis process and types of data analysis which will better help to understand how the analytical methods work on any dataset.
Data Analysis Process
Data Requirements: The data that is required for analysis as an input parameter needs to be specified based on the requirements of the business or a problem statement. This is the first step in an analytical process which is to understand the problem statement and the requirement of the data needed for analysis.
Data Collection: Now that we have the problem statement and data requirements for analysis, we proceed further with data collection. This would imply collecting data from various sources where the data can be either structured or unstructured type of data.
Data Processing: Once the data is collected from various sources, it needs to processed and organized for further analysis. Structured data is easier to use for analysis than unstructured data and hence the data obtained is organized in table format or structured type of data.
Data Cleaning: Data cleaning is an important process in the data analysis process. This takes up majority of the time in the entire analytical phase. The data obtained and organized may be incomplete, containing duplicate values and errors which require cleaning in order to avoid errors in the further analysis process.
Exploratory Data Analysis: Now that our dataset is cleaned, it can be analyzed using a variety of techniques which refer to as the EDA, i.e., Exploratory Data Analysis. Here, various methods and tasks are performed in order to get a better understanding of the data.
Data Visualization: Data visualization helps in graphically representing the information which can be easily understood by the target audience. It helps to understand and find new trends and patterns, and also to understand the relationship between the various independent and dependent variables.
Data Modeling: It helps the organizations and businesses use data effectively which would result in an efficient decision for the business. Various machine learning algorithms are implemented and built in order to better understand the data.
Communication & Findings: Finally, at the last stage is the communication and findings, where it is important to present the data in an effective manner to the target audience through visual representations and model results.
Data Analysis Types
Descriptive Analysis: To understand the raw and present data to answer the question of what exactly happened with the data.
Exploratory Data Analysis: To explore the relationships between the various parameters of the dataset.
Diagnostic Analysis: To understand why what happened within the dataset.
Predictive Analysis: To predict and forecast the trends and patterns and answer the question of what will happen with respect to the dataset.
Prescriptive Analysis: To understand how it will happen based on the trends and patterns predicted.
Summary
Data Analysis deals with analyzing data to find new patterns and trends within the dataset which can be done with the help of various data analytical methods and tools that follows an overall workflow and process in order to analyze the data in an effective way.
The data analytical types mentioned somehow belong to the data analysis process, for instance, with respect to the model building phase, we would consider the predictive analysis which would help in predicting new trends and patterns from the data. In the same way, the process and the types of data analysis go alongside.
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