## Introduction****
Exploratory Data analysis is one of the most crucial process in ascertaining that the data analysis process is seamless to prevent data bias and false conclusions due to inconsistencies. It aids in exploring data structure, point out anomalies and check for assumptions in order to improve data quality, especially because there are many. These essentials include the following:
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Data collection
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This is the first step that every data analyst, data engineer and data scientist should be conversant with. You have to be cognizant of where your data will come from, what type of data you will need, how you will gather the data, etc. Some of the data collection tools often employed by data analysts in this process includes manual data entry from interviews, questionaries and surveys, observation, focus group data and consumer data, either manually or by use of survey platforms such as survey monkey, google forms and questionnaire Pro, which can then be stored in databases and later on conducted by using software such as SQL AND NoSQL.
Data can also be collected by using data integration tools such as Apache Nifi and Talend, which can support scalable data routing and transformation. Web scrapping tools such as BeautifulSoup and scrappy are used to collect data from websites. These are python libraries that are able to pull data out of websites that are static or dynamic without a lot of struggle.
API tools such as postman and rapid API offer immense support in testing APIs that have been tasked to collect data from web services and provide access to many other APIs worldwide. Other tools for the collection of data include data collection apps such as Open Data Kit (ODK) and Kobo Toolbox, both of which are used by organizations to collect, manage, and use data in challenging environments.
To ensure that you have collected good data, ensure that you are aware of data sources and databases that provide data that is accurate, without bias and well organized. Data can be collected in different forms, such as Csv files, excel files, Pdf files or from websites that may be static or dynamic, among many other forms.
Data collection is mostly subjective to the specific field of interest and organization, which means that there are many ways to collect data depending on the tools used in different professions. Since different fields of specificity have different ways in which data should be collected, ethical considerations should be highly considered. For example, data from the medical field can be collected through testing of people’s health status, which comes with its own set of ethical challenges, whereas data about climate or geography can be collected by using QGIS, etc. It is therefore crucial to understand that good data collection follows a defined set of objectives and aims in order to ensure that a dataset is accurate and caters to your specific need.
Data Cleaning and rangling
After the dataset is identified and collected, it is crucial to undertake rigorous data structuring. Data should be formatted to the required format to make it easier to work with. For example, data can be in form of a pdf file, which can be transformed into a csv file where needed in order to facilitate organization of said data into tables, charts, etc.
Next, data cleaning is conducted to ensure that the quality of your data is impeccable and can be used to come up with concise conclusions and recommendations to the end user. Before any data is utilized, a data analyst should check for any cases of inconsistencies, missing/null entries duplicate entries if present. Missing values or double entries oftentimes lead to false outcomes, which may impact crucial decision-making. Therefore, anyone analyzing data should ensure that their dataset is accurate.
After cleaning, an analyst should enrich the dataset by including additional information lacking in the dataset, merging with other datasets to get a bigger scope, or even conducting feature engineering in order to provide other variables that can improve the analysis process. After all these, an analyst should check for accuracy and consistency once more to ascertain the quality of data before moving forward.
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Descriptive statistics
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After preparation of the dataset, data characteristics have to be outlined. This gives a brief summary of the overall dataset by indicating the mean, median, mode, and standard deviation, as well as the percentiles of data, distribution analysis to check for the central measures of tendency, distribution shapes, i.e., skewness and kurtosis, and visualization techniques to check for the distribution of dataset and probability density.
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Data visualization
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This stage of EDA encompasses several steps, which include the identification of data variables. Data variables can be classified into the following.
- Univariate analysis: visualizing individual variables
- Bivariate analysis: visualizing the relationships between two variables by using tools such as scatter plots, correlation matrices, etc.
- Multivariate analysis: visualizing data by analyzing relationships between more than two sets of data or multiple variables.
- Outliers’ identification: the identification of data that is unusual or varies significantly from the overall dataset within specific variables.
- Hypotheses testing and formulation: The testing of evidence to reject or accept a null or alternative hypothesis.
- Testing assumptions: checking whether the data conforms to previously mentioned assumptions.
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Communication of findings
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This is the final step in the EDA process. A summary of the evaluation process is conducted, and findings are mentioned. The context of the data is articulated, and the scope and objectives of the analysis are identified. In this final stage, patterns, anomalies and perceptions should be discussed and suggestions are made for future areas to improve on.
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Conclusion
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To conclude, exploratory data analysis is a formidable process that enables an in-depth understanding of data and datasets by use of scientific statistical analysis techniques in order to enable fact driven decision making. As such, this process should be approached with meticulous precision in order to avoid inaccuracies.
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