Data Analysis Steps
There is a need for becoming fluent in the data analysis process and staying up to date by continually learning and adding new knowledge in this field
The statistical approach to data analysis is much broader than just analysing data. Data analysis process starts with defining problem statement, continues with planning and collecting data, pre-processing data, exploring data using descriptive statistics and data visualizations, analysing/modelling data, and finalizes with interpreting and reporting findings.
Defining Problem Statement: This is the first step of data analysis. In this step, the problem statement is identified by the organizations/researchers. The data analyst should thoroughly understand the problem and the domain of the problem.
Planning and Collecting Data: In this step, the appropriate tools for data collection related to the problem statement will be identified. This step may include designing a survey for data collection, scraping data from web or accessing an Excel/a database file.
Data Pre-processing: The objective of this step is to make the data ready for the further statistical analysis. This step is one of the important phases in data analysis. The accuracy of the statistical analysis depends on the quality of the data gained in this step. Several operation such as importing data, reshaping data from long to wide format, filtering data, cleaning data, identifying outliers, and transforming variables can be applied to the data to make ready for the statistical analysis.
Exploring Data via Descriptive Statistics/Visualizations: The objective of this step is to understand the main characteristics of the data. Exploratory analyses are generally done using descriptive statistics (i.e. mean, median, standard deviation, frequencies, percentages etc.) Exploratory analysis will show you the things that you didn't expect or raise new questions about the data.
Analysing/Modelling Data: The statistical analysis/modelling step can include a broad range of techniques like statistical hypothesis testing, statistical modelling, and machine learning algorithms, Generally, the type of the variables in the data set and the purpose of the investigation will determine the appropriate analysis technique.
Interpretation and Reporting: The last step of the data analysis is the reporting and the interpretation of the results. This step is also critical as if you cannot understand and communicate your results to others, it doesn't matter how well you conducted your analysis.